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Quantifying forest cover loss in Democratic Republic of the Congo, 2000-2010, with Landsat ETM+ data
Abstract
Forest cover and forest cover loss for the last decade, 2000–2010, have been quantified for the Democratic Republic of the Congo (DRC) using Landsat time-series data set. This was made possible via an exhaustive mining of the Landsat Enhanced Thematic Mapper Plus (ETM +) archive. A total of 8881 images were processed to create multi-temporal image metrics resulting in 99.6% of the DRC land area covered by cloud-free Landsat observations. To facilitate image compositing, a top-of-atmosphere (TOA) reflectance calibration and image normalization using Moderate Resolution Imaging Spectroradiometer (MODIS) top of canopy (TOC) reflectance data sets were performed. Mapping and change detection was implemented using a classification tree algorithm. The national year 2000 forest cover was estimated to be 159,529.2 thou-sand hectares, with gross forest cover loss for the last decade totaling 2.3% of forest area. Forest cover loss area increased by 13.8% between the 2000–2005 and 2005–2010 intervals, with the greatest increase occur-ring within primary humid tropical forests. Forest loss intensity was distributed unevenly and associated with areas of high population density and mining activity. While forest cover loss is comparatively low in protected areas and priority conservation landscapes compared to forests outside of such areas, gross forest cover loss for all nature protection areas increased by 64% over the 2000 to 2005 and 2005 to 2010 intervals.
... These images were downloaded and analysed on the Google Earth Engine (GEE) platform. The GEE provides free satellite imagery and large-scale spatial analysis and calculation functions, which can be used to assess spatiotemporal changes in the landscape pattern [45], as shown by several studies [20,45,46]. Furthermore, Landsat satellite images have the advantage of being low-cost and offering the potential to survey large areas despite their lower spatial resolution [47]. ...
... The image with the minimum cloud (<10%) cover for each target year in the study area was selected as the data source to establish the sample dataset [49]. These Landsat images were reprojected into the WGS 84/UTM 35s system and mapped onto a pre-defined pixel grid using bilinear interpolation to facilitate subsequent image composition [20]. After radiometric correction, the images were cut and spliced, and the bands were synthesised [46]. ...
... Landscape analysis of the LCPB has shown remarkable dynamics, as evident by the regression of miombo woodland (natural and dominant cover), where its area was divided nearly in half between 1990 and 2022. The resulting general trends in LCPB anthropisation confirm the conclusions of numerous studies in the Katanga region, a historic mining area [20,22,23,32,35,68]. This would be justified by the use of the same approach despite the difference in analysis tools. ...
Population growth in the city of Lubumbashi in the southeastern Democratic Republic of the Congo (DR Congo) is leading to increased energy needs, endangering the balance of the miombo woodland in the rural area referred to as the Lubumbashi charcoal production basin (LCPB). In this study, we quantified the deforestation of the miombo woodland in the LCPB via remote sensing and landscape ecology analysis tools. Thus, the analysis of Landsat images from 1990, 1998, 2008, 2015 and 2022 was supported by the random forest classifier. The results showed that the LCPB lost more than half of its miombo woodland cover between 1990 (77.90%) and 2022 (39.92%) and was converted mainly to wooded savannah (21.68%), grassland (37.26%), agriculture (2.03%) and built-up and bare soil (0.19). Consecutively, grassland became the new dominant land cover in 2022 (40%). Therefore, the deforestation rate (−1.51%) is almost six-times higher than the national average (−0.26%). However, persistent miombo woodland is characterised by a reduction, over time, in its largest patch area and the complexity of its shape. Consequently, because of anthropogenic activities, the dynamics of the landscape pattern are mainly characterised by the attrition of the miombo woodland and the creation of wooded savannah, grassland, agriculture and built-up and bare soil. Thus, it is urgent to develop a forest management plan and find alternatives to energy sources and the sedentarisation of agriculture by supporting local producers to reverse these dynamics.
... In the DRC, deforestation and forest degradation are mainly the result of the expansion of slash-and-burn agriculture (the main form of agriculture in the country) (Ickowitz et al. 2015;Katembera et al. 2015) combined with fuelwood extraction, mining, logging and urbanization (Paluku 2005;Duveiller et al. 2008;Sikuzani et al. 2017;MECNT 2012;Megevand 2013;Deklerck et al. 2019). Although mainly practiced on a small scale, shifting cultivation can range from a few hectares to hundreds of hectares (Harris et al. 2017;Potapov et al. 2012;de Wasseige et al. 2014). Contributing to up to 92% of the deforestation in the DRC , slash-and-burn agriculture is supported by the high population growth rate (currently estimated at 3.1% in DRC (World Bank 2021) and expected to double by the end of the century), accelerated urbanization and high levels of poverty (Megevand 2013). ...
... Contributing to up to 92% of the deforestation in the DRC , slash-and-burn agriculture is supported by the high population growth rate (currently estimated at 3.1% in DRC (World Bank 2021) and expected to double by the end of the century), accelerated urbanization and high levels of poverty (Megevand 2013). The lack of alternative sources of income leads the population to depend mainly on natural resources for basic commodities and household energy needs (Lubalega et al. 2018;Potapov et al. 2012). ...
... Two data sources (Landsat 7/8 and MODIS MCD12Q1) were used to analyse the state of LULCs and their changes over time to increase the accuracy of the results obtained from two sources at different levels of resolution. The presence of significant cloud cover in the region (Potapov et al. 2012;Tyukavina et al. 2018) did not allow acquisition of Landsat images at annual steps. This led to the use of MODIS images to complete the data, albeit at medium resolution. ...
Human-induced fire is one of the most important determinants of forest cover and change in tropical and subtropical regions of the world. Yet its impact on forest cover and forest cover change remains unclear, as fires in Africa generally do not spread over very large area. This is particularly the case in the Democratic Republic of Congo (DRC), a region of the world that is still poorly investigated. Here, we propose to study the effect of human-induced fire on land use and land cover change in a protected area of the DRC, i.e. the Luki Biosphere Reserve (LBR). We investigate tree cover changes in and around the reserve between 2002 and 2019 using Landsat 7 ETM+, Landsat 8 OLI/TIRS and MODIS MCD12Q1 images and quantify human induced fires using MODIS MCD64A1 images. The study combines land use and land cover (LULC) change detection analysis of four images, two acquired in 2002 and two acquired in 2019, with multi-temporal assessment of annual burnt area acquired between 2002 and 2019 from MODIS MCD64A1 to assess the role of fire in LULC changes and the sensitivity of different LULC types to fire. The results show a dynamic conversion of primary forest to secondary forest over about 16% of the area, the evolution of savanna to secondary forest over 9.6% (Landsat image) and the replacement of secondary forest by savanna over 8.1% (MODIS image) of the total area of Luki Reserve. Of the total area undergoing land use change, 34.1% (Landsat image) and 35.7% (MODIS image) were caused by fire, which however did not cause a significant LULC change. For the LULC types that experienced fire events, the least stable type was primary forest, which had the lowest stability rate (34.2% and 23% for Landsat and MODIS image analysis, respectively) compared to others. This result illustrates the importance of fire as a driver of primary forest loss and degradation in the region. Despite the high exposure of savannas to fire events, they were not significantly destabilized by fire (stability rates of 86.3 and 97% for Landsat and MODIS analysis, respectively). Future analyses should focus on discriminating between different fire types to better understand the complex relationship between fire and ecosystem conditions.
... Several studies have been conducted on ethnobotany and the utilization of plant-based NTFPs by local communities in the DRC [11,[19][20][21], and particularly in the Katangese Copperbelt Area in the southeastern part of the country [22,23] where miombo woodlands predominate and account for almost 70% of the former Katanga Province [24] and 12% of the Lubumbashi plain [25]. Notably, in Haut-Katanga Province, over 80% of the population live predominantly in rural areas and rely heavily on forests for their survival [21]. ...
... However, human activity in the form of agriculture and charcoal production has caused significant regression of miombo woodlands in the Lubumbashi region [16]. As a result, an estimated deforestation rate of 1.41% of miombo woodlands was reported in Haut-Katanga Province between 2000 and 2010 [24]. ...
The overexploitation of forest resources in the charcoal production basin of the city of Lubum-bashi (DR Congo) is reducing the resilience of miombo woodlands and threatening the survival of the riparian as well as urban human populations that depend on it. We assessed the socio-economic value and availability of plant-based non-timber forest products NTFPs in the rural area of Lubumbashi through ethnobotanical (100 respondents) and socio-economic (90 respondents) interviews, supplemented with floristic inventories, in two village areas selected on the basis of the level of forest degradation. The results show that 60 woody species, including 46 in the degraded forest (Maksem) and 53 in the intact forest (Mwawa), belonging to 22 families are used as sources of NTFPs in both villages. Among these species, 25 are considered priority species. NTFPs are collected for various purposes, including handcrafting, hut building, and traditional medicine. Moreover, the ethnobotanical lists reveal a similarity of almost 75%, indicating that both local communities surveyed use the same species for collecting plant-based NTFPs, despite differences in the level of degradation of the miombo woodlands in the two corresponding study areas. However, the plant-based NTFPs that are collected from miombo woodlands and traded in the urban markets have significant economic value, which ranges from 0.5 to 14.58 USD per kg depending on the species and uses. NTFPs used for handicraft purposes have a higher economic value than those used for other purposes. However, the sustainability of this activity is threatened due to unsustainable harvesting practices that include stem slashing, root digging, and bark peeling of woody species. Consequently, there is a low availability of plant-based NTFPs, particularly in the village area where forest degradation is more advanced. It is imperative that policies for monitoring and regulation of harvesting, and promoting sustainable management of communities’ plant-based NTFPs priority, be undertaken to maintain their resilience.
... Landsat images. The Landsat images used to interpret reservoirs are median composited following the methods outlined by Potapov, et al. 43 . The median spectral reflectance of the four spectral bands-Red, Near-Infrared (NIR), Shortwave Infrared 1 (SWIR1), and Shortwave Infrared 2 (SWIR2)-from all Landsat images throughout each year are used to determine the spectral reflectance of the annually composited image. ...
... for computation purposes. The values of the notation g vary in the different bands of Red, NIR, SWIR1, and SWIR2, with values of 508, 254, 363, and 423, respectively, as established by Potapov, et al.43 .Geographical data. The geographic data used in this study include the Wetland Map of China in 2008, the National 1:250,000 Public Basic Geographic Database of 2019, and reservoir locations obtained from Baidu Maps for 2016 and 2021. ...
Reservoir inventories are essential for investigating the impact of climate change and anthropogenic activities on water scape changes. They provide fundamental data sources to explore the sustainability and management efficiency of water resources. However, publicly released reservoir inventories are currently limited to a single temporal domain. As a result, the effectiveness of governmental policy implementation on water resources remains to be explored due to the lack of multi-time datasets. In this study, we generated a reservoir inventory for China for the years 2016 and 2021 with an overall accuracy of 99.71%. The reservoirs were visually interpreted from annually composited Landsat images, and each reservoir is represented by a polygon with attributes of reservoir name, area and storage capacity. About 10.32% of the reservoirs have increased storage capacity from 2016 to 2021, while 22.73% have decreased. Most provinces and river basins in China have expanded their accumulated storage capacity from 2016 to 2021.
... Ces écosystèmes forestiers jouent des rôles cruciaux tant à l'échelle locale qu'internationale, tels que le maintien des sols contre l'érosion, la régulation du régime des pluies, le maintien de l'équilibre climatique, le maintien de l'habitat de la faune sauvage, et cetera. Néanmoins plusieurs études scientifiques signalent la régression perpétuelle du couvert forestier dans différentes zones de la RDC (Masimo et al., 2020 ;Kisangala et al., 2019 ;Muyaya et al., 2016 ;Molinario et al., 2015 ;Potapov et al., 2012). Le taux de déforestation annuelle en RDC est variable selon la zone concernée, la période d'étude, les méthodes utilisées, ainsi que les types de forêts. ...
... Le taux de déforestation annuelle en RDC est variable selon la zone concernée, la période d'étude, les méthodes utilisées, ainsi que les types de forêts. Il varie ainsi entre 0,18 et 0,46 % (Tungi Tungi et al., 2021 ;Defourny et Kibambe, 2012 ;De Wasseige et al., 2012 ;Potapov et al., 2012 ;Duveiller et al., 2008). Les forêts situées dans les aires protégées ne font pas exception à la forte pression que subissent les écosystèmes forestiers de la RDC. ...
The forest ecosystems of the Democratic Republic of Congo are prone to deforestation, highlighting fragmented and anthropized landscapes. The INERA Kiyaka forest station is not spared. This regression of plant cover is dependent on human activity. The cartographic and quantitative analysis by remote sensing of the dynamics of land use was carried out in order to highlight the essential factor of the deforestation of the vegetation cover between human pressure and climatic parameters. The classification by object with the supervised approach of Sentinel 2Landsat and Spot images made it possible to characterize land cover in three periods (2000, 2010 and 2020). We have selected six classes of land cover according to the realities on the ground: mature secondary forest, young secondary forest, savannah, agricultural and anthropogenic zone, water and palm grove. The analysis of the fragmentation of the landscape was done by comparing the results of the classification of satellite images of the years under study. The cartographic and quantitative analysis reveals a regressive dynamic of the forest formations (secondary forest) and the palm grove in favor of the savannah, agricultural and anthropic zone. The high percentages of regression are observed between 2010 and 2020. This study reveals that since 2000, the forest cover of the Kiyaka forest station has undergone a transformation of its landscape due to human activities supported by population growth.
