Article

Quantifying forest cover loss in Democratic Republic of the Congo, 2000-2010, with Landsat ETM+ data

Article

Quantifying forest cover loss in Democratic Republic of the Congo, 2000-2010, with Landsat ETM+ data

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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.

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... Deforestation is one of the main environmental problems in the Democratic Republic of the Congo (DRC) [1]. Studies show that deforestation and forest degradation cause disturbances at several levels, such as biodiversity loss, soil erosion and global warming [2]. Indeed, these two processes lead to the modification of the composition and configuration of forest landscapes [3]. ...
... Moreover, except in the OWR, these rates are above this average, particularly in the second period. Several authors share the same opinion that deforestation is increasing in the majority of forests [2,29]. ...
... Comparison of key deforestation figures obtained in this study with those of other similar studies should be done with caution since the methodologies and data used are not always compatible. FACET [2] estimates the area of old-growth forests in 2010 at 3,843,218.88 ha. ...
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Citation: Kabuanga, J.M.; Kankonda, O.M.; Saqalli, M.; Maestripieri, N.; Bilintoh, T.M., Mweru, J.-P.M.; Liama, A.B.; Nishuli, R.; Mané, L. Historical Changes and Future Trajectories of Deforestation in the Ituri-Epulu-Aru Landscape (Democratic Republic of the Congo). Land 2021, 10, 1042.
... Many tropical African countries suffer from a lack of alien species inventories despite recent efforts to fill this gap of knowledge (e.g., Maroyi 2012;Rejmánek et al. 2016;Witt et al. 2018;Ansong et al. 2019;Omer et al. 2021). Some recent national flora checklists incorporate information on introduced species (Mapaura and Timberlake 2004; Phiri 2005;Figueiredo and Smith 2008), as does the African Plants database (https://www.ville-ge.ch/musinfo/bd/ cjb/africa/index.php?langue=an). ...
... Agriculture is the largest sector in economy with 10 million ha cultivated (FAO 2013). D.R. Congo comprises 18% of the world's tropical forests, but the Congo Basin is subjected to steadily increasing human influence due to deforestation and urbanisation (Anonymous 2012;Potapov et al. 2013), which could favour the expansion of non-native species . ...
... (Pagad et al. 2018) comprises 397 alien seed plants for D.R. Congo (Groom et al. 2020 Pauwels (2014), and Lejoly et al. (2010). Checklists of alien species in neighbouring countries were mined to orient herbarium collection search for more species (Mapaura and Timberlake 2004;Phiri 2005;Bigirimana 2011;Maroyi 2012;Rejmánek et al. 2016;Anonymous 2016;Noba et al. 2017;Witt et al. 2018;Ansong et al. 2019). ...
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The Democratic Republic of the Congo (D.R. Congo) represents a striking gap of knowledge on alien plant species. In this paper, we use digitised herbarium collections to assemble a new checklist of alien plant species in D.R. Congo and to examine patterns in the alien flora. The new checklist comprises 436 alien species i.e., 189 (43%) casuals, 247 (57%) naturalised of which 80 (18% of aliens) are invasive. Discrepancies with previous databases are discussed. For many species in previous databases, all herbarium specimens come from cultivated specimens (e.g. botanic gardens) and we failed to find evidence for occurrence outside of cultivation. A total of 166 taxa were not included in previous lists, 41 of which are new records to the flora of D.R. Congo. Considering the size of the country and its rich native flora, the alien flora of D.R. Congo does not appear to be species-rich. The alien flora is particularly rich in Fabaceae (16%) and in annual species (37%). The Americas are by far the most important source continent (65%) and the proportion of annuals of American origin is particularly large among the most widespread species. 90% of invasive species are from the Americas. Invasive success is discussed in terms of residence time. The very low number of new species records after 1960 is similar to other African countries and could be due to decreasing sampling effort. The results illustrate how herbarium collections can be used to critically revise existing checklists of alien species in tropical Africa. Field work is urgently needed to improve coverage of recent introductions and to monitor the status of alien species, especially in protected areas and around botanic gardens.
... In DR Congo, Miombo woodland occupies nearly 286,000 km 2 , corresponding to almost 11% of the total Zambezian woodland area (Malaisse, 2010). However, more than 70% of the Miombo woodland are located in Katanga region (Southeastern DR Congo), where paradoxically, more than 350,000 ha of their areas were lost over the period 2000e2010 (Potapov et al., 2012), under the pressure of slash-and-burn agriculture, woodfuel production, mining, timber exploitation and urbanization Useni et al., 2017). Indeed, in the absence of fertilizers, the supply of mineral elements to crops is largely dependent on Miombo woodland, through the transfer of forest litter to the fields and the release of elements associated with slash-and-burn crops (Ryan et al., 2016). ...
... In addition, in Upper Katanga region, the supply of electrical energy to households is relatively limited (Banza et al., 2016) and they are consequently developing alternative approaches such as woodfuel for cooking food and bricks Kabulu et al., 2018). Indeed, charcoal production is a major driver of forest cover loss (Potapov et al., 2012;Bangirinama et al., 2016). ...
... This policy is based, among other things, on the interest of current and future generations in conserving biodiversity because of the resulting ecosystem services (Kalaba et al., 2013;Ryan et al., 2016;Useni et al., 2017). However, the administration is struggling to implement a coherent (sustainable) protected area management policy (P elissier et al., 2015) and, as an illustration, studies carried out in the central basin of D.R. Congo and in the Kivu region have shown that forest ecosystems located within protected areas have decreased in area (Potapov et al., 2012;Balol e et al., 2015;Kyale et al., 2019). Moreover, the quantification of this deforestation is intensely based on remote sensing data (Hansen et al., 2008;Bamba et al., 2010;Potapov et al., 2011;Cabala et al., 2018;Useni et al., 2019), the acquisition of which generally requires (very) good quality of internet connection and the commitment of significant financial costs (Semeki et al., 2016). ...
Conference Paper
Lufira Biosphere Reserve (LBR) is a protected area located in Southeastern DR Congo, created for the conservation of Miombo woodland, an ecosystem threatened by anthropogenic activities developed in the region. However, scientific studies regarding land cover dynamics within the LBR are non-existent to date. This study maps and quantifies the land cover dynamics within and around the LBR, based on diachronic analysis of five Landsat images (1979 (date of its recognizing by the UNESCO), 1986, 1998, 2008 and 2018) and field verification missions. Landscape metrics were utilized to understand changes in landscape pattern. The results indicate that Miombo woodland area have been reduced by a factor of three in the LBR, as they covered 11.2 km² in 2018 compared to 85.3 km² in 1979. The annual deforestation rate between 1979 and 2018 was 1.8%, almost eight-fold higher than the rate registered at the country level. Within the LBR, this deforestation has been offset by an increase in areas occupied by grassy savanna (+16.9 km²), as well as fields and fallows (+53.3 km²). Further, water and wetland area increased by 17.9 km² in 39 years whereas the wooded savanna, the bare soil and built-up decreased by 24.9 km² and 4.0 km² respectively. The increase in the proportion of grassy savanna is a tangible evidence of the degradation of past forest resources. In general, analysis of landscape spatial pattern dynamics through landscape metrics, showed a process of creation and aggregation of grassy savanna, water and wetlands, as well as fields and fallows, as opposed to dissection and attrition of Miombo woodland, wooded savanna, bare soil and built-up. Overall, the LBR has undergone a major transformation, mainly deforestation, due to demographic pressure and the development of subsistence activities in a precarious economic context. So, the small forest patches that persist within the LBR owe their existence to the unsuitable nature of their soils or their inaccessibility, due to their location on hills. Consequently, wooded savanna are in turn cultivated or their individuals of reduced stem diameter are also cut for carbonization. Indeed, wood energy production is seen as an essential supplement to household income, which accelerates deforestation and regression of wooded savanna. The study concludes that in the absence of any land use planning policy, LBR risks losing its status following lost of the rare Miombo woodland patches still existing. For this reason, the DRC has asked UNESCO to remove the LBR from the World Network of Biosphere Reserves.
