Article

Adapting a global stratified random sample for regional estimation of forest cover change derived from satellite imagery

February 2011
Remote Sensing of Environment 115(2):650-658

DOI:10.1016/j.rse.2010.10.009

Authors:

Stephen V. Stehman

State University of New York College of Environmental Science and Forestry

Mark Broich

UNSW Sydney

Peter Potapov

University of Maryland, College Park

A desirable feature of a global sampling design for estimating forest cover change based on satellite imagery is the ability to adapt the design to obtain precise regional estimates, where a region may be a country, state, province, or conservation area. A sampling design stratified by an auxiliary variable correlated with forest cover change has this adaptability. A global stratified random sample can be augmented by additional sample units within a region selected by the same stratified protocol and the resulting sample constitutes a stratified random sample of the region. Stratified sampling allows increasing the sample size in a region by a few to many additional sample units. The additional sample units can be effectively allocated to strata to reduce the standard errors of the regional estimates, even though these strata were not initially constructed for the objective of regional estimation. A complete coverage map of deforestation within the Brazilian Legal Amazon (BLA) is used as a population to evaluate precision of regional estimates obtained by augmenting a global stratified random sample. The standard errors of the regional estimates for the BLA and states within the BLA obtained from the augmented stratified design were generally smaller than those attained by simple random sampling and systematic sampling.Research Highlights► A stratified random sample can be augmented to provide precise regional estimates. ► The allocation of the stratified sample can be improved by better auxiliary information. ► Stratified random sampling standard errors were equal to or smaller than systematic sampling.

Improved forest-cover mapping based on MODIS time series and landscape stratification

Article

Jan 2017

Detailed forest-cover mapping at a regional scale by supervised classification is technically limited by various factors. This study evaluates the ability of a landscape stratification method to improve classification accuracy. An object-based segmentation technique (OBIA) was performed to delineate radiometrically homogeneous regions into the study area, used as strata for the classification of a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. As a reduction of the spatial variability of the signatures of the vegetation classes is expected, Maximum Likelihood Classifier (MLC) was used to analyse potential effects on classification accuracy. Accuracy assessment was based on the calculation of kappa coefficient (κ) and reject fraction (RF). The values obtained with and without stratification were compared, to assess their global and per-stratum influence on the quality of a detailed forest-cover map (20 different classes). To study the influence of topographical and landscape stratum characteristics on classification accuracy, eight indicators were calculated. Their correlation with κ and RF differences due to stratification was analysed. Our study showed that stratification improved global and per-stratum classification accuracy and in parallel caused an RF increase. Both these evolutions are not conditioned by the stratum topographical and landscape characteristics but strongly influenced by stratum and classified vegetation area.

A Sample-Based Forest Monitoring Strategy Using Landsat, AVHRR and MODIS Data to Estimate Gross Forest Cover Loss in Malaysia between 1990 and 2005

Article

Full-text available

Apr 2013

Insular Southeast Asia is a hotspot of humid tropical forest cover loss. A sample-based monitoring approach quantifying forest cover loss from Landsat imagery was implemented to estimate gross forest cover loss for two eras, 1990-2000 and 2000-2005. For each time interval, a probability sample of 18.5 km x 18.5 km blocks was selected, and pairs of Landsat images acquired per sample block were interpreted to quantify forest cover area and gross forest cover loss. Stratified random sampling was implemented for 2000-2005 with MODIS-derived forest cover loss used to define the strata. A probability proportional to x (pi px) design was implemented for 1990-2000 with AVHRR-derived forest cover loss used as the x variable to increase the likelihood of including forest loss area in the sample. The estimated annual gross forest cover loss for Malaysia was 0.43 Mha/yr (SE = 0.04) during 1990-2000 and 0.64 Mha/yr (SE = 0.055) during 2000-2005. Our use of the pi px sampling design represents a first practical trial of this design for sampling satellite imagery. Although the design performed adequately in this study, a thorough comparative investigation of the pi px design relative to other sampling strategies is needed before general design recommendations can be put forth.

Forest cover and deforestation patterns in the Northern Andes (Lake Maracaibo Basin): A synoptic assessment using MODIS and Landsat imagery

Article

Jul 2012
APPL GEOGR

This work synthesizes results from the application of land cover classification techniques and probability sampling of satellite imagery for estimating forest extent and deforestation in Lake Maracaibo Basin (Venezuela and Colombia). A forest map was produced using a semi-automated supervised classification routine on MODIS 8-day 500-m imagery acquired in January 2010. Results show that forests occupy 29,710 km 2 which represents 38% of the basin's total terrestrial landmass. From this extent, 61% belongs to Venezuela and 39% falls within the Colombian region. Findings indicate a drastic decrease in forest cover as a result of anthropogenic agricultural and urban expansion, especially when compared to its potential extent within the 'Maracaibo dry forests' and the 'Venezuelan Andean montane forests' ecor-egions. Using time series of Landsat imagery, deforestation rates for the 1985e2010 time period were calculated. The analysis was performed on 24 samples blocks of 10 Â 10 km 2 randomly allocated within previously defined change probability strata. The general spatial distribution of deforestation rates was predicted by a simple regression model between sample blocks and prior change probabilities at the basin scale. Our results indicate that deforestation was low (<0.5%/y) in 85% of the basin, with highly focalized deforestation fronts (intermediate-to-high rates, <2.5%/y) in three regions: a) the Motatán river sub-basin in the Eastern Cordillera, b) the lower slopes of the Catatumbo river sub-basin and c) the submontane regions of the Apón and Santa Ana river sub-basins. The results of this paper lead the way for understanding current patterns in socioeconomic drivers of forest clearing in Lake Maracaibo Basin. The study also demonstrates the feasibility of using alternatives methods to the time-consuming and financially unsustainable methods traditionally used at national and sub-national scale in Venezuela and other Latin American countries.

Інвентаризація рівнинних лісів України за даними супутникової зйомки: монографія

Book

Full-text available

Dec 2020

Viktor Myroniuk

In the monograph, an accuracy of four global forest products of 25–30 m spatial resolution has been analysed. Exploring the relationship of tree cover extracted from the Global Forest Change (GFC) data and relative stocking of forest stands, the 40 % tree cover percentage value has been adopted as a threshold for distinction between forested and unforested areas within flat land Ukraine. In comparison with analogous product Landsat Tree Cover Continuous Fields (LTCCF), the GFC map has appeared to be more advanced in terms of accuracy and applicability for forest area dynamics assessment. Analysis of discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30 has revealed a major misclassification of forested areas under severe fragmentation patterns of flat land forest Ukraine. The results of global map of forest biomass GlobBiomass showed that it over approximates the data so that the mapped growing stock volume at pixel level is not agreed with the field measurements data. Nevertheless, the map could be used for preliminary assessment of total and mean values of growing stock volumes for large areas that has been successfully tested on 15 randomly chosen forest enterprises distributed throughout the study region. To map forested areas in flat land Ukraine using Landsat 8 OLI imagery, the land cover reflectance properties in different spectral bands have been investigated. As a result, phenology-based methods of classification for processing time series of satellite observations have been substantiated, thus more than 1500 scenes of Landsat 8 OLI have been merged into cloudless mosaics for the following four seasons: yearly, summer, autumn and April-October. Google Earth Engine API for cloud-based computation allowed to run high performance algorithms for creation, processing and classification of Landsat 8 OLI seasonal mosaics, while nonparametric method Random Forest was efficient to deal with multidimensional training dataset. Finally, forest mask for flat land of Ukraine has been created having 30 m spatial resolution. Its user’s and producer’s accuracy are 0.910±0.015 and 0.880±0.018 accordingly, albeit for Polissia regions the accuracy is higher, but for Steppe zone it is on the contrary, lower. The forest mask allowed to estimate forested area which for the flat land Ukraine is 9440.5±239.4 thousands hectares. Forest cover for the territory is estimated to be 17.4±0.4 %. The analogous values according to the state forest assessment data are significantly lower, 7766.7 thousand hectares and 14.4 % respectively. The overestimation of forested area can be explained due to the forests that are not included in the state forest assessment data (for example, urban forests). Basal area of main tree species in forest stands of Sumy region have been mapped using data collected by Centre of national forest inventory during 2013 and custom version of k-NN (k-Nearest Neighbors) algorithm written for Google Earth Engine API. Per-pixel distribution of basal area allowed to map tree species abundance, composition and tree species richness of forest stands. Applying the same distance matrix between nearest neighbors, growing stock volume for each tree species have been mapped for the region. As it was computed, the 95 % confidence interval for mean value of growing stock volume for Sumy region is 329±8 m3·ha-1, while in different districts of the region mean value varies between 268 and 385 m3·ha-1. The estimated values are significantly higher of those in the official forest inventory database. Besides, forested area and total growing stock volume of forest stands for major tree species were assessed.