... Remote sensing is the most time-efficient and economical method for obtaining large spatial-scale land-cover information [12,13]. With the improvement in remote-sensing image resolution, remote-sensing technology plays a vital role in forest-cover extraction and forest-change monitoring [14]. In Vietnam, researchers utilized Landsat images from 1973 to 2020 to identify changes in the spatial distribution of mangroves in Thanh Hoa and Nghe An provinces, and analyzed the reasons for these changes [15]. ...
... Forests 2023, 14, 1373 ...
As a distinct species in the Tianshan Mountains (TS) of Central Asia (CA), Picea schrenkiana plays a significant role in water purification, soil and water conservation, and climate regulation. In the context of climate change, rapidly and accurately obtaining its spatial distribution has critical decision-making significance for maintaining ecological security in the arid area of CA and the sustainable development of the “Silk Road Economic Belt”. However, conventional methods are extremely challenging to accomplish the high-resolution mapping of Picea schrenkiana in the TS, which is characterized by a wide range (9.97 × 105 km2) and complex terrain. The approach of geo-big data and cloud computing provides new opportunities to address this issue. Therefore, the purpose of this study is to propose an automatic extraction procedure for the spatial distribution of Picea schrenkiana based on Google Earth Engine and the Jeffries–Matusita (JM) distance, which considered three aspects: sample points, remote-sensing images, and classification features. The results showed that (1) after removing abnormal samples and selecting the summer image, the producer accuracy (PA) of Picea schrenkiana was improved by 2.95% and 0.24%–2.10%, respectively. (2) Both the separation obtained by the JM distance and the analysis results of eight schemes showed that spectral features and texture features played a key role in the mapping of Picea schrenkiana. (3) The JM distance can seize the classification features that are most conducive to the mapping of Picea schrenkiana, and effectively improve the classification accuracy. The PA and user accuracy of Picea schrenkiana were 96.74% and 96.96%, respectively. The overall accuracy was 91.93%, while the Kappa coefficient was 0.89. (4) The results show that Picea schrenkiana is concentrated in the middle TS and scattered in the remaining areas. In total, 85.7%, 66.4%, and 85.9% of Picea schrenkiana were distributed in the range of 1500–2700 m, 20–40°, and on shady slope and semi-shady slope, respectively. The automatic procedure adopted in this study provides a basis for the rapid and accurate mapping of the spatial distribution of coniferous forests in the complex terrain.
... Few studies have used MODIS products to assess Congo Basin deforestation. This is partially due to the spatial resolution of the products; the smallest-resolution MODIS product has a pixel size of 250 m, which is larger than the average clearing patch size of around 118 m in the Democratic Republic of the Congo (DRC) (Potapov et al. 2012). This issue is common for slash-and-burn clearing globally and can be alleviated by using both Landsat and MODIS data, as done in Central America (Hayes and Cohen 2007) and the Congo Basin (Hansen et al. 2008). ...
... The forest mask applied to the data does not distinguish between primary and secondary forests, so the surrounding forest may also have undergone multiple fallow cycles. Furthermore, most forest clearing in the Democratic Republic of Congo is from the secondary forest and young fallows (Molinario et al. 2015;Potapov et al. 2012), which further indicates that the deforested forested land was likely secondary forest. Analysis of the surrounding landcover type, F(s, t) [Eq. ...
The Congo Basin is severely understudied compared to other tropical regions; this is partly due to the lack of meteorological stations and the ubiquitous cloudiness hampering the use of remote-sensing products. Clustering of small-scale agricultural deforestation events within the Basin may result in deforestation on scales that are atmospherically important. This study uses 500 m MODIS data and the Global Forest Change dataset (GFC) to detect deforestation at a monthly and sub-km scale and to quantify how deforestation impacts vegetation proxies (VPs) within the Basin, the timescales over which these changes persist, and how they’re affected by the deforestation driver.
Missing MODIS data has meant that a new method, based on two-date image differencing, was developed to detect deforestation at a monthly scale. Evaluation against the yearly GFC data shows that the highest detection rate was 79% for clearing sizes larger than 500 m ² . Recovery to pre-deforestation levels occurred faster than expected; analysis of post-deforestation evolution of the VPs found 66% of locations recovered within a year. Separation by land-cover type also showed unexpected regrowth as over 50% of rural complex and plantation land recovered within a year. The fallow period in the study region was typically short; by the 6 th year after the initial deforestation event, ~88% of the locations underwent a further considerable drop. These results show the importance of fine spatial and temporal information to assess Congo Basin deforestation and highlight the large differences in the impacts of land-use change compared to other rainforests.
... The underlying concept of this approach is to reduce the disturbing influences, because clouds, snow, and aerosols typically depress NDVI and BT over land. Other best-pixel compositing approaches select the observation corresponding to the median [41,44] (or multi-dimensional versions of it such as the geometric median [45] or medoid [46]) of a distribution; however, this is usually applied to spectral rather than NDVI compositing. ...
... In the literature, median compositing is usually only used for combining high spatial resolution data such as Landsat or Sentinel-2 [41,[44][45][46]. Thereby, the aim is mostly the generation of gap-free multi-spectral mosaics that are representative of long time periods (seasons or years), while no composite time series at high frequency intervals (such as weeks or months) were generated using this approach. ...
Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change-related environmental processes. For monitoring vegetation conditions, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as no standard procedure has been established. Thirteen different compositing methods have been implemented; daily, decadal, and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best-performing compositing algorithm considering all the investigated aspects. However, the combination of the NDVI value and viewing and illumination angles as the criteria for the best-pixel selection proved to be a promising approach, too. The generated NDVI time series, currently ranging from 1981–2018, shows a consistent behavior and close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites.
... However, since 2013, the official deforestation rate has increased, especially in 2019 and 2020 [3]. In DRC, the gross forest cover loss from 2000 to 2010 was estimated to be 37,118 km 2 , or 2.3% of the total forest area in 2000 [4]. In Sumatra and Kalimantan, primary forest loss increased from 2001 to 2012 and gradually decreased afterward through to 2019 [5]. ...
... Other data were used to produce the forest map of each study site. Visual interpretation of images with a high spatial resolution is often utilized to construct reference data to validate the image processing results of satellite data [4,[6][7][8]. At Rio Branco, reference data were produced by visual interpretation of an area comprising 67.2°-67.5°W ...
A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the forest classification accuracy and helps to realize rapid deforestation detection because dual-polarization data are more frequently acquired than POLSAR data. The proposed method involves constructing scattering power models for dual-polarization data considering the radar scattering scenario of tropical forests (i.e., ground scattering, volume scattering, and helix scattering). Then, a covariance matrix is created for dual-polarization data and is decomposed to obtain three scattering powers. We evaluated the proposed method by using simulated dual-polarization data for the Amazon, Southeast Asia, and Africa. The proposed method showed an excellent forest classification performance with both user’s accuracy and producer’s accuracy at >98% for window sizes greater than 7 × 14 pixels, regardless of the transmission polarization. It also showed a comparable deforestation detection performance to that obtained by POLSAR data analysis. Moreover, the proposed method showed better classification performance than vegetation indices and was found to be robust regardless of the transmission polarization. When applied to actual dual-polarization data from the Amazon, it provided accurate forest map and deforestation detection. The proposed method will serve tropical forest monitoring very effectively not only for future dual-polarization data but also for accumulated data that have not been fully utilized.
... At present, several kinds of remotely sensed data are available in different resolutions on different platforms including Landsat. With a growing availability of remotely sensed data (Potapov et al. 2019;Potapov et al. 2012), the extent, dynamics, and spatial characteristics of swidden agriculture are quantified on different scales in Asiapacific countries (Castella et al. 2013;Hurni et al. 2013;Liao et al. 2015;Molinario et al. 2017;Messerli et al. 2009). ...
... The advancement of remote sensing techniques since 1970s, however, has made it possible to monitor environmental changes ever more closely, in that remotely sensed data provide invaluable information on fire events and burned area with its synoptic, multi-temporal, multi-spectral and repetitive coverage capabilities (Vadrevu and Justice 2011). With a growing availability of remotely sensed data (Potapov et al. 2019;Potapov et al. 2012), the extent, dynamics, and spatial characteristics of swidden agriculture are quantified at different scales in Southeast Asian countries (Heinimann et al. 2007;Castella et al. 2013;Hurni et al. 2013;Liao et al. 2015;Molinario et al. 2017). Singh and Dubey (Singh and Dubey 2012) suggested that remote sensing provides land resource data in both digital form and different bands of the electromagnetic spectrum. ...
Swidden agriculture is a common land use found in the mountainous regions, especially in Southeast Asia. In Myan-mar, the swidden agriculture has been practicing as an important livelihood strategy of millions of people, mainly by the ethnic groups. However, the extent of swidden agriculture in Myanmar is still in question. Therefore, we attempted to detect swidden patches and estimate the swidden extent in Myanmar using free available Landsat images on Google Earth Engine in combination with a decision tree-based plot detection method. We applied the commonly used indices such as dNBR, RdNBR, and dNDVI, statistically tested their threshold values to select the most appropriate combination of the indices and thresholds for the detection of swidden, and assessed the accuracy of each set of index and thresholds using ground truth data and visual interpretation of sample points outside the test site. The results showed that dNBR together with RdNBR, slope and elevation demonstrated higher accuracy (84.25%) compared to an all-index combination (dNBR, RdNBR, dNDVI, slope, and elevation). Using the best-fit pair, we estimated the extent of swidden at national level. The resulting map showed that the total extent of swidden in Myanmar was about 0.1 million ha in 2016, which is much smaller than other previously reported figures. Also, swidden patches were mostly observed in Shan State, followed by Chin State. In this way, this study primarily estimated the total extent of swidden area in Myanmar at national level and proved that the use of a decision tree-based detection method with appropriate vegetation indices and thresholds is highly applicable to the estimation of swidden extent on a regional basis. Also, as Myanmar is the largest country in mainland Southeast Asia in area with a great majority of the population living in rural areas, and many in the mountains, its land resources are of great relevance to the people's livelihoods and thereby the nation's progress. Therefore, this study will contribute to sustainable land management planning on both regional and national scale.
... The current forest cover (1999-2020) was derived from the classification of Landsat Analysis Ready Data supplied by the Global Land Analysis and Discovery laboratory (GLAD) [63] of the Department of Geographical Sciences at the University of Maryland. The GLAD Landsat ARD products represent a 16-day time-series of tiled Landsat normalized surface reflectance composites with minimal atmospheric contamination [40,64,65]. ...
... We identified a total of 506 multispectral images (January 1999 (image No. 438) to December 2020 (image No. 943)) suitable for our work and proceeded to fill pixels of low quality, including cloud contamination, using the GLAD Landsat ARD Tools v1.1 [40,66]. Subsequently, we generated 89 phenological metrics that together with two topographic variables (elevation and slope) derived from the SRTMGL1 v003 DEM [67] were used as input for land cover classification purposes [65,68,69]. ...
An increasing frequency of extreme atmospheric events is challenging our basic knowledge about the resilience mechanisms that mediate the response of small mountainous watersheds (SMW) to landslides, including production of water-derived ecosystem services (WES). We hypothesized that the demand for WES increases the connectivity between lowland and upland regions, and decreases the heterogeneity of SMW. Focusing on four watersheds in the Central Andes of Colombia and combining “site-specific knowledge”, historic land cover maps (1970s and 1980s), and open, analysis-ready remotely sensed data (GLAD Landsat ARD; 1990–2000), we addressed three questions. Over roughly 120 years, the site-specific data revealed an increasing demand for diverse WES, as well as variation among the watersheds in the supply of WES. At watershed-scales, variation in the water balances—a surrogate for water-derived ES flows—exhibited complex relationships with forest cover. Fractional forest cover (pi) and forest aggregation (AIi) varied between the historic and current data sets, but in general showed non-linear relationships with elevation and slope. In the current data set (1990–2000), differences in the number of significant, linear models explaining variation in pi with time, suggest that slope may play a more important role than elevation in land cover change. We found ample evidence for a combined effect of slope and elevation on the two land cover metrics, which would be consistent with strategies directed to mitigate site-specific landslide-associated risks. Overall, our work shows strong feedbacks between lowland and upland areas, raising questions about the sustainable production of WES.
... The acquisition of clear view remote sensing data in tropical areas is a long-standing challenge (Asner 2001;Huete and Saleska 2010;Li, Feng, and Xiao 2018;Potapov et al. 2012). In response to this challenge, multiple image compositing algorithms have been designed to generate clear view remote sensing images. ...