... An additional constraint in obtaining a homogeneous national stratification map for the DRC is the classes aggregation found in existing maps, which do not adequately represent the classes described in the national stratification scheme. As an example, the class known as dense rainforest found in many land cover maps [10,11,3] is in fact an aggregation of several classes of the national stratification scheme, notably the dense moist forest, the edaphic forest, the secondary forest as well as the closed to open deciduous woodland (Miombo). Such aggregation is either the direct consequence of the spatial resolution of each map i.e. 1-km for [11] or it is tributary of the main objective pursued by the authors i.e. [3] whose vegetation map at 60 m spatial resolution mainly aimed at representing the major land cover classes at the national level. ...
... The forests of the Democratic Republic of Congo (DRC) represent around 60% of the overall Congo Basin forest estate and play an important role in the sequestration of atmospheric CO2, thus contributing to balancing the flow of global greenhouse gas emissions [1,2]. Monitoring the dynamics of Congolese forests is therefore of importance, particularly since the advent of the REDD+ mechanism [2,3]. In order to progress in meeting the requirements of the Warsaw Framework for REDD+, the DRC recently submitted to the United Nations Framework Convention on Climate Change (UNFCCC) its first Forest Reference Emission Level (FREL) covering the period 2000-2014 [4]. ...
... The main reason of that difference being that the latter map's coarser spatial resolution does not allow to distinguishing detailed land cover shapes. The Vgt DRC 2000 map is in turn very similar to the map produced by [3] (Figure 6, C-1) that is at 60-m spatial resolution. The same similarity is observed with the [4] ( Figure 6, D-1) which is at the same spatial resolution as the Vgt DRC 2000 even though the latter assimilates secondary forest to a non-forest area, unlike the three other maps. ...
Article
National stratification maps are essential to improve forest management systems. For the Democratic Republic of the Congo, the existing maps derived from remote sensing techniques do not allow an optimal representation of the diverse land cover classes constituting the national stratification scheme. This situation is inherent to the cloud persistence, the seasonality effects and the spatial resolution of the input satellite imagery used that is not always adequate for the discrimination of certain land cover classes. This paper explores a cloud-based median luminance best pixel approach to obtain a cloud-free mosaic of optimal quality. The mosaic produced has necessitated nearly 2,500 Landsat scenes and a following object-based classification enabled the generation of a stratification map for the year 2000 according to the national stratification theme. A stratified random sampling approach that required 1,141 reference samples allowed estimating the map accuracy at 79.32%. Land cover classes areas computed using standard good practices recommendations to estimate land areas indicated that the dense moist forest area was about 158,810,975 ± 7,460,671 ha representing 68.40% ± 3.21% of the country area. Thanks to the free, user-friendly and cloud-based platforms for satellite images processing, the methodology implemented is easily replicable for other tropical countries.
... Tropical forests contain the most distinct and complex biome on Earth, with unique plant species of high economic value, and support habitat for many animal species. They provide numerous valuable ecosystem services while also aiding the mitigation of climate change [1][2][3][4]. Africa is home to some of the world's most magnificent tropical forests. With more more than 60 million people dwelling within or near these forests, they are relied upon for many ecosystem systems, with livelihoods dependent on them for providing food, medicinal plants, fuel, fibres, and non-timber forest products. ...
... The present analysis estimated a forest cover area of 407,976 km 2 , representing 45.76% of the country landmass ( Table 5). The forest type classification (Figure 5b) result indicated a prominent class of woodlands (closed, open woodland and thickets), estimated to cover approximately 336,405 km 2 , which make up 81.20% of the forested land (Table 8), an important ecosystem of great significance to human economies [1][2][3], mainly covering the central and western part of the country. The montane forests represent biodiversity hotspots along a chain of isolated mountain ranges (Figure 5b), supporting a diversity of endemic species [47], an area of approximately 9717 km 2 representing 2.35% of the forest cover (Table 8). ...
Article
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Tropical forests provide essential ecosystem services related to human livelihoods. However, the distribution and condition of tropical forests are under significant pressure, causing shrinkage and risking biodiversity loss across the tropics. Tanzania is currently undergoing significant forest cover changes, but monitoring is limited, in part due to a lack of remote sensing knowledge, tools and methods. This study has demonstrated a comprehensive approach to creating a national-scale forest monitoring system using Earth Observation data to inform decision making, policy formulation, and combat biodiversity loss. A systematically wall-to-wall forest baseline was created for 2018 through the application of Landsat 8 imagery. The classification was developed using the extreme gradient boosting (XGBoost) machine-learning algorithm, and achieved an accuracy of 89% and identified 45.76% of the country’s area to be covered with forest. Of those forested areas, 45% was found within nationally protected areas. Utilising an innovative methodology based on a forest habitat suitability analysis, the forest baseline was classified into forest types, with an overall accuracy of 85%. Woodlands (open and closed) were found to make up 79% of Tanzania’s forests. To map changes in forest extent, an automated system for downloading and processing of the Landsat imagery was used along with the XGBoost classifiers trained to define the national forest extent, where Landsat 8 scenes were individually downloaded and processed and the identified changes summarised on an annual basis. Forest loss identified for 2019 was found to be 157,204 hectares, with an overall accuracy of 82%. These forest losses within Tanzania have already triggered ecological problems and alterations in ecosystem types and species loss. Therefore, a forest monitoring system, such as the one presented in this study, will enhance conservation programmes and support efforts to save the last remnants of Tanzania’s pristine forests.
... 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]. ...
Article
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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.
... Moreover, despite the fact that agriculture is considered as the most vulnerable sector to climate change at both local and national levels (MECNT and UNDP, 2009;Ulimwengu and Kibonge, 2016); several studies have shown that it is a major source of greenhouse gas (GHG) emissions (Pan et al., 2011;Arneth et al., 2019), through the process of deforestation (Potapov et al., 2012;Hufkens et al., 2020), thus contributing to climate change (Kipalu and Mukungu, 2012;AMCEN, 2014). Slash-and-burn agriculture is the main direct cause of deforestation in DRC (MECNT, 2012;Molinario et al., 2015), particularly in the Yangambi landscape (Hufkens et al., 2020). ...
... These results are close to those found by Kyale et al. (2019) in the same region. Field sizes are close to the Tshopo Province average (FONAREDD, 2016) but small compared with the national average (Potapov et al., 2012). The small field area can be explained by the presence of the Yangambi Biosphere Reserve. ...
Article
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Purpose This paper aims to produce a framework for climate-smart agriculture (CSA) in the Yangambi landscape, Democratic Republic of the Congo (DRC). This would enable the authors to identify agricultural practices, assess vulnerability to climate change, identify options for improving agricultural systems from a climate change mitigation and adaptation perspective and finally provide climate-smart agricultural options. Design/methodology/approach The study used household survey methods of data collection. The data were collected using a structured questionnaire survey by interviewing 250 farm households, subdivided using three axes of the Yangambi landscape. Fisher’s exact test was used to determine relationships between two or more variables. Findings Results of the survey revealed that the vast majority (98%) of respondents perceived changes in temperature, rainfall and weather patterns. Reduction of crop yields and the emergence of new weed species and new crop pests are the main impacts on agricultural activities. Although 87.6% of respondents have no means of adaptation and resilience, some of them use crops rotation, fallow practice, fertilizers and bio-pesticides. A framework for CSA is proposed for the Yangambi landscape. Practical implications Policies and strategies to promote CSA in the study area should take into account local farmers' perceptions of climate change and consider first the adequacy of CSA practices for the specific conditions of the target area before its promotion. This study is thus useful for many REDD+ initiatives that are currently being promoted in DRC and particularly in the Tshopo Province. Originality/value This study is one of the first studies to focus on CSA in the Yangambi landscape, DRC. It assists the use of agriculture as a response to reducing deforestation while at the same time lowering agriculture’s carbon footprint and promoting a resilient and more productive farming system.