Sampling Estimation and Optimization of Typical Forest Biomass Based on Sequential Gaussian Conditional Simulation

Article

Full-text available

Sep 2023

The traditional classical sampling statistics method ignores the spatial location relationship of survey samples, which leads to many problems. This study aimed to propose a spatial sampling method for sampling estimation and optimization of forest biomass, achieving a more efficient and effective monitoring system. In this paper, we used Sequential Gaussian Conditional Simulation (SGCS) to obtain the biomass of four typical forest types in Shangri-La, Yunnan Province, China. In addition, we adopted a geostatistical sampling method for sample point layout and optimization to achieve the purpose of improving sampling efficiency and accuracy, and compared with the traditional sampling method. The main results showed that (1) the Gaussian model, exponential model, and spherical model were used to analyze the variogram of the four typical forests biomass, among which the exponential model had the best fitting effect (R2 = 0.571, RSS = 0.019). The range of the exponential model was 8700 m, and the nugget coefficient (C0/(C0 + C)) was 11.67%, which showed that the exponential model could be used to analyze the variogram of forest biomass. (2) The coefficient of variation (CV) based on 323 biomass field plots was 0.706, and the CV based on SGCS was 0.366. In addition, the Overall Estimate Consistency (OEC) of the simulation result was 0.871, which can be used for comparative analysis of traditional and spatial sampling. (3) Based on the result of SGCS, with 95% reliability, the sample size of traditional equidistant sampling (ES) was 191, and the sampling accuracy was 95.16%. But, the spatial sampling method based on the variation scale needed 92 samples, and the sampling accuracy was 93.12%. On the premise of satisfying sampling accuracy, spatial sampling efficiency was better than traditional ES. (4) The accuracy of stratified sampling (SS) of four typical forest areas based on 191 samples was 97.46%. However, the sampling accuracy of the biomass variance stratified space based on the SGCS was 93.89%, and the sample size was 52. Under the premise of satisfying the sampling accuracy, the sampling efficiency was obviously better than the traditional SS. Therefore, we can obtain the conclusion that the spatial sampling method is superior to the traditional sampling method, as it can reduce sampling costs and solve the problem of sample redundancy in traditional sampling, improving the sampling efficiency and accuracy, which can be used for sampling estimation of forest biomass.

Forest Clearing Dynamics and Its Relation to Remotely Sensed Carbon Density and Plant Species Diversity in the Puuc Biocultural State Reserve, Mexico

Article

Full-text available

Jul 2023

The Puuc Biocultural State Reserve (PBSR) is a unique model for tropical dry forest conservation in Mexico. Preserving forest biodiversity and carbon within the PBSR depends on maintaining low-impact productive activities coordinated by multiple communal and private landowners. In this study, we used state-of-the-art remote sensing data to investigate past spatial patterns in forest clearing dynamics and their relation to forest carbon density and forest plant species richness and diversity in the context of the forest conservation goals of the PBSR. We used a Landsat-based continuous change detection product for the 2000–2021 period and compared it to carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (�19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was signiﬁcantly more intensive outside of the PBSR than within the PBSR during the entire 2000–2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing changed over the years after the creation of the PBSR in 2011. However, an emergent hotspot analysis shows that high spatiotemporal clustering of forest clearing events (hotspots) during the 2012–2021 period was less common than prior to 2011, and these more recent hotspots have been conﬁned to areas outside the PBSR. After comparing forest clearing events to carbon density and tree species richness models, the results show that landowners outside the PBSR often clear forests with lower carbon density and species diversity than those inside the PBSR. This suggests that, compared to landowners outside the PBSR, landowners within the PBSR might be practicing longer fallow periods allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acted as a stabilizing forest management scheme during the 2012–2021 period, minimizing the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests within the PBSR. We recommend continuing efforts to provide alternative optimal ﬁeld data collection strategies and modeling techniques to spatially predict key tropical forest attributes. Combining these models with continuous change detection datasets will allow for underlying ecological processes to be revealed and the generation of information better adapted to forest governance scales.

Forest Clearing Dynamics and Its Relation to Remotely-Sensed Carbon Density and Plant Species Diversity in the Puuc Biocul-Tural State Reserve, Mexico

Preprint

Full-text available

Jun 2023

The Puuc Biocultural State Reserve (PBSR is an unique model for tropical dry forest conservation in Mexico. Preserving forest biodiversity and carbon within the PBSR depends on the mainte-nance of low impact productive activities coordinated by multiple communal and private land-owners. In this study, we used state-of-the-art remote sensing data to investigate past spatial patterns in forest clearing dynamics and its relation to forest carbon density and forest plant species richness and diversity in the context of the forest conservation goals of the PBSR. We used a Landsat-based continuous change detection product for the 2000-2021 period and compared it to carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (+19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was significantly more intensive outside of the PBSR than within the PBSR during the entire 2000-2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing has changed over the years after the creation of the PBSR in 2011. An emergent hotspot analysis shows, however, that high spatiotemporal clustering of forest clearing events (hotspots) during the 2012-2021 pe-riod was less common than prior to 2011 and these more recent hotspots have been confined to areas outside the PBSR. After comparing forest clearing events to carbon density and tree species richness models, results show that land owners outside the PBSR often clear forests with lower carbon density and species diversity than land owners inside the PBSR. This suggests that, compared to land owners outside the PBSR, land owners within the PBSR might be practicing longer fallow periods allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acted as a stabilizing forest management scheme during the 2012-2021 period minimizing the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests within the PBSR. We recommend the continuation of efforts for providing alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. The combination of these models with contin-uous change detection datasets will allow to reveal underlying ecological processes and generate information better adapted to forest governance scales.

Fractal Theory Based Stratified Sampling for Quality Assessment of Remote-Sensing-Derived Geospatial Data

Article

Full-text available

Jan 2023

The stratified sampling is widely used in quality assessment of remote-sensing-derived geospatial data (RSGD). Because of the different stratification indicators (also called the stratification variables) used in stratified sampling, it will lead to different evaluation results. By using fractal theory, this paper proposes a stratified sampling method based on fractal (SSF) for quality assessment of RSGD. As a stratification variable, fractal dimension is related to and independent of the study variable in the quality assessment of RSGD. This method can quantitatively and accurately stratify the population, which leads to minimizing the intra stratum variance, acquiring higher estimation accuracy and estimation efficiency. The proposed SSF method in this paper is transformed into three formulated problems: the quantitative calculation of fractal, the optimal solution of the stratum boundary value and the configuration of sample sizes. The experiment shows a quantitative performance comparison of SSF, Stratified Sampling based on Class (SSC) and Sample Random Sampling (SRS) using the South Sudan Global Core Vector Database (GCVD)2020. Design effect (DEFF) and Root-Mean-Square Error (RMSE) provide a quantitative assessment of the performance in this study. The experimental results verify the feasibility and applicability of the SSF proposed in this paper. It also shows higher estimation accuracy and more economical cost.

Remotely-Sensed Carbon Density, Plant Species Diversity and Its Relation to Forest Clearing Dynamics in the Puuc Biocultural State Reserve, Mexico

Preprint

Full-text available

May 2023

In the Neotropics, the integration of remotely sensed products to understand socioecological processes at local scales is limited by the physical difficulties and financial costs of collecting field data to train and validate these models. In this study, we used carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data and compared these products to a Landsat-based continuous change detection product for the 2000-2021 period. This was performed to evaluate forest clearing dynamics in and around the Puuc Biocultural State Reserve (PBSR) in Mexico. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (+19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was significantly more intensive outside of the PBSR than within the PBSR during the entire 2000-2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing has changed over the years after the creation of the PBSR in 2011. The emerging hotspot analysis shows, however, that forest clearing spatiotemporal clustering (hotspots) during the 2012-2021 period was less widespread and mostly confined to areas outside the PBSR. In addition, the analysis shows forest clearing clustering is on a downward trend within the PBSR. After comparing forest clearing events to carbon density and tree species richness models, our data also suggests that land owners within the PBSR might be practicing longer barbecho (fallow) periods in contrast to land owners outside the PBSR allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acts as stabilizing forest management scheme that minimizes the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests. We recommend the continuation of efforts for providing alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. The combination of these models with continuous change detection datasets will allow to reveal underlying ecological processes and generate information better adapted to forest governance scales.