Accurate forest cover mapping is essential for monitoring the status of forest extent in Southeast Asia. However, tropical areas frequently experience cloud cover, resulting in invalid or missing data in thematic maps. The initial 2005 and 2010 forest cover maps produced by the collaboration of the Greater Mekong Subregion and Malaysia (GMS+) economies contain unclassified pixels in the areas affected by cloud or cloud shadow. To enhance the usability and effectiveness of the 2005 and 2010 GMS+ forest cover maps for further analysis and applications, we present a novel method for accurately mapping forest cover in the presence of cloud cover. We employed a pixel-based algorithm to create clear view composites and automatically generated land cover training labels from the existing forest cover maps. We then reclassified the invalid areas and produced updated maps. The land cover types for all previously missing pixels have been successfully reclassified. The accuracy of this method was assessed at both the pixel and region level, with an overall accuracy of 94.2% at the forest/non-forest level and 86.6% at the finer classification level by pixel level assessment across all reclassified patches, and 93.2% at the forest/non-forest level and 89.9% at the finer level by region level for the selected site. There are 2.6% of forest and 0.7% of non-forest areas in the 2005 map, as well as 2.7% of forest and 0.6% of non-forest in the 2010 map have been reclassified from invalid pixels. This approach provides a framework for filling invalid areas in the existing thematic map toward improving its spatial continuity. The updated outputs provide more accurate and reliable information than the initial maps on the status of forest extent in the GMS+, which is critical for effective forest management and sustainable use in the region. ARTICLE HISTORY
... 168 or 86.5%) supported population growth control. This assertion is consistent with existing literature such as Potatov,[22]; Olatoye,[3,4]; Olatoye,[1] ...
Gambari Forest Reserve (GFR) is located in Oyo State, in the south-western region of Nigeria, in the Mamu locality (Gambari Forest), co-ordinate 3.7 and 3.9E” and latitude 7°26 1 N and longitude 3°5 1 E. i.e. 17 km South-East of Ibadan, along the Ibadan/Ijebu-Ode road. The major taxa studies for this research include the forest tree species forest ecosystem in Gambari Forest Reserve, such as: Leucaena leucocephala, Leucaena glauca, Gliricidia sepium,Tectona grandis, Gmelina arborea, Swietenia macrophylla, Acacia spp., Albizia spp., Cassia siamea, and Pithecellobium saman. 200 key respondents participated in this study, which were drawn from the seven main communities namely Ibusogboro, Oloowa, Daley North and south, Onipe, Mamu, Olubi and Onipanu respectively. The results revealed that there are significant anthropogenic interventions taking place in the study area. It is therefore imperative to conserve and safeguard GFR ecosystem resources, as ensuring that ecosystem services and biodiversity function at optimum levels. This study therefore recommends continued research to be undertaken, in addition to consistent monitoring and conserving our fragile forest resources, with the aim of achieving optimum functioning and service delivery.
... A key innovation of our work lies in the combination of DL time series classification and prior knowledge constraints to detect the history of forest disturbance. Landsatbased forest cover and change mapping using supervised expert-driven classification is a well-established and accepted methodology [71]. Recent studies have shown that utilizing robust reference data and carefully constructed models results in disturbance maps with higher accuracy, such as stacked generalization, secondary classification, and ensemble methods [22,23,26]. ...
The scale and severity of forest disturbances across the globe are increasing due to climate change and human activities. Remote sensing analysis using time series data is a powerful approach for detecting large-scale forest disturbances and describing detailed forest dynamics. Various large-scale forest disturbance detection algorithms have been proposed, but most of them are only suitable for detecting high-magnitude forest disturbances (e.g., fire, harvest). Conversely, more continuous, subtle, and gradual lower-magnitude forest disturbances (e.g., thinning, pests, and diseases) have been subject to less focus. Deep learning (DL) can distinguish subtle differences in information within time series data, offering new opportunities to capture forest disturbances in a complete and detailed way. This study proposes an approach for analyzing forest dynamics across large areas and long time periods by combining DL time series classification and prior knowledge constraint. The approach consists of two stages: (1) an improved self-attention model used for time series classification to identify sequences with forest disturbance characteristics; (2) developed skip-disturbance recovery index (S-DRI) characterizing the temporal context, using prior knowledge constraint to identify forest disturbance years in time series with disturbance characteristics. In this study, the year of forest disturbances in five study areas located in the United States, Canada, and Poland from 2001 to 2020 was mapped. A total of 3082 manually interpreted test data with different disturbance causal agents (such as fire, harvest, conversion, hurricane, and pests) were sampled from five research areas for validation. Our approach was also evaluated against two forest disturbance benchmark datasets derived from LandTrendr and the Global Forest Change (GFC) dataset. The results demonstrate that our approach achieved an overall accuracy of 87.8%, surpassing the accuracy of LandTrendr (84.6%) and the Global Forest Change dataset (81.4%). Furthermore, our approach demonstrated lower omission rates (ranging from 10.0% to 67.4%) in detecting subtle to severe causal agents of forest disturbance, in comparison to LandTrendr (with a range of 18.0% to 81.6%) and GFC (with a range of 15.0% to 88.8%). This study, which involved mapping large-scale and long-term forest disturbance in multiple regions, revealed that our approach can be applied to new areas without a requirement for complex parameter adjustments. These results demonstrate the potential of our approach in generating comprehensive and detailed forest disturbance data, thus providing a new and effective method in this domain.
... In this study, image re ectance variability arising from external seasonal differences was minimized by selecting satellite images captured in the same season. We used the approach of Potapov et al., (2012) where images captured during the growing season of mangroves were used to create a cloud-free image mosaic of Macajalar Bay as they are more suitable conditions to map forest than that of the senescent condition. Growing seasons were de ned geographically using the temperature and rainfall data observation of the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) as a primary reference to delineate varying seasonal differences in the country. ...
Mangrove forest in Macajalar Bay is regarded as an important ecosystem as it provides numerous ecosystem services. Despite their importance, deforestation has been rampant and has reached critical rates. Addressing this problem and further advancing conservation requires accurate mapping of mangroves, and understand the historical land cover changes. However, such information is sparse and insufficient to understand the change dynamics. In this study, mangrove cover change dynamics for Macajalar Bay, Philippines was determined using Landsat data and machine learning techniques. Vegetation maps derived from aerial photographs and satellite images were used to quantify mangroves and to monitor the rates of deforestation over a 70-year period. In 2020, the mangrove forest cover was estimated to be 187.67 ha, equivalent to only 58.00% of the 325.43 ha that was estimated in 1950. Original mangrove forest that existed in 1950 only represents 8.56% of the 2020 extent, suggesting that much of the old-growth mangrove have been cleared before 2000 and that contemporary mangrove extent is mainly composed of secondary forest. Highest deforestation rates occurred between 1950–1990 where it recorded a total of 258.51 ha, averaging a clearing rate of 6.46 ha/year. Clearing has been driven by large-scale aquaculture pond developments. Mangrove gains were evident in 2000 but it plateaued as it approaches 2020, while loss simultaneously increased since 2010. This indicates that mangroves gained since 2000 have experienced low survival rates. Promoting site-species matching, biophysical assessment, and verification of fishpond availability for mangrove rehabilitation programs are necessary undertakings to address such problems.
... CA-Markov is a reliable method for predicting changes in LULC, and it has been advocated as the method of choice since it outperforms other methods (Potapov et al. 2012). In addition, it can forecast transitions between LULC classes in both directions (Pourghasemi et al. 2013). ...
We employed the Cellular Automata Markov (CA-Markov) integrated technique to study land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab, Pakistan. We plotted the distribution of the LULC throughout the desert terrain for the years 1990, 2006, and 2022. The Random Forest methodology was utilized to classify the data obtained from Landsat 5 (TM), Landsat 7 (ETM+), and Landsat 8 (OLI/TIRS), as well as ancillary data. The LULC maps generated using this method have an overall accuracy of more than 87%. CA-Markov was utilized to forecast changes in land usage in 2022, and changes were projected for 2038 by extending the patterns seen in 2022. A CA-Markov-Chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2038. Analysis of urban sprawl was carried out by using the Random Forest (RF). Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 8.12 to 12.26 km 2 and from 18.10 to 28.45 km 2 in 2022 and 2038, as inferred from the changes occurred from 1990 to 2022. The LULC projected for 2038 showed that there would be increased urbanization of the terrain, with probable development in the croplands westward and northward, as well as growth in residential centers. The findings can potentially assist management operations geared toward the conservation of wildlife and the ecosystem in the region. This study can also be a reference for other studies that try to project changes in arid are as undergoing land-use changes comparable to those in this study.
... Fires occur because of natural causes, but the bulk of the forest fires in Sub-Saharan Africa are the result of economic activity (Le Page et al., 2010;CIFOR, 2016). Fire is used to clear agricultural land, produce ashes to fertilize the soil, drive out wildlife for hunters, stimulate the growth of young shoots as feed for cattle, and to produce charcoal as fuel (Savadogo et al., 2007;Sawadogo, 2009;Potapov et al., 2012;Sow et al., 2013;Curtis et al., 2018). While forest conservation is recognized as a key strategy to mitigate climate change (as evidenced by the United Nation's REDD and REDD+ programs), relatively little is known about how effective forest conservation policies are in reducing forest fires, especially so in the dryland forests of Sub-Saharan Africa. ...
... 9 As such, most forest exploitation occurred in more accessible lowland areas for a variety of activities, including logging and agriculture. [10][11][12] However, since the turn of the 21 st century, mountain forests have been increasingly exploited for timber and wood products, as well as to support emerging agricultural systems, such as boom crops and tree-based plantations, for example in Southeast Asia. [13][14][15] These activities have reshaped montane forests, potentially reducing the size and number of refuge areas, increasing the risk of extinction of forest-dwelling species, 16 and weakening the ability of forests to store carbon 13 and regulate climate. ...
... In Africa, studies on forest clearance were undertaken in the Democratic Republic of Congo and Tanzania (Ahrends et al., 2021;Shapiro et al., 2021) with limited information regarding the assessment of forest clearance in the TMFs. Potapov et al., (2012) also quantified forest cover loss in the Democratic Republic of Congo and discovered that between the years 2000 -2010, total gross forest cover loss increased by 13.8% with a forest loss rate of 0.4% in protected areas. Meanwhile, similar studies conducted in Mt Elgon, where the role of protected areas and changing contexts (management and policies) were evaluated and the findings revealed that protected areas continued to lose forest cover . ...
Tropical montane forests are fragile ecosystems that provide a wide range of ecosystem services such as hydrological services, protection of biodiversity, and a contribution to climate change mitigation, yet they face degradation as well as losses due to deforestation. Deforestation poses a major threat yet whether these tropical montane forests recover from these changes is not well understood, especially for African montane forests. This study assessed rates of deforestation, and recovery using remote sensing of two important tropical montane forests of East Africa: the Mau Forest complex and the Mount Elgon forest. An in-depth study of aboveground biomass, species diversity and richness, and soil carbon and nitrogen stocks were conducted for the Mau forest complex. To conduct the detailed study, 47 forest plots were established to collect data subsequently used to calculate the rate of recovery of the aboveground biomass (AGB) and species recovery in 3 blocks of the Mau forest complex. From the same plots, soil samples were collected to assess the response of soil carbon (C) and nitrogen (N) stocks to 60 cm of soil depth from the different recovery stages.
This study found that 21.9% (88,493 ha) of the 404,660 ha of the Mau forest Complex was lost at an annual rate of -0.82% yr-1 over the period between 1986-2017. However, 18.6% (75,438 ha) of the forest cover that was cleared during the same period and is currently undergoing recovery. In the Mt Elgon forest, 12.5% (27,201 ha) of 217,268 ha of the forest cover was lost to deforestation at an annual rate of -1.03 % yr-1 for the period between 1984 - 2017 and 27.2% (59,047 ha) of the forest cover that was lost is undergoing recovery. The analysis further revealed that for the Mau forest complex, agriculture (both smallholder and commercial) was the main driver of forest cover loss accounting for 81.5% (70,612 ha) of the deforestation, of which 13.2% was due to large scale and 68.3% was related to the smallholder farming. For the Mt Elgon forest, agriculture was also the main driver of forest loss accounting for 63.2% (24,077 ha) of deforestation followed by the expansion of human settlements that contributed to 14.7% (5,597 ha) of forest loss. For the aboveground biomass (AGB), it was found that AGB recovered rapidly in the first 20 years at an annual rate of 6.42 Mg ha-1, but the rate of recovery slowed to 4.67 Mg ha-1 at 25 years and 4.46 Mg ha-1, at 30 years of age. At 25 years, the mean AGB (198.32 ± 78.11 Mg ha-1) was statistically indistinguishable from the mean AGB in the old growth secondary forest (282.86 ± 71.64 Mg ha-1). Stem density, species diversity, and richness (i.e., Evenness index, Shannon’s index, and Simpson’s index) did not show any significant changes with the recovery stages of the secondary forest, although there existed a significant variation between the young secondary forests of age below 15 years from the old growth secondary forests. The study further found that, unlike the AGB and aboveground carbon (AGC), the soil C and N stocks were not significantly different across the recovery periods with mean soil C in the youngest forest 184.1 ± 41.0 Mg C ha-1 and old growth secondary forest as 217.9 ± 51.8 Mg C ha-1, the N stocks in the youngest forest was 16.4 ± 4.8 Mg N ha-1 and 20.1 ± 3.9 Mg N ha-1 for the old growth secondary forest.