... In terms of crop classification, some early studies chose to fuse the images during the growth periods of crops [23]; later, the time-series data obtained during crop growth were found to be able to fully reflect the phenological features of different crops and their changes in different physical and chemical parameters and indicators [24], and also to contribute greatly to improving accuracy in crop classification [25]. It is therefore popular for research [26][27][28] to use the data fusion method of calculating the median reflectance or index median of time-series images [29]. ...
... The IO-Growth method for extracting the spatial distribution of crops proposed in the study uses a 10-day interval to represent the growth stages of the crops, which is taken as an independent attribute, and carries out image synthesis on every growth stage [29] to thereby extract the spectral features of each stage. Through feature selection, the spectral features with the growth stage attributes are optimized and the feature combinations that contribute more greatly to the classification are obtained [38], which plays a role in refining the effective interval of the feature wavebands within the growth period. ...
Article
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In recent years, the scale of rural land transfer has gradually expanded, and the phenomenon of non-grain-oriented cultivated land has emerged. Obtaining crop planting information is of the utmost importance to guaranteeing national food security; however, the acquisition of the spatial distribution of crops in large-scale areas often has the disadvantages of excessive calculation and low accuracy. Therefore, the IO-Growth method, which takes the growth stage every 10 days as the index and combines the spectral features of crops to refine the effective interval of conventional wavebands for object-oriented classification, was proposed. The results were as follows: (1) the IO-Growth method obtained classification results with an overall accuracy and F1 score of 0.92, and both values increased by 6.98% compared to the method applied without growth stages; (2) the IO-Growth method reduced 288 features to only 5 features, namely Sentinel-2: Red Edge1, normalized difference vegetation index, Red, short-wave infrared2, and Aerosols, on the 261st to 270th days, which greatly improved the utilization rate of the wavebands; (3) the rise of geographic data processing platforms makes it simple to complete computations with massive data in a short time. The results showed that the IO-Growth method is suitable for large-scale vegetation mapping.
... We used the C1 Quality Assessment (QA) layer to identify all pixels flagged as cloud, cloud shadow, snow, ice, or fill-value in each image (USGS EROS Center, 2021). Scene-wise reflectance variations (bi-directional reflectance distribution function (BRDF) effects) arise from atmospheric conditions and differences across the Landsat track (Toivonen et al., 2006;Hansen et al., 2012;Potapov et al., 2012;Flood et al., 2013). To support corrections for BRDF effects, we created pixel-level sensor and solar azimuth and zenith angle layers using the USGS-provided C1 Angle Coefficient file and the Landsat Angles Creation tool (USGS EROS Center, 2019). ...
... We used the C1 Quality Assessment (QA) layer to identify all pixels flagged as cloud, cloud shadow, snow, ice, or fill-value in each image (USGS EROS Center, 2021). Scene-wise reflectance variations (bi-directional reflectance distribution function (BRDF) effects) arise from atmospheric conditions and differences across the Landsat track (Toivonen et al., 2006;Hansen et al., 2012;Potapov et al., 2012;Flood et al., 2013). To support corrections for BRDF effects, we created pixel-level sensor and solar azimuth and zenith angle layers using the USGS-provided C1 Angle Coefficient file and the Landsat Angles Creation tool (USGS EROS Center, 2019). ...
Preprint
Time series reconstruction methods---used to generate gap-free time series of satellite observations---were historically designed for sensors with frequent image acquisitions. Since 2008, interest in leveraging time series methods has shifted from sensors such as AVHRR and MODIS to Landsat because of free, higher-resolution data availability and improved access to high-performance compute systems. Existing methods are typically designed for specific applications such as land cover classification or for estimating the timing of phenology events.Moreover, approaches developed for specific ecological systems, such as tropical forests or temperate agriculture, often do not generalize well across land cover, vegetation, and climate types. In this study, we introduce a dynamic temporal smoothing (DTS) method to reconstruct sparse, noisy signals into dense time series at regular intervals. The DTS is a weighted smoother with dynamic parameters that is applied over a signal. The smoother is intended to have wide applicability, with particular focus on applications in vegetation remote sensing. In this paper we present and illustrate the DTS over short- and long-term Landsat (TM, ETM+, and OLI) time series and demonstrate the effectiveness of robust gap-filling over a range of landscapes in the South American Southern Cone region.
... Second, long-term recording of satellite observations allows the quantification of forest cover trends over several decades (Cheng & Wang, 2019;Hermosilla et al., 2019;Ho sciło & Lewandowska, 2019;Qin et al., 2019;Vogeler et al., 2018). Besides, new research impulses have arisen, especially after the emergence of remote sensing applications based on cloud computing platforms (Gasparini et al., 2019;Potapov et al., 2012;Praticò et al., 2021). ...
Article
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With the aim to develop a landscape dynamics framework for environmental planning and management and testing the effectiveness of protected areas in achieving the 2030 Agenda of United Nations sustainability goals, we characterised the historical transformation trajectories of forest area changes from 1936 to 2010 in the Metropolitan City of Rome Capital (Italy). Remote sensing-based products coupled with landscape pattern metrics and fragmentation analysis have been implemented, comparing different historical forest maps. Results show a remarkable forest area gain - from 17.6% to 25.5% – thanks to 68,299 ha of recently established forest. Statistical descriptors showed that the highest relative gain occurred in mountain zones, confirming a wide European forest recovery pattern in marginal areas from past deforestation and overexploitation. Deforestation mainly occurred in the flat and hilly areas where almost 26,000 ha of forests were lost since 1936. In summary, two main forest landscape dynamics were reconstructed: I) the increase of forest cover fragmentation in the lowland areas; (II) the rise in forest area in the interior sectors of the mountain landscape, mainly within protected areas. Restoring the forest ecosystem’s bioecological integrity has been highlighted as an urgent action for biodiversity conservation and carbon mitigation. In lowland areas, the study revealed the urgent need to establish new protected areas and rewilding spaces as landscape metrics are relatively below the sustainability targets for healthy forest ecosystems. The proposed framework can be used for testing the effectiveness of environmental planning and management in other forest landscapes to achieve the Agenda 2030 goals.
... Forest degradation affecting individual trees can be challenging to identify because they impact portions of Landsat pixels, and the spectral changes they cause are small relative to other sources of background variation and noise (Negrón-Juárez et al. 2011). Persistent cloud cover also limits the frequency and quality of observation in most tropical forest ecosystems (Potapov et al. 2012;Sannier et al. 2014). Newer satellite missions such as Sentinel-1 and 2 offer advantages such as 10-20 m spatial resolutions (versus 30 m for Landsat), and synthetic aperture radar that can penetrate clouds (Erinjery, Singh, and Kent 2018;Reiche et al. 2018), but lack the long-term record of Landsat. ...