Real-Time Wildfire Detection Algorithm Based on VIIRS Fire Product and Himawari-8 Data

Article

Full-text available

Mar 2023

Wildfires have a significant impact on the atmosphere, terrestrial ecosystems, and society. Real-time monitoring of wildfire locations is crucial in fighting wildfires and reducing human casualties and property damage. Geostationary satellites offer the advantage of high temporal resolution and are gradually being used for real-time fire detection. In this study, we constructed a fire label dataset using the stable VNP14IMG fire product and used the random forest (RF) model for fire detection based on Himawari-8 multiband data. The band calculation features related brightness temperature, spatial features, and auxiliary data as input used in this framework for model training. We also used a recursive feature elimination method to evaluate the impact of these features on model accuracy and to exclude redundant features. The daytime and nighttime RF models (RF-D/RF-N) are separately constructed to analyze their applicability. Finally, we extensively evaluated the model performance by comparing them with the Japan Aerospace Exploration Agency (JAXA) wildfire product. The RF models exhibited higher accuracy, with recall and precision rates of 95.62% and 59%, respectively, and the recall rate for small fires was 19.44% higher than that of the JAXA wildfire product. Adding band calculation features and spatial features, as well as feature selection, effectively reduced the overfitting and improved the model’s generalization ability. The RF-D model had higher fire detection accuracy than the RF-N model. Omission errors and commission errors were mainly concentrated in the adjacent pixels of the fire clusters. In conclusion, our VIIRS fire product and Himawari-8 data-based fire detection model can monitor the fire location in real time and has excellent detection capability for small fires, making it highly significant for fire detection.

A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020

Article

Full-text available

Oct 2022
ENVIRON SCI POLLUT R

To assess the status of hotspots and research trends on geographic information system (GIS)–based landslide susceptibility (LS), we analysed 1142 articles from the Thomas Reuters Web of Science Core Collection database published during 2001–2020 by combining bibliometric and content analysis. The paper number, authors, institutions, corporations, publication sources, citations, and keywords are noted as sub/categories for the bibliometric analysis. Thematic LS data, including the study site, landslide inventory, conditioning factors, mapping unit, susceptibility models, and mode fit/prediction performance evaluation, are presented in the content analysis. Then, we reveal the advantages and limitations of the common approaches used in thematic LS data and summarise the development trends. The results indicate that the distribution of articles shows clear clusters of authors, institutions, and countries with high academic activity. The application of remote sensing technology for interpreting landslides provides a more convenient and efficient landslide inventory. In the landslide inventory, most of the sample strategies representing the landslides are point and polygon, and the most frequently used sample subdividing strategy is random sampling. The scale effects, lack of geographic consistency, and no standard are key problems in landslide conditioning factors. Feature selection is used to choose the factors that can improve the model’s accuracy. With advances in computing technology and artificial intelligence, LS models are changing from simple qualitative and statistical models to complex machine learning and hybrid models. Finally, five future research opportunities are revealed. This study will help investigators clarify the status of LS research and provide guidance for future research.

Object-Based Land Use and Land Cover Change Detection in Multi Temporal Remote-Sensing Images

Preprint

Jun 2021

The changing of land use and land cover (LULC) are both affected by climate and human activity and affect climate, biological diversity, and human well-being. Accurate and timely information about the LULC pattern and change is crucial for land management decision-making, ecosystem monitoring, and urban planning, especially in developing economies undergoing industrialization, urbanization, and globalization. Biodiversity degradation and urban expansion in eastern China are research hot-spots. However, the influence of LULC changes on the region remains largely unexplored. Here, an object-based and multi-temporal image analysis approach was developed to detect how LULC changes during 1985-2015 in the Tiaoxi watershed (Zhejiang province, eastern China) using Landsat TM and OLI data. The main objective of this study is to improve the accuracy of unsupervised change detection from object-based and multi-temporal images. To this end, a total of seven LULC maps are generated with multi-temporal images. A random stratified sample design was used for assessing change detection accuracy. The proposed method achieved an overall accuracy of 91.86%, 92.14%, 92.00%, and 93.86% for 2000, 2005, 2010, and 2015, respectively. Nevertheless, the proposed method, in conjunction with object-oriented and multi-temporal satellite images, offers a robust and flexible approach to LULC changes mapping that helps with emergency response and government management. Urbanization and agriculture efficiency are the main reasons for LULC changes in the region. We anticipate that this freely available data will improve the modeling for surface forcing, provide evidence of changes in LULC, and inform water-management decision-making.

Use of a Systematic Statistical Sample with Moderate-Resolution Imagery to Assess Forest Cover Changes at Tropical to Global Scale

Chapter

Full-text available

Apr 2016

Environmental Quality Assessment of Urban Ecology based on Spatial Heterogeneity and Remote Sensing Imagery

Article

Aug 2019

The phenomenon of urban ecology is very comprehensive, for example, rapid land-use changes, decrease in vegetation cover, dynamic urban climate, high population density, and lack of urban green space. Temporal resolution and spatial resolution of remote sensing data are fundamental requirements for spatial heterogeneity research. Remote sensing data is very effective and efficient for measuring, mapping, monitoring, and modeling spatial heterogeneity in urban areas. The advantage of remote sensing data is that it can be processed by visual and digital analysis, index transformation, image enhancement, and digital classification. Therefore, various information related to the quality of urban ecology can be processed quickly and accurately. This study integrates urban ecological, environmental data such as vegetation, built-up land, climate, and soil moisture based on spectral image response. The combination of various indices obtained from spatial data, thematic data, and spatial heterogeneity analysis can provide information related to urban ecological status. The results of this study can measure the pressure of environment caused by human activities such as urbanization, vegetation cover and agriculture land decreases, and urban micro-climate phenomenon. Using the same data source indicators, this method is comparable at different spatiotemporal scales and can avoid the variations or errors in weight definitions caused by individual characteristics. Land use changes can be seen from the results of the ecological index. Change is influenced by human behavior in the environment. In 2002, the ecological index illustrated that regions with low ecology still spread. Whereas in 2017, good and bad ecological indices are clustered.

Exploring Spectral Index Band and Vegetation Indices for Estimating Vegetation Area

Article

Full-text available

Dec 2018

Visual analysis and transformation of vegetation indices have been widely applied in studies of vegetation density using remote sensing data. However, visual analysis is time intensive compared to index transformation. On the other hand, the index transformation from medium resolution imagery is not fully representative for urban vegetation studies. Meanwhile, the spectral range of high-resolution imagery is usually limited to visible wavelengths for the image transformation. Worldview-2 imagery provides a new breakthrough with a high spatial resolution and supports various spectral resolutions. This study aims to explore the spectral value of the Worldview-2 image index for estimation of vegetation density. Normalized indices were made for 56 band combinations and Otsu thresholding was implemented for the threshold selection to separate vegetation and non-vegetation areas. This thresholding was done by minimizing classes’ variances between two groups of pixels which are distinguished by system or classification. The image binarization process was performed to differentiate between vegetation and non-vegetation. For the accuracy testing, a total of 250 samples was produced by a stratified random sampling method. Our results show that the combination of indices from red channel, red-edge, NIR-1, and NIR-2 provides the best accuracy for semantic accuracy. Vegetation area extracted from the index was then compared with the results of the visual analysis. Although the index results in area difference of 2.32 m2 compared to visual analysis, the combination of NIR-2 and red bands can give an accuracy of 96.29 %.

Assessing the accuracy of detected breaks in Landsat time series as predictors of small scale deforestation in tropical dry forests of Mexico and Costa Rica

Article

Jan 2019
REMOTE SENS ENVIRON

Tracking the occurrence of deforestation events is an essential task in tropical dry forest (TDF) conservation efforts. Ideally, deforestation monitoring systems would identify a TDF clearing with near real time precision and high spatial detail, and alert park managers and environmental practitioners of illegal forest clearings occurring anywhere in a region of interest. Over the past several years there have been significant advances in the design and application of continuous land cover change mapping algorithms with these capabilities, but no studies have implemented such methods over human dominated TDF environments where small-scale deforestation (< 5 ha) is widespread and hard to detect with moderate resolution sensors. The general objective for this research was to evaluate the overall accuracy of the BFASTSpatial R Package for detecting and monitoring small-scale deforestation in four sites located in tropical dry forest landscapes of Mexico and Costa Rica using greenness and moisture spectral indices derived from Landsat time series. Results show a high degree of spatial agreement (90%-94%) between the distribution of TDF clearings occurred during the 2013-2016 period (as indicated by VHR imagery interpretation) and BFASTSpatial outputs. NDMI and NBR2 had the best performance than other indices and this is evidenced by the combined overall, user's and producer's accuracies. In particular, NBR2 were the most accurate predictor of deforestation with an overall accuracy of 94.5%. Our results also imply that monitoring sites at an annual basis is feasible using BFASTSpatial and LTS, but that lower confidence should be given to sub-annual products given significant systematic temporal differences between the BFASTSpatial monthly product and reference data. The possibility of including more clear observations at the spatial resolution of Landsat (30-m) or higher will greatly increase the spatial and temporal accuracies of the method. Given its performance, BFASTSpatial can help monitor hotspots of small-scale TDF loss across Central and North America at little or no cost. Users of the method should have a strong knowledge of the local land use and land cover dynamics and the ecophysiology of vegetation types present in the landscape. This local expertise is necessary for interpreting and validating results as well as communicating its output to decision-makers and stakeholders.