The findings of the study indicate that these tropical montane forests of East Africa are under threat resulting from forest clearance and deforestation. The forest AGB recovers after 25 years while the tree species richness and diversity, soil C and N stocks do not change significantly with the recovery stages. The effects of disturbances i.e., forest fire, charcoal burning, grazing (livestock), elephant damage, and fuelwood collection on the soil C and N stocks within the different recovery stages were not significantly different between old growth secondary forests and the other recovery stages. These findings contribute to the knowledge on the response of the tropical montane forest of East African to pressures of forest clearance and deforestation.
... Because legumes were only delivered by truck, this suggests households with increased relative access to improved cereal seeds decided to clear more fertile land for planting, resulting in a loss of primary forest, while the effect is lower where legumes were also made available. These effects are large: while deforestation of primary forest is much lower than that of secondary forest in the control villages (0.1 hectares compared to 0.4 hectares, in line with more aggregate numbers of ref. 39 ), the relative increase in deforestation of primary forest due to increased access to modern seed varieties represents a more than 100% increase in treatment villages without truck delivery, compared to the mean deforestation rate in the control group. The per-household effect is stronger in those villages where randomly, a larger proportion of households benefited from a subsidy (though not significantly so), possibly due to greater competition for land acquisition as well as collective deforestation (Table A.4 in Supplementary Information). ...
Since the 1960s, the increased availability of modern seed varieties in developing countries has had large positive effects on households’ well-being. However, the effect of related land use changes on deforestation and biodiversity is ambiguous. This study examines this question through a randomized control trial in a remote area in the Congo Basin rainforest with weak input and output markets. Using plot-level data on land conversion combined with remote sensing data, we find that promotion of modern seed varieties did not lead to an increase in overall deforestation by small farmers. However, farmers cleared more primary forest and less secondary forest. We attribute this to the increased demand for nitrogen required by the use of some modern seed varieties, and to the lack of alternative sources of soil nutrients, which induced farmers to shift towards cultivation of land cleared in primary forest. Unless combined with interventions to maintain soil fertility, policies to promote modern seed varieties may come at the cost of important losses in biodiversity.
... It was tested in European Russia between Circa 2000 and 2005 [15]. In the same way, a classification tree algorithm was implemented to map the forest cover loss for the decade 2000-2010 in the Democratic Republic of Congo [16]. The potential effects of sea-level rise on the Sundarbans were investigated with the help of remote and field measurements, simulation modeling, and geographic information systems [17]. ...
Forest cover" refers to the relative land area covered by forests. Anthropological interventions and the subsequent diminishing forest cover, result in environmental degradation, impacting man-nature interactions. Hence, it became the need of the moment to monitor the forest cover to minimize natural perils and promote sustainable development. The present preliminary work focuses on implementing image processing and k-means clustering techniques on satellite imagery to monitor and quantify the forest cover of the Sundarbans delta, existing across India and Bangladesh. Image-based algorithms relying on characteristic colouration were proposed for analysing the percentage of forest cover in the predefined area. Among various methods of monitoring and examining forest land, image-based algorithms can be of vital use due to the rise in the accessibility of information and the potential of analysing large data sets with the least processing time. The above-discussed techniques, along with the availability of Machine Learning (ML) and spaceborne photography, will have a futuristic impact on interpreting the variations in land cover and land utilization. Building upon the following algorithm, it is now conceivable to conduct timely comprehensive analysis, real-time evaluation, monitoring, and control on how events unfold. Similarly, data collected from various geographical observation systems may provide several other qualitative features that are more focused.
... Undoubtedly, cities are expected to account for about 70% of India's GDP by 2030, with a requirement of about 1.2 trillion dollars of capital investment needed to meet the projected demands in various sectors such as transport, housing, water, solid waste disposal and others need (Sankhe et al. 2010). Several problems related to urbanization include a decline in ecosystem services and fragmented landscape (Su et al. 2012), destruction of fertile arable land (Fahim et al. 1999), destruction of habitat (Alphan 2003) and degradation of forest and natural vegetation cover (Potapov et al. 2012). A recent study has shown that due to an increase in urbanization and socioeconomic development, ESVs have decreased from 1986 to 2017 in Guangdong, Hong Kong and Macao region located in South China with decrease in water supply and food production (Hasan, Shi, and Zhu 2020). ...
Assessing the effects of land use and land cover (LULC) on ecosystem service values (ESVs) is critical for public understanding and policymaking. This study evaluated the impacts of LULC dynamics on ESVs in Chandigarh city of India. The assessment of LULC changes was performed by analyzing the satellite imagery of the study area for the years 1990 and 2020 with different band combinations in ArcGIS (10.8 version software). In addition, we analyzed ecosystem services changes which were based on the LULC classes of the study area. Five LULC classes were identified in the present study area (Water bodies, forest and vegetation, built-up, agriculture and shrubland and open spaces). The results demonstrated (from 1990 to 2020) that the forest cover and agricultural areas decreased by 4.19% and 37.01%, respectively, whereas the built-up area substantially increased by 104.61%. Overall, ESV decreased by 2.54% from 1990 to 2020 due to rapid urbanization. The combination of LULC and ecosystem services valuation can increase our understanding of different issues of an urban ecosystem. Hence, we recommend the integration of LULC and ecosystem services valuation as a tool that could provide information to policy-makers, urban planners and land managers for sustainable use in future.
... Jednak ich rzetelna dokumentacja ogranicza się do ostatnich kilkudziesięciu lat (Griffiths i in. 2012;Potapov 2012i in. za: Kaim 2014. ...
... Many protected areas exhibited a loss of forest cover. In some cases, this decrease was comparatively low inside the protected area in contrast to the surrounding non-protected forest (Phua et al., 2008;Potapov et al., 2012). Da Ponte et al., (2017) reported a significant increase in deforestation outside of Paraguayan protected areas, with rates ranging between 13 and 35%, whereas inside these areas deforestation was only 3.3%. ...
Deforestation and fragmentation threaten biodiversity owing to their impacts on many species. To prevent and minimise the problem, protected areas have been created with the aim of conserving biodiversity, and parts of continental territories have been designated for this purpose. However, these areas are not exempt from forest loss and can be directly and indirectly disturbed by surrounding territories, natural disasters, climate , and human actions. In addition, the management quality of many protected areas is unknown. Thus, forest change detection using remote sensing data has been implemented as an approach to assess forest loss in conservation areas, since it generates spatio-temporal information about the protected forest area, which can then be used to improve forest management and decision making. This article reviews the approaches that have been implemented to study forest changes in protected areas.
... Forest disturbance is one of the most important processes in forest ecosystem succession and plays an integral role in maintaining regional ecological balance and stability [1]. However, due to the increasing frequency of human and natural disturbances, forest structure and function have changed significantly, particularly in tropical forests, which are important contributors to the global carbon cycle and biodiversity [2,3]. It is estimated that about 55% of the global forest carbon stock is stored in tropical forests, which host approximately two-thirds of global biodiversity hotspots [4,5]. ...
Monitoring disturbances in tropical forests is important for assessing disturbance-related greenhouse gas emissions and the ability of forests to sequester carbon, and for formulating strategies for sustainable forest management. Thanks to a long-term observation history, large spatial coverage, and support from powerful cloud platforms such as Google Earth Engine (GEE), remote sensing is increasingly used to detect forest disturbances. In this study, three types of forest disturbances (abrupt, gradual, and multiple) were identified since the late 1980s on Hainan Island, the largest tropical island in China, using an improved LandTrendr algorithm and a dense time series of Landsat and Sentinel-2 satellite images on the GEE cloud platform. Results show that: (1) the algorithm identified forest disturbances with high accuracy, with the R2 for abrupt and gradual disturbance detection reaching 0.92 and 0.83, respectively; (2) the total area in which forest disturbances occurred on Hainan Island over the past 30 years accounted for 10.84% (2.33 × 105 hm2 in total area, at 0.35% per year) of the total forest area in 2020 and peaked around 2005; (3) the areas of abrupt, gradual, and multiple disturbances were 1.21 × 105 hm2, 9.96 × 104 hm2, and 1.25 × 104 hm2, accounting for 51.93%, 42.75%, and 5.32% of the total disturbed area, respectively; and (4) most forest disturbance occurred in low-lying (<600 m elevation accounts for 97.42%) and gentle (<25° slope accounts for 94.42%) regions, and were mainly caused by the rapid expansion of rubber, eucalyptus, and tropical fruit plantations and natural disasters such as typhoons and droughts. The resulting algorithm and data products provide effective support for assessments of such things as tropical forest productivity and carbon storage on Hainan Island.
... The ever-increasing human population with accompanying economic drivers such as concentration of people in urban areas and excessive natural resources extraction is exerting stress on the planet leading to changes in the land cover such as vegetation cover loss (Potapov et al., 2012;Ying et al., 2017). Change in land cover is key component of global environment change and drives a wide range of ecological, hydrological, and climatic processes such as loss of forests, urbanization, and increase in agricultural activities (animal grazing and crops production) to increase food production (Foley et al., 2005;Vitousek et al., 1997). ...
This paper estimates bare area gain detected using cloud free Landsat 7 (ETM+) and Landsat 8 (OLI) in Botswana. From 2002 to 2020, agricultural fields shrunk by 76.4%, while built-up increased by 49.2%, and bare areas increased from 3.32% to 7.03% (or 111.7%). There is a significant seasonal change in bare area detected reaching maximum during the dry season when there is little or no ground cover. In this study, the seasonality of bare area gain was overcome by only considering a bare area pixel to contribute to bare area gain if it exists during both the winter and summer months. The probability of bare area detection was 75.0% and probability of false detection 13.3% respectively. The 13% false detection tended to be built-up areas which had similar spectral characteristics as bare areas since most built-up areas have no ground cover. The bare area gain is driven by the high population growth rate of 3.4%. From 2001 to 2017, the population of the study area has increased by 34% and now accounts for 47% of the population of Botswana.
... In particular, tropical forests that have or are experiencing armed conflict, several factors have been found to be correlated with Frontiers in Environmental Science frontiersin.org deforestation: rural and urban population density, agricultural activity (cattle, agro-industrial products included), infrastructure, mining (legal and illegal), and illegal cropping (crops or plants which have been deemed illegal to grow by the government, e.g., coca bush or opium poppy) Kanninen et al., 2009;Potapov et al., 2012;Yasmi et al., 2013;Butsic et al., 2015;Camisani 2018). ...
Deforestation is a documented driver of biodiversity loss and ecosystem services in the tropics. However, less is known on how interacting regional and local-level anthropogenic and ecological disturbances such as land use activities, human populations, and armed conflict affect carbon storage and emissions in Neotropical forests. Therefore, we explored how local-scale, socio-ecological drivers affect carbon dynamics across space and time in a region in Colombia characterized by deforestation, land use cover (LULC) changes, and armed conflict. Specifically, using available municipal level data from a period of armed conflict (2009–2012), spatiotemporal analyses, and multivariate models, we analyzed the effects of a suite of socio-ecological drivers (e.g., armed conflict, illicit crops, human population, agriculture, etc.) on deforestation and carbon storage-emission dynamics. We found that about 0.4% of the initial forest cover area was converted to other LULC types, particularly pastures and crops. Gross C storage emissions were 4.14 Mt C, while gross carbon sequestration was 1.43 Mt C; primarily due to forest regeneration. We found that livestock ranching, illegal crop cultivation, and rural population were significant drivers of deforestation and carbon storage changes, while the influential role of armed conflict was less clear. However, temporal dynamics affected the magnitude of LULC effects and deforestation on carbon storage and emissions. The approach and findings can be used to better inform medium to long-term local and regional planning and decision-making related to forest conservation and ecosystem service policies in Neotropical forests experiencing disturbances related to global change and socio-political events like armed conflict.
... Consequently, forests across the tropics have been cleared for agriculture [8][9][10], resulting in emissions of 2.6 GtCO 2 yr −1 [11]. While tropical regions such as the Amazon [12,13], Congo basin [14,15], and the Malay Archipelago [16][17][18] have received substantial attention, dry woody and grassland systems are increasingly being transformed and negatively impacted by land use [19][20][21][22]. Unfortunately, data on land change in extra-tropical systems of the Global South are lacking, even though these systems are particularly vulnerable to changing climate [2]. ...
The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.
... The DRC has the second-largest carbon stock after Brazil (Baccini et al., 2012), nevertheless, it was the largest country to exhibit a decline in tropical rainforest area across SSA between 1992 and 2018 (Tyukavina et al., 2018;Zhuravleva et al., 2013;Potapov et al., 2012). ...