Article
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The Upper Guinean Forest region of West Africa, a globally significant biodiversity hotspot, is among the driest and most human-impacted tropical ecosystems. We used Landsat to study forest degradation, loss, and recovery in the forest reserves of Ghana from 2003 to 2019. Annual canopy cover maps were generated using random forests and results were temporally segmented using the LandTrendr algorithm. Canopy cover was predicted with a predicted-observed r 2 of 0.76, mean absolute error of 12.8%, and mean error of 1.3%. Forest degradation, loss, and recovery were identified as transitions between closed (>60% cover), open (15-60% cover) and low tree cover (< 15% cover) classes. Change was relatively slow from 2003 to 2015, but there was more disturbance than recovery resulting in a gradual decline in closed canopy forests. In 2016, widespread fires associated with El Niño drought caused forest loss and degradation across more than 12% of the moist semi-deciduous and upland evergreen forest types. The workflow was implemented in Google Earth Engine, allowing stakeholders to visualize the results and download summaries. Information about historical disturbances will help to prioritize locations for future studies and target forest protection and restoration activities aimed at increasing resilience. ARTICLE HISTORY
... 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). ...
Article
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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.
... Currently, lowland farming practices such as open-field burning of crop straws (Roos, 2018), slash-and-burn practices in the uplands (Li et al., 2019), illegal logging (Bowd, 2019) in the tropical zone, and lightning-induced burning of boreal forests (Mollicone, 2006;Potapov, 2012) in the extra-tropics may be closely associated with the variability of elevation of active fires. It is thus believed that the upward elevational trends of active fires may have a close connection with upland agriculture as a result of slash-and-burn activities and deforestation-related fires which are still common in vast areas of the tropics Nikonovas et al., 2020;Reddington et al., 2015). ...
Article
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As an inherent element of the Earth’s ecosystem, forest and vegetation fires are one of the key contributors to and direct consequences of climate change. Given that topography is one of the key drivers of forest landscapes and fire behavior, it is important to clarify what the topographical characteristics and trends of global fire events are, particularly in the tropics. Here, we have investigated the variations in elevation of active fires at the continental to global scale, including the tropics, the extra-tropics, the lowlands and the highlands (greater than 200 m above sea level (asl)), using the available MODIS Collection 6 active fire products (2001-2019). The main conclusions are: (1) the annual totality (average of 4.5 million) of global active fire events decreased and over 97% of them occurred frequently below 1500 m asl. (2) The tropics and the highlands accounted for ~74% (±3%) and 71% (±2%) of global active fires, respectively, and 77% (±2%) were observed in the tropical highlands. (3) From the beginning of the 21st century, active fires in the highlands displayed an upward elevational trend, particularly in the tropics, while the opposite trend was observed for the lowlands. More importantly, the rate of the increasing elevation in the highlands had a greater magnitude than that of decreasing elevation in the lowlands. (4) Finally, the United Nations collaborative programme on Reducing Emissions from Deforestation and Forest Degradation (UN-REDD) in Developing Countries seemed to slow down or even result in a reversal of the upward elevational trend of fire occurrences in the tropics for the partner countries, especially in the lowlands. In the context of global climate change and rampant fires, the trend of rising elevation for active fire occurrences, particularly in the tropical highlands, indicates that more vegetation burning events occur or will occur in hilly to mountainous areas, thus posing further threats to tropical forests and some important biodiversity refuges. More sustained efforts should be made by governments and the scientific community to instigate enhanced fire management practices and to conduct in-depth research programs.
... GCFP available in GEE contains annual information on global forest change at 30-m resolution from 2000 and 2020 (Hansen et al., 2013). GCFP was produced using supervised expert-driven classification, based on time series Landsat mission imagery (Hansen et al., 2013;Potapov et al., 2012). To qualitatively compare its performance with our SPB infestation detection result, we directly extracted annual forest loss information from GCFP for our study area and period on GEE. ...
Article
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The recent northward expansion of Southern Pine Beetle (SPB) outbreaks associated with warming winters has caused extensive tree mortality in temperate pine forests, significantly affecting forest dynamics, structure, and functioning. Spatially-explicit early warning and detection of SPB-induced tree mortality is critical for timely and sustainable forest management practices. The unique contributions of remote sensing technologies to mapping the location, extent, and severity of beetle outbreaks, as well as assisting in analyzing the potential drivers for outbreak predictions, have been well recognized. However, little is known about the performance of moderate resolution satellite multispectral imagery for early warning and detection of SPB-induced tree mortality. Thus, we conducted this study, as the first attempt, to capture the spatial-temporal patterns of SPB infestation severity at the regional scale and to understand the underlying environmental drivers in a spatially-explicit manner. First, we explored the spectral signatures of SPB-killed trees based on 30-m plot measurements and Landsat-8 imagery. Then, to improve detection accuracy for areas with low-moderate SPB infestation severity, we added spectral-temporal anomaly information in the form of a linear trend of the spectral index trajectory to a previously developed approach. The best overall accuracy increased from 84.7% to 90.1% and the best Macro F1 value increased from 0.832 to 0.900. Next, we compared the performances of spectral indices in mapping SPB infestation severity (i.e., % red stage within the 30-m grid cell). The results showed that the combination of Normalized Difference Moisture Index and Tasseled Cap Greenness had the best performance for mapping SPB infestation severity (2016: R2 = 0.754; RSME = 15.7; 2017: R2 = 0.787; RSME = 12.4). Finally, we found that climatic and landscape variables can explain the detected patterns of SPB infestation from 2014 to 2017 in our study area (R2 = 0.751; RSME = 9.67), providing valuable insights on possible predictors for early warning of SPB infestation. Specifically, in our study area, winter dew point temperature was found to be one of the most important predictors, followed by SPB infestation locations in the previous year, canopy cover of host species, elevation, and slope. In the context of continued global warming, our study not only provides a novel framework for efficient, spatially-explicit, and quantitative measurements of forest damage induced by SPB infestation over large scales, but also uncovers opportunities to predict future SPB outbreaks and take precautions against it.
... This technology has been proved to be an indispensable tool, because it covers a wide area, provides spatial information and repeated coverage of large areas and reduces the cost and time for data acquisition. Thus, remotely sensed data, especially Landsat images, have been successfully used to model land-use/land-cover change [27][28][29][30][31][32] and assess its impact on the variation of LST [17,33]. Although numerous studies have been conducted across the world, the impact of land-use/land-cover change on the land-surface-temperature variation in the villages within the Luki Biosphere Reserve is yet to be investigated. ...
Article
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Major land-use/land-cover change due to rapid urbanization has been known to increase the land-surface temperature around the world. Consequently, examining the variation of land-surface temperatures and mitigating the related impacts remain a challenge. The present study employed remote-sensing and geoinformational techniques to examine land-use/land-cover change and its effects on land-surface temperature variations in the villages within the Luki Biosphere Reserve, Democratic Republic of Congo. Land-use/land-cover change for the year 2038 was predicted by using the CA–Markov chain. Additionally, focus-group discussions (FGDs) with local communities from different villages were applied to better understand the impact of climate change, considering the increase of land-surface temperature. The results revealed major changes in land-use/land-cover in the four villages from 2002 to 2020, principally the expansion of fallow land and built-up areas, as well as the decline in forest land, and the complex of young secondary and degraded forest. There was an increase in mean LST values over all villages between 2002 and 2020. The highest value was observed in Tsumba kituti (25.12 °C), followed by Kisavu (24.87 °C), Kibuya (23.31 °C) and Kiobo (21.82 °C). Between 2002 and 2020, the mean LST of built-up areas increased from 23.18 to 25.12 °C, 21.55 to 23.38 °C, 21.4 to 25.78 °C and 22.31 to 25.62 °C in Tsumba kituti, Kiobo, Kisavu and Kibuya, respectively. Moreover, the mean LST of fallow land increased from 20.8 to 23.2 °C, 21.13 to 22.12 °C, 21.89 to 23.12 °C and 20.31 to 23.47 °C in Tsumba, Kiobo, Kibuya and Kisavu, respectively. This indicates that built-up and fallow land experienced the highest land-surface temperature compared to other land-use/land-cover categories. Meanwhile, the conversion of all land-use/land-cover categories into built-up areas in all the villages resulted in the increase of the land-surface temperature. FGDs results recognize the recurrent land-use/land-cover change as the major driver of the increase in LST (86%). However, it was predicted that farmland and built-up area will still increase within all the villages, while the forest land will decline. As for the complex of secondary and degraded forest, it will decrease in Tsumba kituti, while, in Kiobo and Kisavu, it is expected to increase. Through a combination of remote-sensing and primary data, this study provides accurate information that will benefit decision-makers to implement appropriate landscape-planning techniques to mitigate the effect of the increased land-surface temperature in the villages.