Evaluation of different approaches for identifying optimal sites to predict mean hillslope soil moisture content

Article

Jan 2017
J HYDROL

The identification of representative soil moisture sampling sites is important for the validation of remotely sensed mean soil moisture in a certain area and ground-based soil moisture measurements in catchment or hillslope hydrological studies. Numerous approaches have been developed to identify optimal sites for predicting mean soil moisture. Each method has certain advantages and disadvantages, but they have rarely been evaluated and compared. In our study, surface (0-20 cm) soil moisture data from January 2013 to March 2016 (a total of 43 sampling days) were collected at 77 sampling sites on a mixed land-use (tea and bamboo) hillslope in the hilly area of Taihu Lake Basin, China. A total of 10 methods (temporal stability (TS) analyses based on 2 indices, K-means clustering based on 6 kinds of inputs and 2 random sampling strategies) were evaluated for determining optimal sampling sites for mean soil moisture estimation. They were TS analyses based on the smallest index of temporal stability (ITS, a combination of the mean relative difference and standard deviation of relative difference (SDRD)) and based on the smallest SDRD, K-means clustering based on soil properties and terrain indices (EFs), repeated soil moisture measurements (Theta), EFs plus one-time soil moisture data (EFsTheta), and the principal components derived from EFs (EFs-PCA), Theta (Theta-PCA), and EFsTheta (EFsTheta-PCA), and global and stratified random sampling strategies. Results showed that the TS based on the smallest ITS was better (RMSE=0.023 m³ m⁻³) than that based on the smallest SDRD (RMSE=0.034 m³ m⁻³). The K-means clustering based on EFsTheta (-PCA) was better (RMSE<0.020 m³ m⁻³) than these based on EFs (-PCA) and Theta (-PCA). The sampling design stratified by the land use was more efficient than the global random method. Forty and 60 sampling sites are needed for stratified sampling and global sampling respectively to make their performances comparable to the best K-means method (EFsTheta-PCA). Overall, TS required only one site, but its accuracy was limited. The best K-means method required <8 sites and yielded high accuracy, but extra soil and terrain information is necessary when using this method. The stratified sampling strategy can only be used if no pre-knowledge about soil moisture variation is available. This information will help in selecting the optimal methods for estimation the area mean soil moisture.

Mapping and estimating forest area and aboveground biomass in miombo woodlands in Tanzania using data from airborne laser scanning, TanDEM-X, RapidEye, and global forest maps: A comparison of estimated precision

Article

Mar 2016
REMOTE SENS ENVIRON

Field surveys are often a primary source of data for aboveground biomass (AGB) and forest area estimates — two fundamental parameters in forest resource assessments and for measurement, reporting, and verification (MRV) under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD +). However, plot-based estimates of such parameters are often not sufficiently precise for their intended purposes, and especially so in developing and tropical countries in which implementation of extensive sample surveys can be cost-prohibitive or infeasible due to inaccessibility. Remotely sensed data can improve the precision of estimates and thereby reduce the need for field samples. To guide investment decision in MRV systems, comparative analyses of the contribution of different types of remotely sensed data to improve precision of estimates are required. The aim of the current study was to quantify the contribution of data from (1) airborne laser scanning (ALS), (2) interferometric synthetic aperture radar (InSAR) derived from TanDEM-X, (3) RapidEye optical imagery, and global forest map products derived from (4) Landsat and (5) ALOS PALSAR L-band radar imagery to improve precision of AGB and forest area estimates beyond the precision that could be obtained by a pure field-based survey in miombo woodlands of Tanzania. Miombo woodlands is one among the most wide-spread vegetation types in eastern, central, and southern Africa, occupying about 9% of the entire African land area. A 365.6 km2 region in Liwale district in Tanzania served as area of interest for this study. Eighty-eight ground plots distributed on 11 clusters of eight plots each according to a probability-based single-stage cluster sampling design served as field data for regression model calibration used for mapping and estimation of AGB and forest area. Model-assisted estimators were used in the estimation. The relative efficiency (RE) of the ALS-assisted estimates of mean AGB per hectare (variance of the field-based estimate relative to the variance of the ALS-assisted estimate) was 3.6. Relative efficiency translates directly to the factor by which the sample size used for the ALS-assisted estimate would have to be multiplied to arrive at the same precision for a pure field-based estimate. RE values for InSAR and RapidEye were 2.8 and 3.3, while the global Landsat and PALSAR map products contributed only marginally to improve precision (RE = 1.3–1.4). For forest area estimation, ALS-assisted estimates showed an RE of 3.7–4.6, while InSAR, RapidEye, and global Landsat and PALSAR maps resulted in RE values of 1.0–1.3, 2.0–2.1, 1.4–1.8, and 1.7, respectively.

Estimating tropical deforestation and related carbon emissions over 1990-2010 from medium resolution optical imagery

Conference Paper

Sep 2013

Assessment of forest cover in Russia by combining a wall-to-wall coarse-resolution land-cover map with a sample of 30 m resolution forest maps

Article

Full-text available

Mar 2014

The process of gathering land-cover information has evolved significantly over the last decade (2000–2010). In addition to this, current technical infrastructure allows for more rapid and efficient processing of large multi-temporal image databases at continental scale. But whereas the data availability and processing capabilities have increased, the production of dedicated land-cover products with adequate accuracy is still a prerequisite for most users. Indeed, spatially explicit land-cover information is important and does not exist for many regions. Our study focuses on the boreal Eurasia region for which limited land-cover information is available at regional level.

Determination of tropical deforestation rates and related carbon losses from 1990 to 2010

Article

Full-text available

Apr 2014
GLOBAL CHANGE BIOL

We estimate changes in forest cover (deforestation and forest regrowth) in the tropics for the two last decades (1990-2000 and 2000-2010) based on a sample of 4,000 units of 10km×10km size. Forest cover is interpreted from satellite imagery at 30m×30m resolution. Forest cover changes are then combined with pan-tropical biomass maps to estimate carbon losses. We show that there was a gross loss of tropical forests of 8.0 million ha y−1 in the 1990s and 7.6 million ha y−1 in the 2000s (0.49% annual rate), with no statistically significant difference. Humid forests account for 64% of the total forest cover in 2010 and 54% of the net forest loss during second study decade. Losses of forest cover and other wooded land cover result in estimates of carbon losses which are similar for 1990s and 2000s at 887 MtC y−1 (range: 646 – 1238) and 880 MtC y−1 (range: 602 – 1237) respectively, with humid regions contributing two thirds. The estimates of forest area changes have small statistical standard errors due to large sample size. We also reduce uncertainties of previous estimates of carbon losses and removals. Our estimates of forest area change are significantly lower as compared to national survey data. We reconcile recent low estimates of carbon emissions from tropical deforestation for early 2000s and show that carbon loss rates did not change between the two last decades. Carbon losses from deforestation represent circa 10% of Carbon emissions from fossil fuel combustion and cement production during the last decade (2000-2010). Our estimates of annual removals of carbon from forest regrowth at 115 MtC y−1 (range: 61-168) and 97 MtC y−1 (53-141) for the 1990s and 2000s respectively are five to fifteen times lower than earlier published estimates.This article is protected by copyright. All rights reserved.

Improved Mapping of Tropical Forests With Optical and SAR Imagery, Part I: Forest Cover and Accuracy Assessment Using Multi-Resolution Data

Article

Full-text available

Feb 2013

This paper describes an improved concept for the mapping of tropical forest classes with ALOS AVNIR and ALOS PALSAR data. The improvement comes from a combination of a sample of very high resolution (VHR) satellite images with medium resolution wall-to-wall mapping in a statistical sampling framework. The approach developed makes it possible to obtain reliable information on mapping accuracy over the whole area of interest. A simulation study indicated that the sample of VHR images should be collected in a stratified manner using small (25 km) images. The VHR images should cover approximately one percent of the total area of interest, depending on the accuracy requirement. The recommended size of the reference plots (population units) that are selected within the VHR imagery is in the order of 50 m by 50 m. In a systematic selection the plots should be located at a distance of several hundred meters from each other. The forest variables were predicted with an unsupervised fuzzy classification method. The ALOS AVNIR-based forest/non-forest mapping accuracies varied between 68% and 97% of the areas of the VHR images. The corresponding ALOS PALSAR mapping accuracies were poorer. At AVNIR resolution, the area of natural forest was over-estimated, and the degree of disturbance underestimated in humid, heavily disturbed parts of the study area in Laos. The three predictions for the total forest fraction from VHR, AVNIR and PALSAR data over the area that was covered by the VHR images were 55.1%, 53.6%, and 52.8%, respectively.