Changes in global land cover (LC) have significant consequences for global environmental change, impacting the sustainability of biogeochemical cycles, ecosystem services, biodiversity, and food security. Different forms of LC change have taken place across the world in recent decades due to a combination of natural and anthropogenic drivers, however, the types of change and rates of change have traditionally been hard to quantify. This thesis exploits the properties of the recently released ESA-CCI-LC product – an internally consistent, high-resolution annual time-series of global LC extending from 1992 to 2018. Specifically, this thesis uses a combination of trajectories and transition maps to quantify LC changes over time at national, continental and global scales, in order to develop a deeper understanding of what, where and when significant changes in LC have taken place and relates these to natural and anthropogenic drivers. This thesis presents three analytical chapters that contribute to achieving the objectives and the overarching aim of the thesis. The first analytical chapter initially focuses on the Nile Delta region of Egypt, one of the most densely populated and rapidly urbanising regions globally, to quantify historic rates of urbanisation across the fertile agricultural land, before modelling a series of alternative futures in which these lands are largely protected from future urban expansion. The results show that 74,600 hectares of fertile agricultural land in the Nile Delta (Old Lands) was lost to urban expansion between 1992 and 2015. Furthermore, a scenario that encouraged urban expansion into the desert and adjacent to areas of existing high population density could be achieved, hence preserving large areas of fertile agricultural land within the Nile Delta. The second analytical chapter goes on to examine LC changes across sub-Saharan Africa (SSA), a complex and diverse environment, through the joint lenses of political regions and ecoregions, differentiating between natural and anthropogenic signals of change and relating to likely drivers. The results reveal key LC change processes at a range of spatial scales, and identify hotspots of LC change. The major five key LC change processes were: (i) “gain of dry forests” covered the largest extent and was distributed across the whole of SSA; (ii) “greening of deserts” found adjacent to desert areas (e.g., the Sahel belt); (iii) “loss of tree-dominated savanna” extending mainly across South-eastern Africa; (iv) “loss of shrub-dominated savanna” stretching across West Africa, and “loss of tropical rainforests” unexpectedly covering the smallest extent, mainly in the DRC, West Africa and Madagascar. The final analytical chapter considers LC change at the global scale, providing a comprehensive assessment of LC gains and losses, trajectories and transitions, including a complete assessment of associated uncertainties. This chapter highlights variability between continents and identifies locations of high LC dynamism, recognising global hotspots for sustainability challenges. At the national scale, the chapter identifies the top 10 countries with the largest percentages of forest loss and urban expansion globally. The results show that the majority of these countries have stabilised their forest losses, however, urban expansion was consistently on the rise in all countries. The thesis concludes with recommendations for future research as global LC products become more refined (spatially, temporally and thematically) allowing deeper insights into the causes and consequences of global LC change to be determined.
... Fortunately, such places are rare. In such cases, creating pixel-level composites using available historical Landsat data is an option (Potapov et al., 2012). (Qiu, 2021) investigated a range of different approaches to compositing and found that no single compositing scheme is optimal in all situations but that the data application in combination with the spectral and spatial conditions dictate the choice of compositing algorithm. ...
The Landsat program has the longest collection of moderate-resolution satellite imagery, and the data are free to everyone. With the improvements of standardized image products, the flexibility of cloud computing platforms, and the development of time series approaches, it is now possible to conduct global-scale analyses of time series using Landsat data over multiple decades. Efforts in this regard are limited by the density of usable observations. The availability of usable Landsat Tier 1 observations at the scale of individual pixels from the perspective of time series analysis for land change monitoring is remarkably variable both in space (globally) and time (1985–2020), depending most immediately on which sensors were in operation, the technical capabilities of the mission, and the acquisition strategies and objectives of the satellite operators (e.g., USGS, commercial company) and the international ground receiving stations. Additionally, analysis of data density at the pixel scale allows for the integration of quality control data on clouds, cloud shadows, and snow as well as other properties returned from the atmospheric correction process. Maps for different time periods show the effect of excluding observations based on the presence of clouds, cloud shadows, snow, sensor saturation, hazy observations (based on atmospheric opacity), and lack of aerosol optical depth information. Two major discoveries are: 1) that filtering saturated and hazy pixels is helpful to reduce noise in the time series, although the impact may vary across different continents; 2) the atmospheric opacity band needs to be used with caution because many images are removed when no value is given in this band, when many of those observations are usable. The results provide guidance on when and where time series analysis is feasible, which will benefit many users of Landsat data.
... Numerous studies illustrate these increased changes related to agriculture, built-up area expansion, charcoal production and mining activities in the sub-region at the expense of natural cover [17,63,65,68]. This is also consistent with the findings of [10] that both deforestation and forest degradation in the Congo Basin have significantly accelerated in recent years. The trends observed around the main anthropization poles in southern Katanga confirm the findings of [79] that charcoal production and agriculture have led to a loss of forest cover in southern Africa in general. ...
In Southeastern Katanga, mining activities are (in)directly responsible for deforestation, ecosystem degradation and unplanned building densification. However, little is known about these dynamics at the local level. First, we quantify the landscape anthropization around four agglomerations of Southeastern Katanga (Lubumbashi, Likasi, Fungurume and Kolwezi) in order to assess the applicability of the Nature-Agriculture-Urbanization model based on the fact that natural landscapes are replaced by anthropogenic landscapes, first dominated by agricultural production, and then built-up areas. Secondly, we predict evolutionary trends of landscape anthropization by 2090 through the first-order Markov chain. Mapping coupled with landscape ecology analysis tools revealed that the natural cover that dominated the landscape in 1979 lost more than 60% of its area in 41 years (1979-2020) around these agglomerations in favor of agricultural and energy production, the new landscape matrix in 2020, but also built-up areas. These disturbances, amplified between 2010 and 2020, are more significant around Lubumbashi and Kolwezi agglomerations. Built-up areas which spread progressively will become the dominant process by 2060 in Lubumbashi and by 2075 in Kolwezi. Our results confirm the applicability of the Nature-Agriculture-Urbanization model to the tropical context and underline the urgency to put in place a territorial development plan and alternatives regarding the use of charcoal as a main energy source in order to decrease the pressure on natural ecosystems, particularly in peri-urban areas.
... ref. 27 ), to obtain the percentage tree-cover data circa 2015. These data were derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Image (OLI) (for 2013 onward) scenes, with the reflectance of Landsat time-series images calibrated and normalized using MODIS reflectance datasets 38 . Note that the tree-cover gain data were not updated after 2012. ...
High-elevation trees cannot always reach the thermal treeline, the potential upper range limit set by growing-season temperature. But delineation of the realized upper range limit of trees and quantification of the drivers, which lead to trees being absent from the treeline, is lacking. Here, we used 30 m resolution satellite tree-cover data, validated by more than 0.7 million visual interpretations from Google Earth images, to map the realized range limit of trees along the Himalaya which harbours one of the world’s richest alpine endemic flora. The realized range limit of trees is ~800 m higher in the eastern Himalaya than in the western and central Himalaya. Trees had reached their thermal treeline positions in more than 80% of the cases over eastern Himalaya but are absent from the treeline position in western and central Himalaya, due to anthropogenic disturbance and/or premonsoon drought. By combining projections of the deviation of trees from the treeline position due to regional environmental stresses with warming-induced treeline shift, we predict that trees will migrate upslope by ~140 m by the end of the twenty-first century in the eastern Himalaya. This shift will cause the endemic flora to lose at least ~20% of its current habitats, highlighting the necessity to reassess the effectiveness of current conservation networks and policies over the Himalaya. A high-resolution map of the realized range limit of high-elevation trees across the Himalayas shows that trees are absent from the thermal treeline, determined by growing-season temperature, across the western and central Himalayas, as a result of human disturbance and/or premonsoon drought.
... To ensure high-quality surface reflectance data, a series of quality control procedures were applied including atmospheric correction and pixel removals contaminated by cloud or shadow (Foga et al., 2017;Masek et al., 2006;Vermote et al., 2016). Following previous studies (Berveglieri et al., 2021;Potapov et al., 2012;Younes et al., 2020), we used the median of all quality-controlled EVI values within a year to represent annual EVI, aiming to reduce the disturbances from atmospheric, tidal and seasonal variations. A summary of the acquisition dates and number of all available EVI images for each wetland can be found in Figure S3. ...
Chinese mangroves have been recovered in area over the past two decades from previous declining trend, and about half of existing mangroves are still in their young growth stage. This provides a unique opportunity to assess mangrove conservation by examining the growth dynamics of young mangroves over different conservation periods. However, we are currently short of effective assessment tools for spatially explicit quantification of mangrove conservation effects. To fill up this gap, we proposed a novel remote sensing approach using readily available unmanned aerial vehicle (UAV) and Landsat enhanced vegetation index (EVI) data to assess the spatial evolution of aboveground biomass (AGB) of young mangroves. With the space‐for‐time hypothesis, the approach implemented with an empirical EVI‐height‐AGB equation was tested in four subtropical estuarine mangroves in the southeastern coast of China. The results indicated: (a) the UAV‐based Structure from Motion (SfM) technology served as an effective and low‐cost means for capturing the spatial heterogeneity of mangrove canopy heights; (b) a maximum stand age of 15 years could be used to define the young growth stage of mangroves, for which the EVI‐height relationships could be described by exponential equations without suffering significant spectral saturation effects; (c) mangrove forests had overall faster annual AGB accumulation during the young growth stage over the post‐2000 versus pre‐2000 conservation period. This study is one of the first attempts to develop a remote sensing approach for quantifying spatially explicit AGB accumulation rates of young mangroves. It highlights the practicability and advantage of the UAV‐SfM technology and confirms that stronger conservation efforts promote mangrove AGB accumulation over the past two decades. The developed EVI‐height‐AGB framework fueled with readily available UAV and Landsat data provides a unique tool for assessing mangrove conservation effects from landscape to regional scales. Effective assessment tools for spatially explicit quantification of mangrove conservation effects are very limited. A novel remote sensing approach using readily available UAV and Landsat enhanced vegetation index data was proposed in this study to assess the spatial evolution of aboveground biomass of young mangroves in four subtropical estuarine mangroves in the southeastern coast of China. As one of the first attempts to develop a remote sensing approach for quantifying spatially explicit AGB accumulation rates of young mangroves, this study highlights the practicability and advantage of the UAV‐based structure‐from‐motion technology and confirms that stronger conservation efforts promote mangrove aboveground biomass accumulation over the past two decades.
... differs from the research of Pfeifer et al. (2012) and Potapov et al. (2012). This insignificant association is because the government has initiated ecological compensation and ecological migration to reduce human disturbance (Zhang and Lu, 2012), and the low human pressure on the TP might have not been sufficient to affect NRs' effectiveness. ...
Protected areas (PAs) are the cornerstones of global vegetation conservation efforts, but growing evidence showed the limited effectiveness of PAs in some regions. Recent attempts to quantify conservation efficiency were mainly focused on vegetation coverage, overlooking other vegetation characteristics, such as greenness and productivity. Here, using multiple indices of vegetation status from satellite observations and a windows search strategy, we measured the conservation efficiency of nature reserves (NRs, the primary category of PAs in China) edge on vegetation greenness, cover, and productivity on the Tibetan Plateau. The results showed that NRs’ edges performed a weak, but significant role in vegetation growth. Over 40% of the areas showed a positive impact of NRs’ edge on vegetation growth in different degrees. However, about 10% of the areas located on NRs’ edge showed a noticeable opposite effect on greenness, cover, and productivity. Compared to some climatic and socio-economic factors (e.g., population density and air temperature), fragmented landscapes and landforms are more likely to inhibit conservation efficiency. The findings of this work can help better understand PAs’ role in securing vegetation conservation and optimize the design of PAs for preventing vegetation losses.
... For each year, the rainy season was defined from October of the year prior to March of the current year and the dry season from April to September of the current year. These compositions were made by obtaining the best pixel, according to the methodology described by (Potapov et al 2012(Potapov et al , 2019. ...
Advances in monitoring capacity and strengthened law enforcement have helped to reduce deforestation in the Brazilian Amazon the early 2000’s. Embargoes imposed on the use of deforested land are important instruments for deterring deforestation and enabling forest recovery. However, the extent to which landowners respect embargoes in the Brazilian Amazon is unknown. In this study, we evaluated the current recovery status of embargoes due to deforestation imposed between 2008 and 2017 to conduct the first large-scale assessment of compliance with embargo regulations. We observed forest recovery in only 13.1% of embargoed polygons, while agriculture and pasture activities were maintained in 86.9% of embargoed polygons. Thus, landowners openly continue to disrespect environmental legislation in the majority of embargoed areas. We attribute the marked non-compliance observed to limited monitoring of embargoed areas, as environmental agents seldom return to verify the status of embargoed lands after they have been imposed. Recent advances in remote sensing provide low-cost ways to monitor compliance and should form the basis of concerted efforts to ensure that the law is observed and that those responsible for illegal deforestation do not benefit from it.
Securidaca longepedunculata Fresen. is an overexploited forest species in the Lubumbashi region (south-eastern DR Congo), as its roots are highly valued in traditional medicine. Conventional propagation of this species is affected by seed dormancy and a high mortality rate during early seedling development. To improve on existing methods, we developed an in vitro seed germination protocol. After observing the germination rates, the effects of different doses (0.5, 1, 1.5, and 2 mg/L)
of cytokinins (6-benzylaminopurine, kinetin, and meta-topolin) on S. longepedunculata seedling development were compared. Our results showed that soaking for 10 min in NaOCl (10%) followed by 5 min in ethanol (70%) effectively reduced the death rate of seeds while increasing the germination rate to almost 77%. The addition of cytokinins improved plantlet growth: a 12.2� increase in the
number of plantlets was obtained with 1.5 mg/L meta-topolin, while only a single stem was obtained from the control. The effects of different auxin types on rhizogenesis did not differ significantly. The best recovery and rooting were noted with microcuttings from the basal parts of S. longepedunculata plantlets. Finally, the seedlings produced survived during the acclimatisation phase regardless of
the type of substrate used. The established protocol provides a means for large-scale production of S. longepedunculata plantlets for the restoration of degraded landscapes and agroforestry.