... Recently, numerous algorithms for Landsat time series change detection have been proposed, reviewed and widely used (Banskota et al. 2014Cohen et al. 2017;Huang et al. 2009;Kennedy et al. 2010;Zhu 2017, Zhu et al. 2020. A wealth of forest cover change products from local to global scales have been generated from a medium-or high-frequency Landsat time series using these algorithms (Cohen et al. 2016;Czerwinski et al. 2014;Hansen et al. 2016;Margono et al. 2012;Masek et al. 2008;Potapov et al. 2012;Schroeder et al. 2007Schroeder et al. , 2011White et al. 2017). However, these products are either region-specific datasets with high map accuracy or global datasets with high regional uncertainty in map accuracy. ...
Article
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Background Natural forests in the Hengduan Mountains Region (HDMR) have pivotal ecological functions and provide diverse ecosystem services. Capturing long-term forest disturbance and drivers at a regional scale is crucial for sustainable forest management and biodiversity conservation. Methods We used 30-m resolution Landsat time series images and the LandTrendr algorithm on the Google Earth Engine cloud platform to map forest disturbances at an annual time scale between 1990 and 2020 and attributed causal agents of forest disturbance, including fire, logging, road construction and insects, using disturbance properties and spectral and topographic variables in the random forest model. Results The conventional and area-adjusted overall accuracies (OAs) of the forest disturbance map were 92.3% and 97.70% ± 0.06%, respectively, and the OA of mapping disturbance agents was 85.80%. The estimated disturbed forest area totalled 3313.13 km ² (approximately 2.31% of the total forest area in 1990) from 1990 to 2020, with considerable interannual fluctuations and significant regional differences. The predominant disturbance agent was fire, which comprised approximately 83.33% of the forest area disturbance, followed by logging (12.2%), insects (2.4%) and road construction (2.0%). Massive forest disturbances occurred mainly before 2000, and the post-2000 annual disturbance area significantly dropped by 55% compared with the pre-2000 value. Conclusions This study provided spatially explicit and retrospective information on annual forest disturbance and associated agents in the HDMR. The findings suggest that China’s logging bans in natural forests combined with other forest sustainability programmes have effectively curbed forest disturbances in the HDMR, which has implications for enhancing future forest management and biodiversity conservation.
... As in Sierra Leone, this is linked to the massive migration due to the civil war, and over population in some forest areas. Similar research has been done for the period 2000 -2010, with a more detailed analysis (Potapov et al., 2012). Although the overall annual forest loss was not intensive, the difference was clear in some districts. ...
Article
The negative impact humans have on the environment directly affects the environmental security. Over the years, it has been proven that wars make drastic and sometimes unrecoverable environmental damage. Remote sensing has played a main role in providing necessary data for spatio-temporal analysis. This paper reviews remote sensing implementation of war activities for environmental monitoring. This review is timely due to the exponentially increasing number of works published in recent years. The paper's main objective is to locate the papers and find geographic link, sensor use, and environmental degradation type. Following a discussion of remote sensing's capabilities, this overview illustrates numerous environmental damages caused by military operations in various world places. The study found that wars has a detrimental influence on the ecosystem across the world, with major reasons being forest loss, oil spills, and urban growth. According to the findings, remote sensing, particularly middle-resolution satellite images, is extensively and successfully employed for environmental security monitoring. The rehabilitation of a deteriorated environment should be one of the key areas of future study.
... -La forêt claire de type miombo, le « miombo woodland » des auteurs anglo-saxons, est un type de végétation largement distribué en Afrique zambézienne où il fournit des produits forestiers, ligneux ou non, à des millions d'habitants. En RD Congo, il couvre près de 23 % de la surface forestière totale et reste le type de forêt le plus dominant (>50 %) dans l'ex-province du Katanga (Kabulu et al., 2008;Potapov et al., 2012). Autour de la ville de Lubumbashi, les causes de la régression de sa couverture, soutenues par la croissance démographique rapide, sont principalement : (i) le développement agricole, (ii) la production de charbon de bois, (iii) l'expansion de la ville et (iv) les activités minières. ...
Conference Paper
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La forêt claire de type miombo, le « miombo woodland » des auteurs anglo-saxons, est un type de végétation largement distribué en Afrique zambézienne où il fournit des produits forestiers, ligneux ou non, à des millions d'habitants. En RD Congo, il couvre près de 23 % de la surface forestière totale et reste le type de forêt le plus dominant (>50 %) dans l'ex-province du Katanga (Kabulu et al., 2008; Potapov et al., 2012). Autour de la ville de Lubumbashi, les causes de la régression de sa couverture, soutenues par la croissance démographique rapide, sont principalement : (i) le développement agricole, (ii) la production de charbon de bois, (iii) l'expansion de la ville et (iv) les activités minières. Les perturbations engendrées par cette déforestation seraient responsables du raccourcissement de la durée des pluies (Sanga-Ngoie & Fukuyama, 1996; Assani, 1999), d'une malnutrition persistante dans le milieu rural (Malaisse, 1997) et de la perte de biodiversité (Barima et al., 2011; Vranken et al., 2011). L'ampleur inquiétante de ces conséquences a conduit plusieurs chercheurs à quantifier la déforestation autour de Lubumbashi à travers le concept de «rayon de déforestation». Nous présentons une méta-analyse des études ayant circonscrit le rayon de déforestation autour de Lubumbashi. Ce rayon, utilisé à la fois pour exprimer la superficie (zone circulaire), l’intensité et l'ampleur (distance à la ville) de la déforestation, a été déterminé à travers les observations de la production de charbon de bois in situ et la télédétection. Les observations effectuées dans les villages des producteurs de charbon de bois expriment le rayon de déforestation à travers la distance qui les sépare de la ville, ce qui reflète plutôt l'ampleur de la déforestation. Ces estimations de distances, qui n'augmentent pas nécessairement avec le temps comme attendu par ailleurs, varient selon les auteurs, les années d'observation et les distances des villages visités par rapport à la ville; en plus, souvent elles ne considèrent pas les taches de miombo peu accessibles situées entre les villages à proximité de Lubumbashi. Les études in situ ignorent les taches de miombo proches de la ville, et semblent donc surévaluer l'ampleur de la déforestation. A partir de cette approche, des projections de la suppression complète du miombo ont été réalisées (Assani, 1999). Force est de constater que les difficultés d'accès et la privatisation de certaines concessions font que des taches de miombo subsistent sur des courtes distances à la ville et le seront jusqu'à l'horizon 2050 (Vranken et al., 2011). Par contre, les études basées sur la télédétection surestiment parfois ce rayon, mais aussi la résistance des taches de miombo, en ignorant leur taux de dégradation, sur des courtes distances à la ville. Malaisse & Binzangi (1985) ont considéré que les taches de miombo qui subsistent sur des courtes distances à la ville, et identifiées par télédétection comme telles, correspondent plutôt aux savanes secondaires que Kabulu et al. (2008) ont identifié comme des complexes de forêt claire et savanes boisées. Il en résulte que ces deux approches ne sont pas cohérentes ou compatibles dans l'étude de l'importance de la déforestation autour de la ville de Lubumbashi en raison de la variabilité des protocoles méthodologiques au sein de chaque approche, mais aussi entre les approches. Ces observations empêchent le développement d'une politique appropriée de conservation et d'exploitation durable de l'écosystème en question. Par conséquent, une harmonisation des approches utilisées en termes méthodologique et conceptuel s'impose. Elle pourrait former le point départ d'une relecture rétrospective critique des estimations historiques de la déforestation et de la dégradation du miombo, afin d'interpréter correctement les dynamiques spatio-temporelles de cet écosystème unique et crucial pour la population katangaise.