The 2010 global forest resource assessment remote sensing survey

Article

Full-text available

Jan 2009

Global concern is growing over deforestation because of its climate impacts as well as loss of biodiversity and other forest services. FAO has been reporting on the world's forests at 5 to 10 year intervals from 1946 to 2005 through the Global Forest Resources Assessments (FRA). As part of FRA 2010, FAO, its member countries and partner organizations are undertaking a new global remote sensing survey of forests over the next two years. The assessment will use the Landsat Global Land Survey database to systematically sample the entire land surface of the Earth at each degree intersection of latitude and longitude. The main outcomes will be information at the global and ecozone level on changes in forest cover and land use including trends in the rate of deforestation, afforestation and natural expansion of forests from 1990 to 2005.

Forest Cover Changes in Tropical South and Central America from 1990 to 2005 and Related Carbon Emissions and Removals

Article

Full-text available

Dec 2012

This paper outlines the methods and results for monitoring forest change and resulting carbon emissions for the 1990-2000 and 200-2005 periods carried out over tropical Central and South America. To produce our forest change estimates we used a systematic sample of medium resolution satellite data processed to forest change maps covering 1230 sites of 20 km by 20 km, each located at the degree confluence. Biomass data were spatially associated to each individual sample site so that annual carbon emissions could be estimated. For our study area we estimate that forest cover in the study area had fallen from 763 Mha (s.e. 10 Mha) in 1990 to 715 Mha (s.e. 10 Mha) in 2005. During the same period other wooded land (i.e., non-forest woody vegetation) had fallen from 191 Mha (s.e. 5.5 Mha) to 184 Mha (s.e. 5.5 Mha). This equates to an annual gross loss of 3.74 Mha.y(-1) of forests (0.50% annually) between 1990 and 2000, rising to 4.40 Mha.y(-1) in the early 2000s (0.61% annually), with Brazil accounting for 69% of the total losses. The annual carbon emissions from the combined loss of forests and other wooded land were calculated to be 482 MtC.y(-1) (s.e. 29 MtC.y(-1)) for the 1990s, and 583 MtC.y(-1) (s.e. 48 MtC.y(-1)) for the 2000 to 2005 period. Our maximum estimate of sinks from forest regrowth in tropical South America is 92 MtC.y(-1). These estimates of gross emissions correspond well with the national estimates reported by Brazil, however, they are less than half of those reported in a recent study based on the FAO country statistics, highlighting the need for continued research in this area.

A Study on the Accuracy and Precision of Estimation of the Number, Basal Area and Standing Trees Volume per Hectare Using of some Sampling Methods in Forests of NavAsalem

Article

Jun 2019

A quantitative performance comparison of paddy rice acreage estimation using stratified sampling strategies with different stratification indicators

Article

Full-text available

Sep 2017

Various indicators derived from thematic maps have been widely used to determine the strata needed to perform stratified sampling. However, these indicators typically do not quantify the spatial errors in the crop thematic maps that are needed to reduce the uncertainty. To address this lack of error information, this paper introduces a hybrid entropy indicator (HEI). Two conventional indicators, the acreage indicator (AI) and the fragmentation indicator (FI), were also evaluated to compare the results of the three indicators in a homogeneous agricultural area (Pinghu, PH) and a heterogeneous agricultural area (Zhuji, ZJ). The results show that HEI performs the best in heterogeneous areas with the lowest coefficient of variation (CV) (as low as 1.59%) and also has the highest estimation accuracy with the lowest standard deviation of estimation. For both areas, the performances of HEI and AI are very similar, and better than FI. These results highlight that the HEI should be considered as an effective indicator and used in place of AI and FI to help improve sampling efficiency of crop acreage estimation, while FI is not recommended. Furthermore, the positive performance achieved using HEI indicates the potential for incorporating thematic map uncertainty information to improve sampling efficiency.

Change detection tools

Article

Jan 2009

In this chapter a wide range of change detection tools is addressed. They are grouped into methods suitable for optical and multispectral data, synthetic aperture radar (SAR) images, and 3D data. Optical and multispectral methods include unsupervised approaches, supervised and knowledge-based approaches, pixel-based and object-oriented approaches, multivariate alteration detection, hyperspectral approaches, and approaches that deal with changes between optical images and existing vector data. Radar methods include constant false-alarm rate detection, adaptive filtering, multi-channel segmentation (an object-oriented approach), hybrid methods, and coherent change detection. 3D methods focus on tools that are able to deal with 3D information from ground based laser-ranging systems, LiDAR, and elevation models obtained from air/space borne optical and SAR data. Highlighted applications are landcover change, which is often one of the basic types of information to build analysis on, monitoring of nuclear safeguards, third-party interference close to infrastructures (or borders), and 3D analysis. What method to use is dependent on the sensor, the size of the changes in comparison with the resolution, their shape, textural properties, spectral properties, and behaviour in time, and the type of application. All these issues are discussed to be able to determine the right method, with references for further reading.

Deforestation Mapping Sampling Designs for Canadian Landscapes

Article

Aug 2015

Deforestation is the direct human-induced conversion of forest to nonforest land uses. It is important for nations to understand and report the extent of their deforestation. Because of the vastness of Canada's forest and the rare and spatially diverse nature of its deforestation, a sampling approach in which deforestation is mapped and then scaled up to represent deforestation for different regions was needed. The effectiveness of different sample designs in capturing the area of deforestation was evaluated using a Monte Carlo approach in which alternate sample designs were applied to simulated forest landscapes representative of different regions and deforestation patterns in Canada. Sampling error as expressed by the standard error in the estimated deforestation level for the sample divided by actual deforestation of the simulated landscape was used as a measure of sample design performance. Results indicated that sampling error was dependent on the characteristics of the deforestation (e.g., amount, shape, size, and distribution). For example, as mean event size increases or the proportion of linear deforestation events (e.g., roads and corridors) decreases, the required sampling intensity to reach a certain level of sampling error increases, and landscapes with a small number of very large events required the largest sampling intensity. To achieve a relative sampling error target (standard error / sample mean) of 10%, given sample designs of square plots on a systematic grid, a sample of 15%-25% of a landscape will be required for most Canadian landscapes, given a 10-year mapping time frame (interval between samples) and assuming a deforestation rate of 0.025% per annum. With mapping over a 5-year period, the required sampling intensity rises to 20%-40%. Also discussed are the consequences of the sampling error of different designs on the uncertainty in estimated greenhouse gas emission resulting from deforestation.

Earth Observation Satellite Sensors for Biodiversity Monitoring: Potentials and Bottlenecks

Article

Full-text available

Oct 2014
INT J REMOTE SENS

Many biologists, ecologists, and conservationists are interested in the possibilities that remote sensing offers for their daily work and study site analyses as well as for the assessment of biodiversity. However, due to differing technical backgrounds and languages, cross-sectorial communication between this group and remote-sensing scientists is often hampered. Hardly any really comprehensive studies exist that are directed towards the conservation community and provide a solid overview of available Earth observation sensors and their different characteristics. This article presents, categorizes, and discusses what spaceborne remote sensing has contributed to the study of animal and vegetation biodiversity, which different types of variables of value for the biodiversity community can be derived from remote-sensing data, and which types of spaceborne sensor data are available for which time spans, and at which spatial and temporal resolution. We categorize all current and important past sensors with respect to application fields relevant for biologists, ecologists, and conservationists. Furthermore, sensor gaps and current challenges for Earth observation with respect to data access and provision are presented.

Monitoring Forest cover at global scale: the JRC approach

Article

Jan 2009

Abstract – Research groups at the Joint Research Centre (JRC) have been heavily involved in the development of methods for monitoring forest cover resources in a global perspective. A JRC project aims at estimating forest cover changes for the periods 1990-2000-2005 based on a systematic sample of medium ,resolution satellite imagery from pan- tropical to sub-regional levels. The project is carried out in a collaborative partnership with FAO by supporting the remote sensing survey of the FAO Forest Resources Assessment 2010 programme. Anoperational system is being developed for the processing and change assessment of multi-temporal (3 dates) 30-m resolution imagery over circa 4,000 sample sites over the tropics. The paper presents the objectives and the status of the project including the steps of data collection, processing chain and pilot study over test sites. The future steps required to develop a full operational system will also be presented. Keywords: Forestry, Change Detection, Sampling, Landsat. 1.,INTRODUCTION Tropical deforestation contributes approximately ,to 20 % of ,the world’s anthropogenic greenhouse gas emissions, mainly through CO2 emissions. Global net carbon flux resulting from land use changes during the 1990s, predominantly deforestation in the tropics, have been estimated by IPCC at 1.6 GtC yr,. However,

Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm

Article

Full-text available

Oct 2003
EARTH INTERACT

The first results of the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous field algorithm's global percent tree cover are presented. Percent tree cover per 500-m MODIS pixel is estimated using a supervised regression tree algorithm. Data derived from the MODIS visible bands contribute the most to discriminating tree cover. The results show that MODIS data yield greater spatial detail in the characterization of tree cover compared to past efforts using AVHRR data. This finer-scale depiction should allow for using successive tree cover maps in change detection studies at the global scale. Initial validation efforts show a reasonable relationship between the MODIS-estimated tree cover and tree cover from validation sites.