The accuracy of existing forest cover products typically suffers from “rounding” errors arising from classifications that estimate the fractional cover of forest in each pixel, which often exclude the presence of large, isolated trees and small or narrow forest clearings, and is primarily attributable to the moderate resolution of the imagery used to make maps. However, the degree to which such high-resolution imagery can mitigate this problem, and thereby improve large-area forest cover maps, is largely unexplored. Here, we developed an approach to map tropical forest cover at a fine scale using Planet and Sentinel-1 synthetic aperture radar (SAR) imagery in the Google Earth Engine platform and used it to map all of Southeastern Asia’s forest cover. The machine learning approach, based on the Random Forests models and trained and validated using a total of 37,345 labels collected from Planet imagery across the entire region, had an accuracy of 0.937 and an F1 score of 0.942, while a version based only on Planet imagery had an accuracy of 0.908 and F1 of 0.923. We compared the accuracy of our resulting maps with 5 existing forest cover products derived from medium-resolution optical-only or combined optical-SAR approaches at 3,000 randomly selected locations. We found that our approach overall achieved higher accuracy and helped minimize the rounding errors commonly found along small or narrow forest clearings and deforestation frontiers where isolated trees are common. However, the forest area estimates varied depending on topographic location and showed smaller differences in highlands (areas >300 m above sea level) but obvious differences in complex lowland landscapes. Overall, the proposed method shows promise for monitoring forest changes, particularly those caused by deforestation frontiers. Our study also represents one of the most extensive applications of Planet imagery to date, resulting in an open, high-resolution map of forest cover for the entire Southeastern Asia region.
In the Kundelungu National Park (KNP), southeast of the Democratic Republic of Congo, illicit human activities including recurrent bushfires contribute to constant regression of forest cover. This study quantifies the landscape dynamics and analyses the spatio-temporal distribution of bushfire occurrence within KNP. Based on classified Landsat images from 2001, 2008, 2015 and 2022, the evolutionary trend of land cover was mapped and quantified through landscape metrics. The
spatial transformation processes underlying the observed landscape dynamics were identified based on a decision tree. Finally, the spatio-temporal fire risk assessment was carried out after defining the burnt areas for each year between 2001 and 2022. The obtained results, expressed by the process of dissection and attrition of patches, show that the forest cover has regressed from 2339 km² to 1596 km² within the PNK, with an annual deforestation rate varying from 0.8% to 3.4% between 2001 and 2022. Over the same period, the average distance between forest patches has increased significantly, indicating fragmentation and spatial isolation. On the other hand, savannahs as well as field and fallow mosaics have expanded within KNP through the creation of new patches. In addition, several active fires affected more savannahs between 2001 (70 km² in Integral Zone, 239 km² in Annex Zone and 309 km² in KNP) and 2022 (76 km² in Integral Zone, 744 km² in Annex Zone and 819 km² in KNP), limiting their capacity to evolve into forests. Overall, anthropogenic pressure is higher in the Annex Zone of the KNP. Illegal agricultural development and vegetation fires have thus doubled the level of landscape disturbance in 21 years. Our observations justify the need to
strengthen protection measures for KNP by limiting repeated human intrusions.
The misappropriation of sustainable forest programs by local communities and the under-utilization of their knowledge are major impediments to the mitigation of deforestation. Within this context, participation has become a principle used in almost all interventions. It is important to evaluate the practices in this area to ensure better involvement of local communities. This survey examined the perception and participation of local communities in the management of miombo woodlands, based on semi-structured questionnaire surveys involving 945 households in 5 villages in the Lubumbashi rural area. The results reveal that local communities perceive soil fertility loss and deforestation as major environmental challenges in their area. This perception remains largely influenced by their socio-demographic factors such as respondents' age, seniority in the villages, and level of education. To mitigate deforestation, the rare actions of provincial public services and non-governmental organizations are focused on the sustainable exploitation of miombo woodlands through the development of simple management plans, reforestation, and forest control. These activities are sparse and poorly inclusive of scientific findings and the priorities of local communities. These justify poor community participation, particularly in the actions of provincial public services. For a better appropriation of sustainable forest management plans and to reinforce miombo woodlands' resilience to anthropogenic pressures, based on these findings, we recommend a concerted and inclusive approach to forest planning.
Primary forest conservation is essential for limiting climate change, for meeting conservation objectives, and the Sustainable Development Goals. Schemes that compensate communities for forgone extractive uses are important policy tools, but effective deployment demands an understanding of local deforestation drivers and host communities’ preferences. We use Q-methodology to reveal discourses present in three communities in the Democratic Republic of Congo. Our results reveal three factors with a common emphasis on forest conservation and preferences for compensation in the form of social investments, rather than cash. The main contrasts were in attitudes towards farming. The first discourse, we call conservationist—open to ideas, displayed a commitment to learning better practices for community material benefit in service of forest conservation. The second discourse, which demonstrated greater confidence in their capacity to support livelihoods from farming, we call aspirational artisans. The third, which was acutely aware of the impact of their farming on forest conservation, we called passive, conflicted farmers. We also demonstrate an aspiration for the continued development of farming amongst participants, which although still correlated with preferences for forest conservation, may lead to compensation schemes inadvertently stirring future land use tensions if design does not reconcile agricultural development and conservation.
Deforestation and forest degradation is a global concern, especially in developing countries. The Margalla Hills of Pakistan—Himalayan foothills—also face the threat of deforestation and forest degradation. These Margalla Hills, considering the need for forest protection activities in Pakistan, were declared a reserved national forest and named “the Margalla Hills National Park (MHNP)”. This study quantitively evaluates whether deforestation and forest degradation have occurred at MHNP and identifies their possible drivers. Satellite (Landsat) data 1988–2020 was employed for the land use change analysis, whereas a socio-economic survey of the local population and structured interviews with government officials were conducted to identify the drivers of deforestation. Supervised classification was performed for imagery classification and the Vegetation Condition Index (VCI) was also calculated to measure degradation. Supervised classification showed that the forest cover increased from 65% of the total area in 1988 to 69% in 2020. The VCI results show that the moderate level of degradation has increased from 3.5% of MHNP area in 1988 to 8.8% in 2020. The cumulative measure of degradation from 1988 to 2020 is 1.09% of the total forest (using p < 0.05). Major drivers identified are fuel wood and timber collection. The results reveal a decline in both deforestation and forest degradation. There is a need for further quantitative analysis of the drivers, strict implementation of legislative and control measures, and continuous invigilation of the deforestation trends in MHNP.
Anthropogenic impact and population growth have caused a dramatic loss of biodiversity worldwide. Deforestation due to logging, mining, and burning are of particular severity in tropical rainforests with the Amazonian and Congolese basins harboring the largest reminders on our planet. While research projects particularly those with permanent presence on ground have been considered an excellent conservation measures to protect habitat and wildlife, no studies are known to assess their negative implications. Here, we assess the impact of a long-term research project on the tropical rainforest in the Democratic Republic of the Congo (DRC). We investigate the LuiKotale Bonobo project (LKBP) established for research and conservation in 2002, closely cooperating with several villages located in the buffer zone of Salonga National Park Block South, Territoire d'Inongo, Province Mai-Ndombe, DRC. We combine the results of Land Use and land Cover (LULC) drawn from satellite imagery with population data for four villages comparing anthropogenic impact before and after establishment of the project covering 31 years between 1987 and 2018. While deforestation decreased in Lompole, the first and main village of collaboration, it increased continuously over time in neighboring villages. Increase can be linked to population growth and cash income provided by the LKBP with habitants investing into construction material and expansion of agricultural fields for cash crops.
In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of R are shown to be effective in remote sensing data processing tasks, such as classification using k-means clustering and computing the Normalized Difference Vegetation Index (NDVI). The data are processed using an integration of the RStoolbox, terra, raster, rgdal and auxiliary packages of R. The proposed approach to image processing using R is designed to exploit the parameters of image bands as cues to detect land cover types and vegetation parameters corresponding to the spectral reflectance of the objects represented on the Earth’s surface. Our method is effective at processing the time series of the images taken at various periods to monitor the landscape dynamics in the middle part of the Congo River basin, Democratic Republic of the Congo (DRC). Whereas previous approaches primarily used Geographic Information System (GIS) software, we proposed to explicitly use the scripting methods for satellite image analysis by applying the extended functionality of R. The application of scripts for geospatial data is an effective and robust method compared with the traditional approaches due to its high automation and machine-based graphical processing. The algorithms of the R libraries are adjusted to spatial operations, such as projections and transformations, object topology, classification and map algebra. The data include Landsat-8 OLI-TIRS covering the three regions along the Congo river, Bumba, Basoko and Kisangani, for the years 2013, 2015 and 2022. We also validate the performance of graphical data handling for cartographic visualization using R libraries for visualising changes in land cover types by k-means clustering and calculation of the NDVI for vegetation analysis.
NDVI в период с 2014 по 2017 гг. Объектом исследования являются территории четырех провинций Центрального Ирака: Бабиля, Багдада, Васита и Диялы. Предмет исследования-временно-территориальная изменчивость состояния растительности. Методы: дистанционного зондирования Земли, тематического картирования, обработки изображений, пространственного анализа данных, статистического анализа. Результаты. С использованием вегетационного индекса, рассчитанного по данным дистанционного зондирования Земли, полученным со спутника Landsat-8, выявлены закономерности временно-территориальной динамики состояния раститель-ности территории Центрального Ирака. Установлены региональные особенности, проявившиеся в структуре раститель-ности и скорости ее изменения. Максимальные площади земель, не покрытых растительностью, выявлены в провинциях Багдад и Васит; покрытых растительностью большинства классов-также в Васите; покрытых плотной растительно-стью-в Дияле, отмеченной также минимумом бесплодных территорий. Бабиль характеризуется минимальной площадью, покрытой растительностью. Багдад по изученным показателям занимает промежуточное положение. Данные закономерно-сти слабо согласуются с абсолютными значениями площадей провинций. Они вызваны сложившимися условиями хозяйство-вания и проявляются в качественной и количественной неоднородности распределения растительности по провинциям. Во время активной стадии вегетации, при переходе от февраля к марту, вариации площадей, не занятых растительностью, занятых умеренной и плотной растительностью, стабильны; в то же время происходит рост вариации площадей с очень плотной и падение площадей со слабой растительностью и растительностью максимальной плотности. Корреляционный анализ между показателями NDVI и погодными условиями достоверных связей не выявил. Ключевые слова: Вегетационный индекс, геоинформационная система, данные дистанционного зондирования, классы растительности, сельское хозяйство, Центральный Ирак. Введение В процессе изменения климата и деятельности че-ловека в мире происходит прогрессирующая деграда-ция земель [1], которая ведет к снижению плодородия почвы, сокращению площади пашни и ухудшению качества жизни населения [2]. В засушливых, полуза-сушливых и сухих субгумидных районах культурные растения возделывают вблизи границ их экологиче-ской толерантности, поэтому они особенно чувстви-тельны к изменениям внешней среды, и сельскохо-зяйственное производство связано с высокими риска-ми. С другой стороны, игнорирование биологических особенностей почв ведет к деградации сельскохозяй-ственных (с.-х.) земель [3-5] и проявляется в форме засоления, опустынивания почв, снижения биологи-ческой продуктивности экосистем и др. [6, 7]. Для предотвращения деградации требуется комплекс ор-ганизационно-хозяйственных и технических меро-приятий, таких как искусственное орошение почвы, рекультивация засоленных земель, новые агротехни-ческие методы и технологии [8]. Перечисленные проблемы особенно актуальны для Республики Ирак, основу экономического благополу-чия которой составляют нефть и сельское хозяйство. Земли сельскохозяйственного назначения составляют примерно пятую часть территории Ирака, из них по-ловина расположена в долинах Евфрата и Тигра и
The uncontrolled logging of Pterocarpus tinctorius Welw. in the Kasenga territory in the southeast of the Democratic Republic of the Congo is of significant socioeconomic benefit, but above all, it is a threat to the stability of forest ecosystems. Based on Landsat images from 2009, 2013, 2017 and 2021, the landscape dynamics of the Kasomeno region in the Kasenga territory, a P. tinctorius exploitation area, was quantified using a mapping approach coupled with landscape ecology analysis tools. The results reveal a continuous loss of forest cover over all the periods studied, mostly between 2013 and 2017, primarily through the dissection of patches. Also, through the spatial process of attrition, the fields recorded a regressive dynamic between 2013-2017, a sign of abandon-ment of agricultural activity in favour of P. tinctorius illegal logging. These landscape dynamics are the consequences of strong anthropic activities in the study area, leading to an important spatial expansion of the savannah. Consequently, the level of landscape disturbance doubled from 0.8 to 1.7 between 2009 and 2021. Our results suggest that, without regulatory enforcement, illegal logging of P. tinctorius seriously compromises forest ecosystem health and household food security in the region.