... However, in 2017, people mentioned that they were still being bitten by blackflies around the Kakoi river at lower altitude and during the cold season or in rainy and foggy weather [42]. A similar level of deforestation (up to 6.8% between 2000 and 2020) has also been noted in many other parts of the DRC [43,44]. Another explanation of the low O. volvulus transmission in the Logo health zone in DRC could be the restrictions of movements to crop fields far from houses due to the conflicts and insecurity that have increased since 2017 [45]. ...
Article
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To eliminate onchocerciasis-associated morbidity, it is important to identify areas where there is still high ongoing Onchocerca volvulus transmission. Between 2015 and 2021, door-to-door surveys were conducted in onchocerciasis-endemic villages in Cameroon, the Democratic Republic of Congo (DRC), Nigeria, South Sudan, and Tanzania to determine epilepsy prevalence and incidence, type of epilepsy and ivermectin therapeutic coverage. Moreover, children aged between six and 10 years were tested for anti-Onchocerca antibodies using the Ov16 IgG4 rapid diagnostic test (RDT). A mixed-effect binary logistic regression analysis was used to assess significantly associated variables of Ov16 antibody seroprevalence. A high prevalence and incidence of epilepsy was found to be associated with a high Ov16 antibody seroprevalence among 6–10-year-old children, except in the Logo health zone, DRC. The low Ov16 antibody seroprevalence among young children in the Logo health zone, despite a high prevalence of epilepsy, may be explained by a recent decrease in O. volvulus transmission because of a decline in the Simulium vector population as a result of deforestation. In the Central African Republic, a new focus of O. volvulus transmission was detected based on the high Ov16 IgG4 seropositivity among children and the detecting of nodding syndrome cases, a phenotypic form of onchocerciasis-associated epilepsy (OAE). In conclusion, Ov16 IgG4 RDT testing of 6–10-year-old children is a cheap and rapid method to determine the level of ongoing O. volvulus transmission and to assess, together with surveillance for OAE, the performance of onchocerciasis elimination programs.
... Sentinel-2). However, as obtaining cloud-free information was still challenging in Ecuador and Philippines, we created multi-temporal seasonal mosaics, similarly to previous approaches (Hansen et al., 2013;Potapov et al., 2012). ...
Article
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Tropical forests represent half of the Earth's remaining forest area, but they are shrinking at high rates, which poses a threat to their multiple ecosystem services. As a response, international environmental agreements and related programs require information about tropical forested landscapes. Despite the increasing quantity and quality of remote sensing-based data, the effective monitoring of forests in the tropics still faces operational challenges: (a) applicability at local levels, with lack of reference or cloud-free information; (b) overcoming geographical, ecological, or biophysical variability; (c): stratification, distinguishing forest categories related to functionality and disturbance history. We conducted an extensive ground verification campaign through 36 landscapes in 9 regions of Zambia, Ecuador and Philippines, which constitute a gradient of pantropical deforestation contexts or forest transitions. We collected over 16,000 ground control points and digitized over 18,000 ha with details on land use and forest disturbance history. We trained a random forest algorithm and generated high-resolution (30 m) binary forest maps covering ~15 Mha, building on 39 optical (Landsat-8), radar (Sentinel-1) and elevation bands, indices and textures. We validated the quality of the outputs across the studied deforestation gradient and compared them to (a): 3 national land cover maps used for international reporting, (b): 4 global forest datasets (Global Forest Change, Copernicus Land Cover, JAXA and TanDEM-X Forest/Non-Forest). Our method generated highly accurate (92%) forest maps for the studied regions when compared to the global datasets, which generally overestimated forest cover. We achieved accuracies similar to the national maps, following a standardized method for all countries. The difficulties in delineating forest increased in more advanced stages of deforestation, with recurring struggles to distinguish non-forest tree-based systems (e.g. perennials, palms, or agroforestry), shrublands and grasslands. Regrowth forests were repeatedly misclassified across contexts, countries and datasets, in contrast to reference or degraded forests. Our results highlight the importance of in situ verification as accompanying method to establish efficient forest monitoring systems, especially in areas with higher rates of forest cover change and in tropical regions of advanced deforestation or early reforestation stages. These are precisely the areas where current REDD+ or Forest Landscape Restoration initiatives take place.
... Agriculture is the largest sector in economy with 10 million ha cultivated (FAO 2013). D.R. Congo comprises 18% of the world's tropical forests, but the Congo Basin is subjected to steadily increasing human in uence due to deforestation and urbanisation (Anonymous 2012;Potapov et al. 2013), which could favour the expansion of non-native species ...
Preprint
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The Democratic Republic of the Congo (D.R. Congo) represents a striking gap of knowledge on alien plant species. In this paper, we use digitised herbarium collections to assemble a checklist of alien plant species in D.R. Congo and to examine patterns in the alien flora. The new checklist comprises 426 alien species i.e., 182 (42.5%) casuals, 244 (57.5%) naturalised of which 80 (19% of aliens) are invasive. Discrepancies with previous databases are discussed. For many species in previous databases, we failed to find evidence for occurrence outside cultivation. A total of 158 taxa were not included in previous lists, 44 of which are new to D.R. Congo. Considering the size of the country and its rich native flora, the alien flora of D.R. Congo does not appear to be species rich. The alien flora is particularly rich in Fabaceae (15%) and in annual species (36%). America is by far the most important source continent (65%) and the proportion of annuals of American origin is particularly large among the most widespread species. Invasive success is discussed in terms of residence time. The very low number of new species records after 1960 is most likely accounted for by decreasing sampling effort. The results illustrate how herbarium collections can be used to critically revise existing checklists of alien species in tropical Africa. Field work is urgently needed to improve coverage of recent introductions and to monitor the status of alien species, especially in protected areas and around botanic gardens.
... 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]. ...
Article
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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). ...
Thesis
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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. ...
Article
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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. ...
Article
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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. ...
Article
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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.
... In addition, lack of effective policies on land use in the DRC has resulted in deforestation and degradation of forest land. Thus, the DRC experiences tremendous losses of forest area each year due to those various anthropogenic pressures [12]. The annual rate of deforestation varies from 0.18 to 0.46%, depending on the estimation methods used, types of vegetation under consideration, sites, and period under study [13][14][15][16]. ...