Pan-tropical monitoring of deforestation

Article

Full-text available

Oct 2007
ENVIRON RES LETT

This paper reviews the technical capabilities for monitoring deforestation from a pan-tropical perspective in response to the United Nations Framework Convention on Climate Change (UNFCCC) process, which is studying the technical issues surrounding the ability to reduce greenhouse gas emissions from deforestation in developing countries. The successful implementation of such policies requires effective forest monitoring systems that are reproducible, provide consistent results, meet standards for mapping accuracy, and can be implemented from national to pan-tropical levels. Remotely sensed data, supported by ground observations, are crucial to such efforts. Recent developments in global to regional monitoring of forests can contribute to reducing the uncertainties in estimates of emissions from deforestation. Monitoring systems at national levels in developing countries can also benefit from pan-tropical and regional observations, mainly by identifying hot spots of change and prioritizing areas for monitoring at finer spatial scales. A pan-tropical perspective is also required to ensure consistency between different national monitoring systems. Data sources already exist to determine baseline periods in the 1990s as historical reference points. Key requirements for implementing such monitoring programs, both at pan-tropical and at national scales, are international commitment of resources to increase capacity, coordination of observations to ensure pan-tropical coverage, access to free or low-cost data, and standardized, consensus protocols for data interpretation and analysis.

Quantifying changes in the rates of forest clearing in Indonesia from 1990 to 2005 using remotely sensed data sets

Article

Full-text available

Jul 2009
ENVIRON RES LETT

Timely and accurate data on forest change within Indonesia is required to provide government, private and civil society interests with the information needed to improve forest management. The forest clearing rate in Indonesia is among the highest reported by the United Nations Food and Agriculture Organization (FAO), behind only Brazil in terms of forest area lost. While the rate of forest loss reported by FAO was constant from 1990 through 2005 (1.87 Mha yr−1), the political, economic, social and environmental drivers of forest clearing changed at the close of the last century. We employed a consistent methodology and data source to quantify forest clearing from 1990 to 2000 and from 2000 to 2005. Results show a dramatic reduction in clearing from a 1990s average of 1.78 Mha yr−1 to an average of 0.71 Mha yr−1 from 2000 to 2005. However, annual forest cover loss indicator maps reveal a near-monotonic increase in clearing from a low in 2000 to a high in 2005. Results illustrate a dramatic downturn in forest clearing at the turn of the century followed by a steady resurgence thereafter to levels estimated to exceed 1 Mha yr−1 by 2005. The lowlands of Sumatra and Kalimantan were the site of more than 70% of total forest clearing within Indonesia for both epochs; over 40% of the lowland forests of these island groups were cleared from 1990 to 2005. The method employed enables the derivation of internally consistent, national-scale changes in the rates of forest clearing, results that can inform carbon accounting programs such as the Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD) initiative.

Effects of systematic sampling on satellite estimates of deforestation rates

Article

Full-text available

Sep 2009
ENVIRON RES LETT

Options for satellite monitoring of deforestation rates over large areas include the use of sampling. Sampling may reduce the cost of monitoring but is also a source of error in estimates of areas and rates. A common sampling approach is systematic sampling, in which sample units of a constant size are distributed in some regular manner, such as a grid. The proposed approach for the 2010 Forest Resources Assessment (FRA) of the UN Food and Agriculture Organization (FAO) is a systematic sample of 10 km wide squares at every 1 • intersection of latitude and longitude. We assessed the outcome of this and other systematic samples for estimating deforestation at national, sub-national and continental levels. The study is based on digital data on deforestation patterns for the five Amazonian countries outside Brazil plus the Brazilian Amazon. We tested these schemes by varying sample-unit size and frequency. We calculated two estimates of sampling error. First we calculated the standard errors, based on the size, variance and covariance of the samples, and from this calculated the 95% confidence intervals (CI). Second, we calculated the actual errors, based on the difference between the sample-based estimates and the estimates from the full-coverage maps. At the continental level, the 1 • , 10 km scheme had a CI of 21% and an actual error of 8%. At the national level, this scheme had CIs of 126% for Ecuador and up to 67% for other countries. At this level, increasing sampling density to every 0.25 • produced a CI of 32% for Ecuador and CIs of up to 25% for other countries, with only Brazil having a CI of less than 10%. Actual errors were within the limits of the CIs in all but two of the 56 cases. Actual errors were half or less of the CIs in all but eight of these cases. These results indicate that the FRA 2010 should have CIs of smaller than or close to 10% at the continental level. However, systematic sampling at the national level yields large CIs unless the sample size is very large, especially if any sub-national stratification of estimates is required.

Determination of Deforestation Rates of the World's Humid Tropical Forests

Article

Full-text available

Sep 2002
SCIENCE

A recently completed research program (TREES) employing the global imaging capabilities of Earth-observing satellites provides updated information on the status of the world's humid tropical forest cover. Between 1990 and 1997, 5.8 ± 1.4 million hectares of humid tropical forest were lost each year, with a further 2.3 ± 0.7 million hectares of forest visibly degraded. These figures indicate that the global net rate of change in forest cover for the humid tropics is 23% lower than the generally accepted rate. This result affects the calculation of carbon fluxes in the global budget and means that the terrestrial sink is smaller than previously inferred.

Tropical forest cover change in the 1990s and options for future monitoring

Article

Full-text available

Feb 2005
PHILOS T R SOC B

Despite the importance of the world's humid tropical forests, our knowledge concerning their rates of change remains limited. Two recent programmes (FAO 2000 Forest Resources Assessment and TREES II), exploiting the global imaging capabilities of Earth observing satellites, have recently been completed to provide information on the dynamics of tropical forest cover. The results from these independent studies show a high degree of conformity and provide a good understanding of trends at the pan-tropical level. In 1990 there were some 1150 million ha of tropical rain forest with the area of the humid tropics deforested annually estimated at 5.8 million ha (approximately twice the size of Belgium). A further 2.3 million ha of humid forest is apparently degraded annually through fragmentation, logging and/or fires. In the sub-humid and dry tropics, annual deforestation of tropical moist deciduous and tropical dry forests comes to 2.2 and 0.7 million ha, respectively. Southeast Asia is the region where forests are under the highest pressure with an annual change rate of −0.8 to −0.9%. The annual area deforested in Latin America is large, but the relative rate (−0.4 to −0.5%) is lower, owing to the vast area covered by the remaining Amazonian forests. The humid forests of Africa are being converted at a similar rate to those of Latin America (−0.4 to −0.5% per year). During this period, secondary forests have also been established, through re-growth on abandoned land and forest plantations, but with different ecological, biophysical and economic characteristics compared with primary forests. These trends are significant in all regions, but the extent of new forest cover has proven difficult to establish. These results, as well as the lack of more detailed knowledge, clearly demonstrate the need to improve sound scientific evidence to support policy. The two projects provide useful guidance for future monitoring efforts in the context of multilateral environmental agreements and of international aid, trade and development partnerships. Methodologically, the use of high-resolution remote sensing in representative samples has been shown to be cost-effective. Close collaboration between tropical institutions and inter-governmental organizations proved to be a fruitful arrangement in the different projects. To properly assist decision-making, monitoring and assessments should primarily be addressed at the national level, which also corresponds to the ratification level of the multilateral environmental agreements. The Forest Resources Assessment 2000 deforestation statistics from countries are consistent with the satellite-based estimates in Asia and America, but are significantly different in Africa, highlighting the particular need for long-term capacity-building activities in this continent.

Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data

Article

Full-text available

Aug 2008
P NATL ACAD SCI USA

Forest cover is an important input variable for assessing changes to carbon stocks, climate and hydrological systems, biodiversity richness, and other sustainability science disciplines. Despite incremental improvements in our ability to quantify rates of forest clearing, there is still no definitive understanding on global trends. Without timely and accurate forest monitoring methods, policy responses will be uninformed concerning the most basic facts of forest cover change. Results of a feasible and cost-effective monitoring strategy are presented that enable timely, precise, and internally consistent estimates of forest clearing within the humid tropics. A probability-based sampling approach that synergistically employs low and high spatial resolution satellite datasets was used to quantify humid tropical forest clearing from 2000 to 2005. Forest clearing is estimated to be 1.39% (SE 0.084%) of the total biome area. This translates to an estimated forest area cleared of 27.2 million hectares (SE 2.28 million hectares), and represents a 2.36% reduction in area of humid tropical forest. Fifty-five percent of total biome clearing occurs within only 6% of the biome area, emphasizing the presence of forest clearing “hotspots.” Forest loss in Brazil accounts for 47.8% of total biome clearing, nearly four times that of the next highest country, Indonesia, which accounts for 12.8%. Over three-fifths of clearing occurs in Latin America and over one-third in Asia. Africa contributes 5.4% to the estimated loss of humid tropical forest cover, reflecting the absence of current agro-industrial scale clearing in humid tropical Africa. • deforestation • humid tropics • remote sensing • change detection • monitoring

Model-Assisted Survey Sampling.

Article

Dec 1994

Sampling techniques. 3rd ed

Article

Jan 1977

William G. Cochran

Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data

Article

Jan 2008

Global Forest Resources Assessment 2010. Options And Recommendations For A Global Remote Sensing Survey Of Forests

Article

Jan 2007

R M Ridder

Sampling for forest cover change assessment at the pan-tropical scale

Article

Apr 2000

A major component of the Joint Research Centre's TREES-II project is the assessment of deforestation rates in moist tropical regions for the period 1992 to 1997 using a statistical sample of fine spatial resolution satellite image pairs. It is widely recognized that spatial stratification can reduce the variance of estimates in spatial sampling designs. However, at the pan-tropical scale little reliable spatial information is available to stratify on the basis of deforestation rates. This paper describes a novel sampling scheme for assessing tropical deforestation rates. Stratification is performed using percentages of forest area (derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data) and areas of known deforestation activity (elucidated from expert consultation) estimated for each sampling unit. Sample site selection is performed by using a sample frame based on a tessellation of hexagons on a sphere. This approach allows for a sensor-independent sample from which unbiased estimators and error variance may be computed. The scheme is currently in the implementation phase for the tropical belt, but can be extended to the global scale.

Gross forest cover loss in temperate forests: Biome-wide monitoring results using MODIS and Landsat data

Article

Dec 2009
J APPL REMOTE SENS

The temperate forest is a complex biome due to the diversity of forest types, forest cover change dynamics and forest use management practices. While temperate forests play an important role in the global carbon cycle, their net carbon exchange is uncertain. Quantifying forest cover change is an important step in documenting disturbance regimes and carbon exchange estimates. Biome-wide gross forest cover loss was estimated using a probability-based sampling approach that integrated moderate and high spatial resolution satellite data sets. Area of gross forest cover loss from 2000 to 2005 within the temperate forest biome is estimated to be 1.03% of the total biome area, or 18.41 Mha. Estimated forest cover loss represented a 3.5% reduction in year 2000 forest area. About 68% of the total forest cover loss occurred in Eastern North America and in Europe. The mid-latitude forests of the United States exhibited the highest forest cover loss rates within the biome. Biome-wide rate of gross forest cover loss gradually increased from 2001 to 2005. The lowest change was detected in 2004, followed by the year of the highest change over the 5-year period. The regional forest cover change dynamics were confirmed by official forest fire and timber production statistics. The validation of the MODIS-based product demonstrated its efficiency in forest cover mapping and monitoring. Forest cover change monitoring using the approach presented should bring greater understanding on forest cover dynamics in temperate forests and enable improved carbon accounting.

Desirable design characteristics for long-term monitoring of ecological variables

Article

Dec 1996

Long-term environmental monitoring places a set of demands on a sampling strategy not present in a survey designed for a single time period. The inevitability that a sample will become out of date must be a dominant consideration in planning a long-term monitoring programme. The sampling strategy must be able to accommodate periodic frame update and sample restructuring in order to address changes in the composition of the universe and changes in the perception of issues leading to new questions and concerns. The sampling strategy must be capable of adapting to such changes while maintaining its identification as a probability sample and its capacity to detect trends that span the update occasions. These issues are examined with respect to sub-population estimation, post-stratification via conditioning, and sample enlargement and reduction. Design features that involve complex sample structure create potentially serious difficulties, whereas an equal probability design permits greater adaptability and flexibility. Structure should be employed sparingly and in awareness of its undesirable effects.

Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts

Article

May 2008
REMOTE SENS ENVIRON

Accurate land cover change estimates are among the headline indicators set by the Convention on Biological Diversity to evaluate the progress toward its 2010 target concerning habitat conservation. Tropical deforestation is of prime interest since it threatens the terrestrial biomes hosting the highest levels of biodiversity. Local forest change dynamics, detected over very large extents, are necessary to derive regional and national figures for multilateral environmental agreements and sustainable forest management. Current deforestation estimates in Central Africa are derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited areas. Whereas the first approach cannot detect small forest changes widely spread across a landscape, operational costs limit the mapping extent in the second approach. This research developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques. A multi-date segmentation is obtained by grouping pixels with similar land cover change trajectories which are then classified by unsupervised procedures. The interactive part of the processing chain is therefore limited to land cover class labelling of object clusters. The combination of automated image processing and interactive labelling renders this method cost-efficient. The approach was operationally applied to the entire Congo River basin to accurately estimate deforestation at regional, national and landscape levels. The survey was composed of 10 × 10 km sampling sites systematically-distributed every 0.5° over the whole forest domain of Central Africa, corresponding to a sampling rate of 3.3%. For each of the 571 sites, subsets were extracted from both Landsat TM and ETM+ imagery acquired in 1990 and 2000 respectively. Approximately 60% of the 390 cloud-free samples do not show any forest cover change. For the other 165 sites, the results are depicted by a change matrix for every sample site describing four land cover change processes: deforestation, reforestation, forest degradation and forest recovery. This unique exercise estimates the deforestation rate at 0.21% per year, while the forest degradation rate is close to 0.15% per year. However, these figures are less reliable for the coastal region where there is a lack of cloud-free imagery. The results also show that the Landscapes designated after 2000 as high priority conservation zones by the Congo Basin Forest Partnership had undergone significantly less deforestation and forest degradation between 1990 and 2000 than the rest of the Central African forest.

Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss

Article

Sep 2008
REMOTE SENS ENVIRON

Estimation of forest cover change is important for boreal forests, one of the most extensive forested biomes, due to its unique role in global timber stock, carbon sequestration and deposition, and high vulnerability to the effects of global climate change. We used time-series data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to produce annual forest cover loss hotspot maps. These maps were used to assign all blocks (18.5 by 18.5 km) partitioning the boreal biome into strata of high, medium and low likelihood of forest cover loss. A stratified random sample of 118 blocks was interpreted for forest cover and forest cover loss using high spatial resolution Landsat imagery from 2000 and 2005. Area of forest cover gross loss from 2000 to 2005 within the boreal biome is estimated to be 1.63% (standard error 0.10%) of the total biome area, and represents a 4.02% reduction in year 2000 forest cover. The proportion of identified forest cover loss relative to regional forest area is much higher in North America than in Eurasia (5.63% to 3.00%). Of the total forest cover loss identified, 58.9% is attributable to wildfires. The MODIS pan-boreal change hotspot estimates reveal significant increases in forest cover loss due to wildfires in 2002 and 2003, with 2003 being the peak year of loss within the 5-year study period. Overall, the precision of the aggregate forest cover loss estimates derived from the Landsat data and the value of the MODIS-derived map displaying the spatial and temporal patterns of forest loss demonstrate the efficacy of this protocol for operational, cost-effective, and timely biome-wide monitoring of gross forest cover loss.

Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery

Article

Mar 2010
ISPRS J PHOTOGRAMM

A global systematic sampling scheme has been developed by the UN FAO and the EC TREES project to estimate rates of deforestation at global or continental levels at intervals of 5 to 10 years. This global scheme can be intensified to produce results at the national level. In this paper, using surrogate observations, we compare the deforestation estimates derived from these two levels of sampling intensities (one, the global, for the Brazilian Amazon the other, national, for French Guiana) to estimates derived from the official inventories. We also report the precisions that are achieved due to sampling errors and, in the case of French Guiana, compare such precision with the official inventory precision.