Tidal wetlands are expected to respond dynamically to global environmental change, but the extent to which wetland losses have been offset by gains remains poorly understood. We developed a global analysis of satellite data to simultaneously monitor change in three highly interconnected intertidal ecosystem types—tidal flats, tidal marshes, and mangroves—from 1999 to 2019. Globally, 13,700 square kilometers of tidal wetlands have been lost, but these have been substantially offset by gains of 9700 km ² , leading to a net change of −4000 km ² over two decades. We found that 27% of these losses and gains were associated with direct human activities such as conversion to agriculture and restoration of lost wetlands. All other changes were attributed to indirect drivers, including the effects of coastal processes and climate change.
Forest loss is one of the major environmental issues and is threatening livelihoods across the world. Understanding the drivers of forest cover loss in the different forest cover densities and its impacts on ecosystem services are relevant for sustainable management of the forest ecosystem. Extensive studies exploring topics related to spatial-temporal deforestation, little is known regarding quantitative forest loss by individual disturbance driver and public's perceptions of forest loss's impacts on provisioning ecosystem services. The present study focused on trends and patterns of forest loss by forest disturbance drivers in the different forest cover densities and its impacts on provisioning ecosystem services integrated with remote sensing data and local people's perceptions in the dry deciduous forest of West Bengal. This study employs stratified probability sampling approach using time series Landsat data on Google Earth Engine platform to quantify forest loss between 2006 and 2020 in the different pre-disturbance forest cover densities by forest disturbance drivers. Structured household interviews were used to examine local people's perceptions about forest cover loss and its impacts on ecosystem services. We found that 68.463% of forest area is cleared by disturbance drivers between 2006 and 2020. Annual rate of forest clearing is increasing trend and dominating in dense forests, followed by medium forests and open forests. Logging and agricultural clearing was the most dominant drivers over space and time. Overall 73.92% of households perceived decline in the availability of provisioning ecosystem services. Wild foods and medicinal plants were the most affected ecosystem services followed by livestock feed, construction materials and fuel wood. This study may help to the regional assessment of gross forest loss by individual disturbance driver, and its impacts on provisioning ecosystem services for supporting decision making to sustainable forest management.
Synthetic aperture radar (SAR) has been a powerful tool for deforestation detection in tropical rainforests. Polarimetric SAR (POLSAR) data are acquired in a quad-polarization mode, and L-band POLSAR data in particular are one of the few SAR data types that preserve the dielectric properties and structures of the scatterer. POLSAR data consist of 2 × 2 scattering matrices and consequently offer superior target recognition compared with dual-polarization data. In this study, we applied scattering power decomposition suitable for detecting deforestation in near real-time to POLSAR data obtained from the Earth observation sensor Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2). The reflection symmetry condition is known to apply to natural distributed objects (i.e., the cross-correlation between co- and cross-polarization data is zero). Inspired by this, we theoretically and experimentally examined the volume scattering power component to distinguish natural forests from the surrounding area. Two important results were verified for natural forests: as the window size of the ensemble average increases, (i) the coherency matrix approaches a simple theoretical form and (ii) the volume scattering power becomes dominant among the scattering power components. Based on these results, we constructed an algorithm that applies scattering power decomposition for detecting deforestation. We produced reference data using high-resolution optical images and evaluated the performance of the derived deforestation map in the Amazon natural forest when employing various window sizes for the ensemble average. At an optimal window size of 15 × 15 pixels, the deforestation detection performance reached a user's accuracy of 94.9% ± 1.5%, a producer's accuracy of 72.3% ± 1.2%, and a kappa coefficient of 0.816 ± 0.0039. Sparse trees left after logging increased the volume scattering power and reduced the producer's accuracy. The proposed algorithm can contribute to deforestation detection with slightly lower accuracy than that of the annual map provided by Global Forest Change. Further, the proposed algorithm is robust to the seasonal variations in tropical rainforests and temporal variations in the deforestation process. Consequently, the proposed algorithm employing the six-component scattering power decomposition method can be utilized in near real-time without considering applicable areas in tropical rainforests. Subsequently, we applied our algorithm to dual-polarization data, which are acquired by PALSAR-2 much more frequently than POLSAR data. The false detection rate did not increase when using the dual-polarization data; however, the omission error increased considerably compared with that when using POLSAR data owing to the low total power obtained from the dual-polarization data.
The forests of the Congo basin
State of the forest 2008
The 2008 State of the Forest report benefited from financial support from the European Union, France, Germany and the United States, as well as the ECOFAC Program and UNESCO. It represents the collaborative effort of over 100 individuals from a diversity of institutions and the forestry administrations of the Central African countries. The process to elaborate the 2008 State of the Forest (SOF) report began with the selection and definition of indicators relevant to monitoring the state of forests in Central Africa. The indicators are structured around three thematic areas: (i) forest cover; (ii) management of production forests; and (iii) conservation and biodiversity. They are presented in a hierarchical structure at the regional, national and management unit (i.e. logging concessions and protected areas) levels. The indicators were vetted by a representative panel of stakeholders during a workshop held in Kribi in February 2008. The indicators were used to guide the collection of data from April to August 2008, by national groups of four to ten individuals working within the forestry administrations. The majority of data collected is from 2006 and 2007. Results were validated in national workshops attended by government officials as well as representatives of environmental non-governmental organizations, the private sector and development projects. The entire report was reviewed by a scientific committee of international renown.
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Corporate author(s): European Commission Private author(s): Carlos de Wasseige, Didier Devers, Paya de Marcken Themes: Forestry, Environment policy and protection of the environment Target audience: Specialised/Technical Key words: forest conservation, silviculture, forestry policy, sustainable forest management, Central Africa
The LandScan Global Population Project produced a worldwide 1998 population database at 30" X 30" resolution for estimating ambient populations at risk. Best available census counts were distributed to cells based on probability coefficients which, in turn, were based on road proximity, slope, land cover, and nighttime lights. LandScan 1998 has been completed for the entire world. Verification and validation (V&V) studies were conducted routinely for all regions and more extensively for Israel, Germany, and the Southwestern United States. Geographic information systems (GIS) were essential for conflation of diverse input variables, computation of probability coefficients, allocation of population to cells, and reconciliation of cell totals with aggregate (usually province) control totals. Remote sensing was an essential source of two input variables–land cover and nighttime lights–and one ancillary database–high-resolution panchromatic imagery–used in V&V of the population model and resulting LandScan database.
Mapping northern Canada with medium spatial resolution (30 m) Landsat data is important to complement national multiagency activities in forested and agricultural regions, and thus to achieve full Canadian coverage. Northern mapping presents unique challenges due to limited availability of field data for calibration or class labeling. Additional problems are caused by variability between individual Landsat scenes acquired under different atmospheric conditions and at different times. Therefore, the generation of radiometrically consistent coverage is highly desirable to reduce the amount of reference data required for land cover mapping and to increase mapping efficiency and consistency by stabilizing spectra of land cover classes among hundreds of Landsat scenes. The production chain and dataset of a normalized, 90 m resolution Landsat enhanced thematic mapper plus (ETM+) mosaic of northern Canada is presented in this research note. A robust regression technique called Thiel–Sen (TS) is used to normalize Landsat scenes to consistent coarse-resolution VEGETATION (VGT) imagery. The derived dataset is available for any interested user and can be employed in applications aimed at studying processes in the Canadian Arctic regions above the tree line.
Measuring the aerial extent of tropical deforestation for other than localized areas requires the use of satellite data. We present evidence to show that an accurate determination of tropical deforestation is very di Å cult to achieve by a ' random sampling' analysis of Landsat or similar high spatial resolution data unless a very high percentage of the area to be studied is sampled. In order to achieve a Landsat-derived deforestation estimate within Ô 20% of the actual deforestation amount 90% of the time, 37 of 40 scenes, 55 of 61 scenes and 37 of 45 scenes were required for Bolivia, Colombia and Peru respectively.
The first results of the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous field algorithm's global percent tree cover are presented. Percent tree cover per 500-m MODIS pixel is estimated using a supervised regression tree algorithm. Data derived from the MODIS visible bands contribute the most to discriminating tree cover. The results show that MODIS data yield greater spatial detail in the characterization of tree cover compared to past efforts using AVHRR data. This finer-scale depiction should allow for using successive tree cover maps in change detection studies at the global scale. Initial validation efforts show a reasonable relationship between the MODIS-estimated tree cover and tree cover from validation sites.
The Indonesian islands of Sumatera and Kalimantan (the Indonesian part of the island of Borneo) are a center of significant and rapid forest cover loss in the humid tropics with implications for carbon dynamics, biodiversity conservation, and local livelihoods. The aim of our research was to analyze and interpret annual trends of forest cover loss for different sub-regions of the study area. We mapped forest cover loss for 2000–2008 using multi-resolution remote sensing data from the Landsat enhanced thematic mapper plus (ETM+) and moderate resolution imaging spectroradiometer (MODIS) sensors and analyzed annual trends per island, province, and official land allocation zone. The total forest cover loss for Sumatera and Kalimantan 2000–2008 was 5.39 Mha, which represents 5.3% of the land area and 9.2% of the year 2000 forest cover of these two islands. At least 6.5% of all mapped forest cover loss occurred in land allocation zones prohibiting clearing. An additional 13.6% of forest cover loss occurred where clearing is legally restricted. The overall trend of forest cover loss increased until 2006 and decreased thereafter. The trends for Sumatera and Kalimantan were distinctly different, driven primarily by the trends of Riau and Central Kalimantan provinces, respectively. This analysis shows that annual mapping of forest cover change yields a clearer picture than a one-time overall national estimate. Monitoring forest dynamics is important for national policy makers, especially given the commitment of Indonesia to reducing greenhouse gas emissions as part of the reducing emissions from deforestation and forest degradation in developing countries initiative (REDD+). The improved spatio-temporal detail of forest change monitoring products will make it possible to target policies and projects in meeting this commitment. Accurate, annual forest cover loss maps will be integral to many REDD+ objectives, including policy formulation, definition of baselines, detection of displacement, and the evaluation of the permanence of emission reduction.
An integrated framework for assessing conservation and development changes at the scale of a large forest landscape in the Congo Basin is described. The framework allows stakeholders to assess progress in achieving the often conflicting objectives of alleviating poverty and conserving global environmental values. The study shows that there was little change in either livelihood or conservation indicators over the period 2006 to 2008, and that the activities of conservation organizations had only modest impacts on either. The global economic down-turn in 2008 had immediate negative consequences for both local livelihoods and for biodiversity as people lost their employment in the cash economy and reverted to illegal harvesting of forest products. Weakness of institutions, and corruption were the major obstacles to achieving either conservation or development objectives. External economic changes had more impact on this forest landscape than either the negative or positive interventions of local actors. © Dominique Endamana, Agni Klintuni Boedhihartono, Bruno Bokoto, Louis Defo, Antoine Eyebe, Cléto Ndikumagenge, Zacharie Nzooh, Manuel Ruiz-Perez and Jeffrey A. Sayer.
Digital analysis of remotely sensed data has become an important component of many earth-science studies. These data are often processed through a set of preprocessing or “clean-up” routines that includes a correction for atmospheric scattering, often called haze. Various methods to correct or remove the additive haze component have been developed, including the widely used dark-object subtraction technique. A problem with most of these methods is that the haze values for each spectral band are selected independently. This can create problems because atmospheric scattering is highly wavelength-dependent in the visible part of the electromagnetic spectrum and the scattering values are correlated with each other. Therefore, multispectral data such as from the Landsat Thematic Mapper and Multispectral Scanner must be corrected with haze values that are spectral band dependent. An improved dark-object subtraction technique is demonstrated that allows the user to select a relative atmospheric scattering model to predict the haze values for all the spectral bands from a selected starting band haze value. The improved method normalizes the predicted haze values for the different gain and offset parameters used by the imaging system. Examples of haze value differences between the old and improved methods for Thematic Mapper Bands 1, 2, 3, 4, 5, and 7 are 40.0, 13.0, 12.0, 8.0, 5.0, and 2.0 vs. 40.0, 13.2, 8.9, 4.9, 16.7, and 3.3, respectively, using a relative scattering model of a clear atmosphere. In one Landsat multispectral scanner image the haze value differences for Bands 4, 5, 6, and 7 were 30.0, 50.0, 50.0, and 40.0 for the old method vs. 30.0, 34.4, 43.6, and 6.4 for the new method using a relative scattering model of a hazy atmosphere.
Satellite sensors are well suited to monitoring changes on the Earth's surface through provision of consistent and repeatable measurements at a spatial scale appropriate for many processes causing change on the land surface. Here, we describe and test a new conceptual approach to change detection of forests using a dense temporal stack of Landsat Thematic Mapper (TM) imagery. The central premise of the method is the recognition that many phenomena associated with changes in land cover have distinctive temporal progressions both before and after the change event, and that these lead to characteristic temporal signatures in spectral space. Rather than search for single change events between two dates of imagery, we instead search for these idealized signatures in the entire temporal trajectory of spectral values. This trajectory-based change detection is automated, requires no screening of non-forest area, and requires no metric-specific threshold development. Moreover, the method simultaneously provides estimates of discontinuous phenomena (disturbance date and intensity) as well as continuous phenomena (post-disturbance regeneration). We applied the method to a stack of 18 Landsat TM images for the 20-year period from 1984 to 2004. When compared with direct interpreter delineation of disturbance events, the automated method accurately labeled year of disturbance with 90% overall accuracy in clear-cuts and with 77% accuracy in partial-cuts (thinnings). The primary source of error in the method was misregistration of images in the stack, suggesting that higher accuracies are possible with better registration.