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Villages within the Luki Biosphere Reserve and the surrounding cities have undergone rapid demographic growth and urbanization that have impacted the reserve’s natural landscape. However, no study has focused on the spatiotemporal analysis of its land use/land cover. The present research aims at providing a comprehensive analysis of land use/land cover change in the Luki Biosphere Reserve from the year 1987 to 2020, and to predict its future change for the year 2038. Landsat images were classified in order to provide land use/land cover maps for the years 1987, 2002, 2017 and 2020. Based on these maps, change detection, gradient direction, and landscape metric analyses were performed. In addition, land use/land cover change prediction was carried out using the Multilayer Perceptron Markov model. The results revealed significant land use/land cover changes in the Luki Biosphere Reserve during the study period. Indeed, tremendous changes in the primary forest, which lost around 17.8% of its total area, were noted. Other classes, notably savannah, secondary forest, built-up area, fallow land and fields had gained 79.35, 1150.36, 67.63, 3852.12 hectares, respectively. Based on the landscape metric analysis, it was revealed that built-up areas and fallow land and fields experienced an aggregation trend, while other classes showed disaggregation and fragmentation trends. Analysis further revealed that village expansion has significantly affected the process of land use/land cover change in the Luki Biosphere Reserve. However, the prediction results revealed that the primary forest will continue to increase while built-up area, fallow land and fields will follow a trend similar to a previous one. As for secondary forest and savannah, the forecast revealed a decrease of the extent during the period extending from 2020 to 2038. The present findings will benefit the decision makers, particularly in the sustainable natural resources management of the Luki Biosphere Reserve.
... 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. ...
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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. ...
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... 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. ...
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... Clearing for the installation of new fields is best done in the forest for several reasons: (i) drop in yield in fallows due to soil impoverishment after 1 to 2 crop cycles, (ii) arduous work (ploughing, weeding in particular) in fallows, (iii) availability and easy access to land in forest areas, (iv) internal competition between clan members for the securing of land to descendants or sharecropping, through the 'right of axe', (v) lack of viable alternatives to slaughter-burning techniques, as well as various socio-cultural aspects perpetuating these practices. The factors favouring the expansion of slash-and-burn slaughter are (i) the absence of well-structured agricultural sectors, which benefit small producers, (ii) the absence of economic alternatives and (iii) the lack of support from the technical services of agriculture (FONAREDD 2016).From a public administration perspective, deforestation and degradation hotspots in Tshopo province are concentrated mainly along practicable roads (roads, Congo River, rivers) and around major urban areas, the Isangi territory and the entire north-eastern part of the province(MECNT 2011, Potapov et al. 2012. ...
... This is another reason the percentile composite was chosen in this study. Currently, the percentile composite is widely used in the classification of forest vegetation [64], rice [31], maize [19], crops [15], and land cover [65]. For the Sentinel-2 data, 90 metrics were generated from 9 reflectance bands (B2, B3, B4, B5, B6, B7, B8, B11, and B12) and 9 vegetation indices (NDVI, NDWI, LSWI, GCVI, RD-NDVI1, RDNDVI2, RDGCVI1, RDGCVI2, and EVI) by the percentile composite, with the percentiles set at 5%, 25%, 50%, 75%, and 95%. ...
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Crop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classification model to retrace the historical spatial distribution of crop types? Taking the Hetao Irrigation District (HID) in China as the study area, this study first designed a 10 m crop type classification framework based on the Google Earth Engine (GEE) for crop type mapping in the current season. Then, its interannual transferability to accurately retrace historical crop distributions was tested. The framework used Sentinel-1/2 data as the satellite data source, combined percentile, and monthly composite approaches to generate classification metrics and employed a random forest classifier with 300 trees for crop classification. Based on the proposed framework, this study first developed a 10 m crop type map of the HID for 2020 with an overall accuracy (OA) of 0.89 and then obtained a 10 m crop type map of the HID for 2019 with an OA of 0.92 by transferring the trained model for 2020 without crop reference samples. The results indicated that the designed framework could effectively identify HID crop types and have good transferability to obtain historical crop type data with acceptable accuracy. Our results found that SWIR1, Green, and Red Edge2 were the top three reflectance bands for crop classification. The land surface water index (LSWI), normalized difference water index (NDWI), and enhanced vegetation index (EVI) were the top three vegetation indices for crop classification. April to August was the most suitable time window for crop type classification in the HID. Sentinel-1 information played a positive role in the interannual transfer of the trained model, increasing the OA from 90.73% with Sentinel 2 alone to 91.58% with Sentinel-1 and Sentinel-2 together.
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The characterization of forest harvesting and subsequent vegetation recovery provides valuable insights for effective forest management. Although Landsat time series data offers spatially explicit information regarding large-area forest disturbance and recovery, the detailed characterization of country-wide harvest and post-harvest recovery is insufficient. Despite the importance of planting in harvest areas, the mapping of areas replanted by forest management after harvest are not usually considered. This study investigated an approach to detect harvest and other forest disturbance areas country-wide and to characterize post-harvest recovery using annual Landsat time series data from 1984 to 2020. To do so, a random forest algorithm was used to classify disturbance agents and stable land cover classes using predictor variables derived from the LandTrendr temporal segmentation of five spectral indices and topographic and climate data. Post-harvest recovery was characterized as forest species composition (i.e., coniferous/broadleaved forests) and then used to link replanted areas in harvest areas. The disturbance agents/stable land cover classification achieved producer’s and user’s accuracies of 80.1% (±4.8%) and 93.8% (±3.8%), respectively, for the forest harvest class. The overall accuracy of post-harvest recovery was high (83.9%) and a comparison with statistical data of replanted areas for entire Japan showed good agreement in trends and estimated area with a root mean square error of 2687.8 ha (15.2%). Overall, harvested forest area accounted for 4.6% of the total land area in Japan during the past 35 years. The results indicated that approximately 60.0% of coniferous plantation forests were recovered as coniferous forests. The spatial and temporal distribution of coniferous forests after harvest presumably represented the replanting activity of forest management in harvest areas. The approach presented in this study has the potential to provide valuable information on country-wide replantation activity in harvest areas using Landsat time series data.
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The prevention of tropical forest deforestation is essential for mitigating climate change. We tested the machine learning algorithm Maxent to predict deforestation across the Peruvian Amazon. We used official annual 2001–2019 deforestation data to develop a predictive model and to test the model’s accuracy using near-real-time forest loss data for 2020. Distance from agricultural land and distance from roads were the predictor variables that contributed most to the final model, indicating that a narrower set of variables contribute nearly 80% of the information necessary for prediction at scale. The permutation importance indicating variable information not present in the other variables was also highest for distance from agricultural land and distance from roads, at 40.5% and 14.3%, respectively. The predictive model registered 73.2% of the 2020 early alerts in a high or very high risk category; less than 1% of forest cover in national protected areas were registered as very high risk, but buffer zones were far more vulnerable, with 15% of forest cover being in this category. To our knowledge, this is the first study to use 19 years of annual data for deforestation risk. The open-source machine learning method could be applied to other forest regions, at scale, to improve strategies for reducing future deforestation.
Chapter
In the history and civilization of mankind, shifting cultivation is being regarded as the oldest method of agriculture. Also this agricultural method is termed as “slash and burn agriculture,” “swidden agriculture,” “land-rotation cultivation,” “field-forest rotation agriculture,” “digging stick cultivation,” etc., and in North East India as “Jhum.” The practice is most prevalent in the hilly areas of tropical and subtropical regions of the globe. Notably, this oldest agriculture practice has practitioners in large scale till today in the hilly areas of North East India. This study is an attempt to understand the spatial and temporal dynamics of shifting cultivation in Anjaw district of Arunachal Pradesh using LISS IV and Landsat time-series from 2003 to 2017. Complete shifting cycles were extracted from the sequence of use and fallow periods and vulnerability codes were assigned accordingly. Geodatabase was generated by considering image of 2003 as the base year and the same geodatabase was used to digitize the shifting cultivation areas from the rest of the images. Analysis of shifting cultivation patches showed an increasing and decreasing trend in the occurrence of shifting cultivation practices across 2003–17. The increasing trend during the study period has been observed with the slight dip in 2004, 2005, 2008, and 2015 and some major dips in 2010 and 2014 in shifting cultivation practices. Vulnerability class was assigned to each of these shifting cultivation patches based on threshold level given for the rotation cycles (fallow periods). Vulnerability class analysis revealed that most of the completed shifting cultivation cycles are extremely vulnerable in nature which accounts for almost 40.07% of the completed shifting cultivation patches, whereas only about 13.16% of completed shifting cultivation patches falls under the nonvulnerable status.