A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin

Article

May 2008
REMOTE SENS ENVIRON

In this paper we demonstrate a new approach that uses regional/continental MODIS (MODerate Resolution Imaging Spectroradiometer) derived forest cover products to calibrate Landsat data for exhaustive high spatial resolution mapping of forest cover and clearing in the Congo River Basin. The approach employs multi-temporal Landsat acquisitions to account for cloud cover, a primary limiting factor in humid tropical forest mapping. A Basin-wide MODIS 250 m Vegetation Continuous Field (VCF) percent tree cover product is used as a regionally consistent reference data set to train Landsat imagery. The approach is automated and greatly shortens mapping time. Results for approximately one third of the Congo Basin are shown. Derived high spatial resolution forest change estimates indicate that less than 1% of the forests were cleared from 1990 to 2000. However, forest clearing is spatially pervasive and fragmented in the landscapes studied to date, with implications for sustaining the region's biodiversity. The forest cover and change data are being used by the Central African Regional Program for the Environment (CARPE) program to study deforestation and biodiversity loss in the Congo Basin forest zone. Data from this study are available at http://carpe.umd.edu.

Stratified sampling of satellite images with a systematic grid of points

Article

Nov 2005
ISPRS J PHOTOGRAMM

Francisco Javier Gallego Pinilla

Sampling satellite images presents some specific characteristics: images overlap and many of them fall partially outside the studied region. A careless sampling may introduce an important bias. This paper illustrates the risk of bias and the efficiency improvements of systematic, pps (probability proportional to size) and stratified sampling.A sampling method is proposed with the following criteria: (a) unbiased estimators are easy to compute; (b) it can be combined with stratification; (c) within each stratum, sampling probability is proportional to the area of the sampling unit; and (d) the geographic distribution of the sample is reasonably homogeneous. Thiessen polygons computed on image centres are sampled through a systematic grid of points. The sampling rates in different strata are tuned by dividing the systematic grid into subgrids or replicates and taking for each stratum a certain number of replicates.The approach is illustrated with an application to the estimation of the geometric accuracy of Image2000, a Landsat ETM+ mosaic of the European Union.

A comparison of sampling designs for estimating deforestation from Landsat imagery: A case study of the Brazilian Legal Amazon

Article

Nov 2009
REMOTE SENS ENVIRON

Three sampling designs — simple random, stratified random, and systematic sampling — are compared on the basis of precision of estimated loss of intact humid tropical forest area in the Brazilian Legal Amazon from 2000 to 2005. MODIS-derived deforestation is used to partition the study area into strata to intensify sampling within forest clearing hotspots. The precision of the estimator of deforestation area for each design is calculated from a population of wall-to-wall PRODES deforestation data available for the study area. Both systematic and stratified sampling yield smaller standard errors than simple random sampling, and the stratified design has smaller standard errors than the systematic design at each sample size evaluated. The results of this case study demonstrate the utility of a stratified design based on MODIS-derived deforestation data to improve precision of the estimated loss of intact forest area as estimated from sampling Landsat imagery.

Comparing annual MODIS and PRODES forest cover change data for advancing monitoring of Brazilian forest cover

Article

Oct 2008
REMOTE SENS ENVIRON

Annual forest cover loss indicator maps for the humid tropics from 2000 to 2005 derived from time-series 500 m data from the MODerate Resolution Imaging Spectroradiometer (MODIS) were compared with annual deforestation data from the PRODES (Amazon Deforestation Monitoring Project) data set produced by the Brazilian National Institute for Space Research (INPE). The annual PRODES data were used to calibrate the MODIS annual change indicator data in estimating forest loss for Brazil. Results indicate that MODIS data may be useful in providing a first estimate of national forest cover change on an annual basis for Brazil. When directly compared with PRODES change at the MODIS grid scale for all years of the analysis, MODIS change indicator maps accounted for 75% of the PRODES change. This ratio was used to scale the MODIS change indicators to the PRODES area estimates. A sliding threshold of percent PRODES forest and 2000 to 2005 deforestation classes per MODIS grid cell was used to match the scaled MODIS to the official PRODES change estimates, and then to differentiate MODIS change within various sub-areas of the PRODES analysis. Results indicate significant change outside of the PRODES-defined intact forest class. Total scaled MODIS change area within the PRODES historical deforestation and forest area of study is 120% of the official PRODES estimate. Results emphasize the importance of synoptic monitoring of all forest change dynamics, including the cover dynamics of intact humid forest, regrowth, plantations, and cerrado tree cover assemblages. Results also indicate that operational MODIS-only forest cover loss algorithms may be useful in providing near-real time areal estimates of annual change within the Amazon Basin.

Moel Assisted Survey Sampling

Article

Jul 1993

Incl. bibliographical references, index

Using shade fraction image segmentation to evaluate deforestation in Landsat Thematic Mapper images of the Amazon Region

Article

Feb 1998

Image segmentation based on the shade fraction of a Landsat TM image was effective in measuring the areal extent of Amazonian deforestation. The shade fraction image derived from spectral mixture models was related to the forest canopy structure. Dense tropical forest have a medium proportion of shade within their canopy while deforested areas (bare soil, pasture, and/or regrowth)have a comparatively small proportion. Comparison of image segmentation results with conventional techniques showed visual agreement. Even though additional tests are necessary to validate this approach for large areas, the technical soundness of the approach has been demonstrated. Pages: 535-541

FAO (Food and Agriculture Organization of the United Nations) in 1981

Article

Mapping the world's intact forest landscapes by remote sensing Gross forest cover loss in temperate forests: Biome-wide monitoring results using MODIS and Landsat data Sampling for forest cover change assessment at the pan-tropical scale

Jan 2000

Landsat Combining
P Potapov
A Yaroshenko
S Turubanova
M Dubinin
L Laestadius
C Thies

Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss. Remote Sensing of Environment, 112, 3708−3719. Potapov, P., Yaroshenko, A., Turubanova, S., Dubinin, M., Laestadius, L., Thies, C., et al. (2008). Mapping the world's intact forest landscapes by remote sensing.Ecology and Society, 13(2) [online] URL:. http://www.ecologyandsociety.org/vol13/iss2/art51/ Potapov, P., Hansen, M. C., Stehman, S. V., Pittman, K., & Turubanova, S. (2009). Gross forest cover loss in temperate forests: Biome-wide monitoring results using MODIS and Landsat data. Journal of Applied Remote Sensing, 3, 1−23. Richards, T., Gallego, J., & Achard, F. (2000). Sampling for forest cover change assessment at the pan-tropical scale. International Journal of Remote Sensing, 21, 1473−1490.

Methodological guidance for activities relating to reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries

Jan 2010

UNFCCC (2010). Methodological guidance for activities relating to reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries. Report of the Conference of the Parties fifteenth session, held at Copenhagen from 7 December to 18 December. Draft decision -/CP.15.

Monitoramento da Floresta Amazônica Brasileira por Satélite. Projeto PRODEShttp

Jan 2008

INPE (2008). Monitoramento da Floresta Amazônica Brasileira por Satélite. Projeto PRODEShttp://www.obt.inpe.br/prodes/.

Gross forest cover loss in temperate forests: Biome-wide monitoring results using MODIS and Landsat data

Jan 2008
J APPL REMOTE SENS
1-23

P Potapov
A Yaroshenko
S Turubanova
M Dubinin
L Laestadius
C Thies

Potapov, P., Yaroshenko, A., Turubanova, S., Dubinin, M., Laestadius, L., Thies, C., et al. (2008). Mapping the world's intact forest landscapes by remote sensing.Ecology and Society, 13(2) [online] URL:. http://www.ecologyandsociety.org/vol13/iss2/art51/ Potapov, P., Hansen, M. C., Stehman, S. V., Pittman, K., & Turubanova, S. (2009). Gross forest cover loss in temperate forests: Biome-wide monitoring results using MODIS and Landsat data. Journal of Applied Remote Sensing, 3, 1−23.

Forest Resources Assessment 1990: Survey of tropical forest cover and study of change processes. Forestry Paper No. 130. Rome: Food and Agriculture Organization of the United

Jan 1996

FAO (1996). Forest Resources Assessment 1990: Survey of tropical forest cover and study of change processes. Forestry Paper No. 130. Rome: Food and Agriculture Organization of the United Nationshttp://www.fao.org/docrep/007/w0015e/ w0015e00.htm FAO (2001). Global Forest Resources Assessment 2000: Main report. Forestry Paper No. 140. Rome: Food and Agriculture Organization of the United Nationsftp://ftp.fao. org/docrep/fao/003/Y1997E/FRA%202000%20Main%20report.pdf

Monitoramento da Floresta Amazônica Brasileira por Satélite

Jan 2008

Inpe

INPE (2008). Monitoramento da Floresta Amazônica Brasileira por Satélite. Projeto PRODEShttp://www.obt.inpe.br/prodes/.

Adapting a global stratified random sample for regional estimation of forest cover change derived from satellite imagery

Abstract

No full-text available

Recommendations

Quantifying the trade-off between cost and precision in estimating area of forest loss and degradati...

A comparison of sampling designs for estimating deforestation from Landsat imagery: A case study of...