Since January 2008, the U.S. Department of Interior / U.S. Geological Survey have been providing free terrain-corrected (Level 1T) Landsat Enhanced Thematic Mapper Plus (ETM+) data via the Internet, currently for acquisitions with less than 40% cloud cover. With this rich dataset, temporally composited, mosaics of the conterminous United States (CONUS) were generated on a monthly, seasonal, and annual basis using 6521 ETM+ acquisitions from December 2007 to November 2008. The composited mosaics are designed to provide consistent Landsat data that can be used to derive land cover and geo-physical and bio-physical products for detailed regional assessments of land-cover dynamics and to study Earth system functioning. The data layers in the composited mosaics are defined at 30 m and include top of atmosphere (TOA) reflectance, TOA brightness temperature, TOA normalized difference vegetation index (NDVI), the date each composited pixel was acquired on, per-band radiometric saturation status, cloud mask values, and the number of acquisitions considered in the compositing period. Reduced spatial resolution browse imagery, and top of atmosphere 30 m reflectance time series extracted from the monthly composites, capture the expected land surface phenological change, and illustrate the potential of the composited mosaic data for terrestrial monitoring at high spatial resolution. The composited mosaics are available in 501 tiles of 5000 × 5000 30 m pixels in the Albers equal area projection and are downloadable at http://landsat.usgs.gov/WELD.php. The research described in this paper demonstrates the potential of Landsat data processing to provide a consistent, long-term, large-area, data record.
This study examines the suitability of 250 m MODIS (MODerate Resolution Imaging Spectroradiometer) data for mapping global cropland extent. A set of 39 multi-year MODIS metrics incorporating four MODIS land bands, NDVI (Normalized Difference Vegetation Index) and thermal data was employed to depict cropland phenology over the study period. Sub-pixel training datasets were used to generate a set of global classification tree models using a bagging methodology, resulting in a global per-pixel cropland probability layer. This product was subsequently thresholded to create a discrete cropland/non-cropland indicator map using data from the USDA-FAS (Foreign Agricultural Service) Production, Supply and Distribution (PSD) database describing per-country acreage of production field crops. Five global land cover products, four of which attempted to map croplands in the context of multiclass land cover classifications, were subsequently used to perform regional evaluations of the global MODIS cropland extent map. The global probability layer was further examined with reference to four principle global food crops: corn, soybeans, wheat and rice. Overall results indicate that the MODIS layer best depicts regions of intensive broadleaf crop production (corn and soybean), both in correspondence with existing maps and in associated high probability matching thresholds. Probability thresholds for wheat-growing regions were lower, while areas of rice production had the lowest associated confidence. Regions absent of agricultural intensification, such as Africa, are poorly characterized regardless of crop type. The results reflect the value of MODIS as a generic global cropland indicator for intensive agriculture production regions, but with little sensitivity in areas of low agricultural intensification. Variability in mapping accuracies between areas dominated by different crop types also points to the desirability of a crop-specific approach rather than attempting to map croplands in aggregate.
A recently completed research program (TREES) employing the global imaging capabilities of Earth-observing satellites provides
updated information on the status of the world's humid tropical forest cover. Between 1990 and 1997, 5.8 ± 1.4 million hectares
of humid tropical forest were lost each year, with a further 2.3 ± 0.7 million hectares of forest visibly degraded. These
figures indicate that the global net rate of change in forest cover for the humid tropics is 23% lower than the generally
accepted rate. This result affects the calculation of carbon fluxes in the global budget and means that the terrestrial sink
is smaller than previously inferred.
Land-cover change in eastern lowland Bolivia was documented using Landsat images from five epochs for all landscapes situated below the montane tree line at approximately 3000 m, including humid forest, inundated forest, seasonally dry forest, and cloud forest, as well as scrublands and grasslands. Deforestation in eastern Bolivia in 2004 covered 45,411 km2, representing approximately 9% of the original forest cover, with an additional conversion of 9042 km2 of scrub and savanna habitats representing 17% of total historical land-cover change. Annual rates of land-cover change increased from approximately 400 km2 y(-1) in the 1960s to approximately 2900 km2 y(-1) in the last epoch spanning 2001 to 2004. This study provides Bolivia with a spatially explicit information resource to monitor future land-cover change, a prerequisite for proposed mechanisms to compensate countries for reducing carbon emissions as a result of deforestation. A comparison of the most recent epoch with previous periods shows that policies enacted in the late 1990s to promote forest conservation had no observable impact on reducing deforestation and that deforestation actually increased in some protected areas. The rate of land-cover change continues to increase linearly nationwide, but is growing faster in the Santa Cruz department because of the expansion of mechanized agriculture and cattle farms.
![Figure][1]
Free image.
This Landsat 5 image of the southeastern corner of the Black Sea is part of the general U.S. archive that will be accessible for free under the new USGS policy.
CREDIT: BOSTON UNIVERSITY CENTER FOR REMOTE SENSING
We are entering a new era in the Landsat Program, the
The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center has processed and released 2100 Landsat Thematic Mapper and Enhanced Thematic Mapper Plus surface reflectance scenes, providing 30-m resolution wall-to-wall reflectance coverage for North America for epochs centered on 1990 and 2000. This dataset can support decadal assessments of environmental and land-cover change, production of reflectance-based biophysical products, and applications that merge reflectance data from multiple sensors [e.g., the Advanced Spaceborne Thermal Emission and Reflection Radiometer, Multiangle Imaging Spectroradiometer, Moderate Resolution Imaging Spectroradiometer (MODIS)]. The raw imagery was obtained from the orthorectified Landsat GeoCover dataset, purchased by NASA from the Earth Satellite Corporation. Through the LEDAPS project, these data were calibrated, converted to top-of-atmosphere reflectance, and then atmospherically corrected using the MODIS/6S methodology. Initial comparisons with ground-based optical thickness measurements and simultaneously acquired MODIS imagery indicate comparable uncertainty in Landsat surface reflectance compared to the standard MODIS reflectance product (the greater of 0.5% absolute reflectance or 5% of the recorded reflectance value). The rapid automated nature of the processing stream also paves the way for routine high-level products from future Landsat sensors.
Digital procedures to optimize the information content of
multitemporal Landsat TM data sets for forest cover change detection are
described. Imagery from three different years (1984, 1986, and 1990)
were calibrated to exoatmospheric reflectance to minimize sensor
calibration offsets and standardize data acquisition aspects. Geometric
rectification was followed by atmospheric normalization and correction
routines. The normalization consisted of a statistical regression over
time based on spatially well-defined and spectrally stable landscape
features spanning the entire reflectance range. Linear correlation
coefficients for all bitemporal band pairs ranged from 0.9884 to 0.9998.
The correction mechanism used a dark object subtraction technique
incorporating published values of water reflectance. The association
between digital data and forest cover was maximized and interpretability
enhanced by converting band-specific reflectance values into vegetation
indexes. Bitemporal vegetation index pairs for each time interval (two,
four, and six years) were subjected to two change detection algorithms,
standardized differencing and selective principal component analysis.
Optimal feature selection was based on statistical divergence measures.
Although limited to spectrally-radiometrically defined change classes,
results show that the relationship between reflective TM data and forest
canopy change is explicit enough to be of operational use in a forest
cover change stratification phase prior to a more detailed assessment
The impact of misregistration on the detection of changes in land
cover has been evaluated using spatially degraded Landsat MSS images,
focusing on simulated images of the normalized difference vegetation
index (NDVI). Single-date images from seven areas were misregistered
against themselves, and the statistical properties of the differences
were analyzed. In the absence of any actual changes to the land surface,
the consequences of misregistration were very marked even for subpixel
misregistrations. Pairs of images from different time periods were then
misregistered. For four densely covered areas, an error equivalent to
greater than 50% of the actual differences in the NDVI, as measured by
the semivariance, was induced by a misregistration of only one pixel. To
achieve an error of only 10%, registration accuracies of 0.2 pixels or
less are required. For three more sparsely vegetated areas with semiarid
climates, a registration accuracy of between 0.5 and 1.0 pixel was
sufficient to achieve an error of 10% or less. The results indicate that
high levels of registration are needed for reliable monitoring of global
change
Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.
NASA has sponsored the creation of an orthorectified and geodetically accurate global land data set of Landsat Multispectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper data, from the 1970s, circa 1990, and circa 2000, respectively, to support a variety of scientific studies and educational purposes. This is the first time a geodetically accurate global compendium of orthorectified multi-epoch digital satellite data at the 30- to 80-m spatial scale spanning 30 years has been produced for use by the international scientific and educational communities. We describe data selection, orthorectification, accuracy, access, and other aspects of these data.
Land cover change occurs at various spatial and temporal scales. For example, large-scale mechanical removal of forests for
agro-industrial activities contrasts with the small-scale clearing of subsistence farmers. Such dynamics vary in spatial extent
and rate of land conversion. Such changes are attributable to both natural and anthropogenic factors. For example, lightning-
or human-ignited fires burn millions of acres of land surface each year. Further, land cover conversion requires contrasting
with the land cover modification. In the first instance, the dynamic represents extensive categorical change between two land
cover types. Land cover modification mechanisms such as selective logging and woody encroachment depict changes within a given
land cover type rather than a conversion from one land cover type to another. This chapter describes the production of two
standard MODIS land products used to document changes in global land cover. The Vegetative Cover Conversion (VCC) product
is designed primarily to serve as a global alarm for areas where land cover change occurs rapidly (Zhan et al. 2000). The Vegetation Continuous Fields (VCF) product is designed to continuously represent ground cover as a proportion of basic
vegetation traits. Terra’s launch in December 1999 afforded a new opportunity to observe the entire Earth every 1.2 days at
250-m spatial resolution. The MODIS instrument’s appropriate spatial and temporal resolutions provide the opportunity to
substantially improve the characterization of the land surface and changes occurring thereupon (Townshend et al. 1991).
For many remote sensing studies it is desirable to have a radiometrically matched time series of images or image mosaics. This paper examines the use of an empirical bi- directional reflectance distribution function (BRDF) model for radiometric correction of Landsat 7 ETM+ and Landsat 5 TM imagery. The method combines a simple top-of- atmosphere (TOA) reflectance adjustment with an empirical BRDF model. The model parameters were derived from an overlapping sequence of Landsat 7 ETM+ images and were applied to produce spatially matched mosaics of Landsat ETM+ and TM imagery. The effect of different land cover types and different levels of woody vegetation foliage cover were also investigated. Land cover type and the amount of woody foliage cover were shown to influence the BRDF factor, but a generalised correction also provided a considerable improvement in scene to scene radiometric match when compared to a TOA reflectance adjustment only.
Medium resolution (10-100m) optical sensor data such as those from the Landsat, SPOT, ASTER, CBERS and IRS-P6 satellites provide detailed spatial information for studies of ecosystems, vegetation biophysics, and land cover. While Landsat remains a cornerstone of medium resolution remote sensing, the ETM+ scan-line corrector failure in 2003 has highlighted the need for methods to integrate radiometry from multiple international sensors in order to create a consistent, long-term observational record. Such an approach needs to compensate for differing acquisition plans, sensor bandwidths, spatial resolution, and orbit coverage. Different processing approaches used in the calibration and atmosphere correction across sensors make integration even harder. In this paper, we propose a generalized reference-based approach to convert medium resolution satellite digital number (DN) to MODIS-like surface reflectance using MODIS products as a reference data set. This approach does not require explicit calibration and atmospheric correction procedures for individual medium resolution sensors, therefore minimizing the potential impact of those procedures due to among-sensor differences. Therefore, data in MODIS era from different sources such as Landsat TM/ETM+, IRS-P6 AWiFS, and TERRA ASTER can be combined for time-series analysis, biophysical parameter retrievals, and other downstream analysis. Our results from Landsat TM/ETM+ show that this approach can produce surface reflectance with a similar accuracy to physical approaches based on radiative transfer modeling with mean absolute differences of 0.0016 and 0.0105 for red and near infra-red bands respectively. The normalized MODIS-like surface reflectances from multiple sensors and acquisition dates are consistent and comparable both spatially and temporally with known trends in phenology.
The study compares the applicability of different remote sensing data and digital change detection methods in detecting clear-cut areas in boreal forest. The methods selected for comparisons are simple and straightforward and thus applicable in practical forestry. The data tested were from Landsat satellite imagery and high-altitude panchromatic aerial orthophotographs. The change detection was based on image differencing. Three different approaches were tested: (1) pixel-by-pixel differencing and segmentation; (2) pixel block-level differencing and thresholding; and (3) presegmentation and unsupervised classification. The study shows that the methods and data sources used are accurate enough for operational detection of clear-cut areas. The study suggests that predelineated segments or pixel blocks should be used for image differencing to decrease the number of misinterpreted small areas. For the same reason the use of a digital forest mask is crucial in operational applications.