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The Ministry of Environment and Forestry (MoEF) of the Republic of Indonesia selected East Kalimantan as a pilot area for trialing a national REDD+ program under Forest Carbon Partnership Facility (FCPF). If it works, this pilot area will receive the payment of forest carbon services. Thus, East Kalimantan provincial government established the Green East Kalimantan strategy in 2010 to improve the natural resources and Green Growth Compact in 2016 to reduce carbon emission and increase economic growth by 8%. This paper estimated greenhouse gas (GHG) emissions before and after the REDD+ commitment implemented in the study area. The annual land cover map from 2000 until 2016 derived from satellite remote sensing data was used in this analysis. From this spatial data set, the change in carbon stock for each period was estimated using the IPCC guideline for national GHG inventories. The negative stock difference represents sequestration, while the positive stock defines emission. The result stated that deforestation and forest degradation contribute 80% and 20% of GHG emissions in the study area. During the study period, GHG emissions increased by 31 Mt CO 2 with an increment rate of 2.1 Mt CO 2 yr–1. Furthermore, the increment rate before REDD+ commitment was larger (2.3 Mt CO2 yr –1 ) than the increment rate after commitment (1.5 Mt CO2 yr –1 ). In addition, we projected that the GHG emission was reduced by 13.41% for 2020 and 18.89% for 2030 from the historical baseline. This result illustrated that the local REDD+ intervention in East Kalimantan is able to reduce GHG emissions. However, the progress was still smaller than the target written in the local action plan, which is expected to reduce GHG emissions by 22.38% from historical baseline for 2020. The provincial government should accelerate strategies for reducing GHG emissions by restoring degraded forest landscapes, avoiding further deforestation and forest degradation and making appropriate decisions for the socio‐economic development program. Thus, the spatial information is the critical instrument to assist the provincial government for forest restoration action plan where deforestation and forest degradation occurred.
Chapter
This chapter presents an overview of the main time series analysis methods for environment monitoring with earth observation, from classical methods to the deep learning (DL) methods. It summarizes main differences between bi-temporal change detection, annual time series and dense time series analyses, and also presents the three main types of annual time series methods for environment monitoring. The chapter focuses on dense time series methods using all available data, first presenting the main data preprocessing requirements, and provides an overview of the four main types of change detection methods based on dense time series analysis. These include: map classification, trajectory classification, statistical boundary and ensemble approaches. The chapter discusses three kinds of network architectures suited for the analysis of satellite image time series (SITS): recurrent neural networks, convolutional neural networks and hybrid models combining both. It proposes a prospective reflection upon possible convergence at crossroads between SITS analysis, video processing, computer vision and DL.
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This book presents the results of the IUCN Red List evaluation of all 347 tree taxa with a distribution confined to, or almost confined to, Central Africa (Democratic Republic of the Congo – Rwanda – Burundi). As such, it is part of a global endeavour involving over 60 organisations coordinated by Botanic Gardens Conservation International (BGCI), and culminating in the Global Tree Assessment. Endangered animals generally attract lots of attention, but trees are far more vulnerable. There are nearly 60,000 tree species recognised worldwide, and we now know that 30% (17,500 species) are threatened with extinction. This is higher than the number of all threatened mammals, birds, amphibians, and reptiles combined! The data on the 347 Central African trees presented here shows that 221 (64%) of them are at risk of extinction. Of these, 34 (10%) are critically endangered, of which 25 may already be extinct. Agriculture, livestock farming, and logging are the main global threats. Climate change impacts are emerging, and in Central Africa, charcoal production and mining also provide major pressures. Further, this volume provides a useful overview of all Protected Areas (PAs) in the region, with the (sub)endemic tree taxa they contain. Management plans can now be adjusted and improved taking this information into account. Several other recommendations are listed. Focussed action is needed to ensure the survival of threatened tree species, and all organisms (including humans) that depend on them.
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Human activity in African tropical rainforests continues to threaten wild mammals. Many rural communities are dependent on hunting, yet there is a widespread lack of baseline data on ecology and the sustainability of hunting. We investigated the impacts of human activity on mammal species composition and distributions within a community forest surrounding a village in the buffer zone of the Dja Biosphere Reserve in south-east Cameroon. We conducted a camera-trap survey in August–November 2017 and detected 24 mammal species, including Critically Endangered western lowland gorilla Gorilla gorilla gorilla , Endangered central African chimpanzee Pan troglodytes troglodytes and Endangered tree pangolin Phataginus tricuspis . We used occupancy analysis to explore relationships between indicators of human activity (distance to a road and the Reserve), habitat quality (distance to the river and tree cover) and the distributions of species. We found that the local distribution of threatened mammals was not apparently limited by human activity, and proximity to the road did not negatively influence occupancy for any species. However, most of the Reserve's large species were not detected, including the African forest elephant Loxodonta cyclotis and the largest ungulates, and the occupancy of two species commonly hunted for wild meat was positively correlated with distance from the village, indicating hunting may be unsustainable. Our results show that the community forest provides habitat for threatened species outside the Reserve and in close proximity to people. However, effective conservation management will require continued monitoring and research to determine whether current rates of hunting are sustainable.
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Time series reconstruction methods are widely used to generate smooth and gap-free time series using imagery acquired at coarse spatial resolution and high frequency return intervals. However, as interest has grown in leveraging the nearly 40-a record of Landsat to study long-term changes in terrestrial ecosystems at 30-m spatial resolution, new methods are required to reconstruct time series of Landsat imagery, which have lower temporal density than coarse resolution sensors such as AVHRR or MODIS. To address this need, we introduce a dynamic temporal smoothing (DTS) method that reconstructs sparse and noisy signals into dense time series at regular intervals. The DTS is a weighted smoother with parameters that adjust dynamically to variation in time series and can be applied to both dense and sparse time series measurements. Because the DTS smoother we describe is specifically designed to reconstruct high-quality time series of optical imagery, it has utility for applications focused on land cover and vegetation remote sensing over long time periods at moderate spatial resolution. We present the DTS algorithm that we have implemented and illustrate the ability of the DTS to reconstruct time series of Landsat imagery across multiple sensors (TM, ETM+, and OLI). To demonstrate the effectiveness of the DTS algorithm we apply it and evaluate results across a diverse range of land cover and vegetation types in the South American Southern Cone region.
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The Congo basin forests have vast conservation potential but because of their inaccessibility and periodic insecurity there is little formal protection or ecological research occurring there. Community-based conservation efforts in the unprotected forest corridor separating Kahuzi-Biega and Maiko National Parks in eastern Democratic Republic of the Congo aim to protect a unique forest ecosystem and facilitate the development of ecological research. To support this process, we obtained baseline data on the occurrence of terrestrial mammals in the Nkuba Conservation Area by conducting camera-trap (-) and transect (-) surveys. From camera-trap images we also extracted diel activity patterns and estimated overlap in these patterns between selected pairs of species. We identified  mammal species weighing.  kg using camera traps and  species in transect surveys, with a total of  mammal species, of which seven are categorized as threatened on the IUCN Red List. Among this mammalian community, we recorded nocturnal and diurnal species with short core activity periods, and several cathemeral species with long activity periods, with various degrees of temporal separation of diel activity between species. The presence of threatened species, including the Critically Endangered Grauer's gorilla Gorilla beringei graueri, suggests that the Nkuba Conservation Area harbours a forest community that requires continuous monitoring , further research and investment in protection from the ongoing deforestation and resource exploitation occurring in the surrounding region.
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