Article

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

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Abstract

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.

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... A stratified nested sampling design using optimal allocation was used to determine the number of 10 Â 10 km samples blocks per stratum to be further analyzed using time-series of Landsat data (Broich et al., 2009;Stehman et al., 2011). A total sample size encompassing 5e10% of the total number of blocks was considered a proper representative and manageable sample size range, based on the results from similar global and regional studies (Broich et al., 2009), as well as the computing capacity and man-hours effort needed. ...
... First, sample sizes were calculated for each stratum based on Neyman Optimal allocation (Broich et al., 2009;Stehman et al., 2011). The optimal allocation was determined using per-stratum standard deviations of the percentage of change of all blocks within each stratum. ...
... ,Hansen, Roy et al. (2008),Hansen, Shimabukuro et al. (2008),Hansen, Stehman et al. (2008),Hansen et al. (2009), Broich et al. (2009 andStehman et al. (2011). The method consists of three steps: i) derivation of a change probability map showing general trends of forest loss, ii) statistical design for selecting 10 Â 10 km sample blocks, iii) deforestation mapping in 10 Â 10 km sample blocks using Landsat time series, and iv) derivation of a deforestation model based on a regression analysis. ...
Article
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.
... This sampling design was chosen because an existing sample of 15 blocks had previously been obtained by Hansen et al. [1] as part of a global humid tropical forest biome study. Because of the considerable effort required to obtain the Landsat-derived forest cover area and gross forest cover loss per sample block, it was decided to retain this existing sample of 15 blocks and to augment it by an additional 20 sample blocks from Malaysia selected by a stratified protocol described by Stehman et al. [29]. The strata for the 2000-2005 design were defined by Hansen et al. [1] and were based on MODIS-derived percent area of forest cover loss, as well as data from the Intact Forest Landscapes project (IFL, defined as an unfragmented area of at least 500 km 2 of forest minimally influenced by human economic activity) [30] and the VCF tree cover map [26]. ...
... The stratified random design implemented for 2000-2005 added 20 sample blocks to an existing sample of 15 blocks in Malaysia. Stratified random sampling is often implemented in practice when satellite imagery is used as the basis of a land-cover monitoring strategy [31] and the ability to augment an existing global stratified sample to increase the sample size within a country is an attractive feature of stratified sampling [29]. However, the stratified design for Malaysia turned out to be ineffective relative to simple random sampling. ...
... Similar to any sampling design that depends on an auxiliary variable to improve precision, if the auxiliary variable (e.g., forest cover loss derived from AVHRR) is not strongly associated with the target variable (e.g., forest cover loss derived from Landsat), the πpx design will not yield a substantially smaller standard error relative to an equal probability sampling design. The πpx design is more complex to implement and to analyze than the stratified design, and it would be much more difficult to augment the πpx design after the initial sample had been selected, whereas the stratified random sample is easy to augment [29]. The difficulty to augment the πpx design would suggest that it is best applied to a single purpose use as was the case for Malaysia in 1990-2000 rather than as the base design for a large-area (e.g., global) forest monitoring strategy. ...
Article
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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.
... Producing forest-cover maps characterized by both a detailed forest-type nomenclature and a sufficient accuracy is still difficult because of technical limitations related to supervised classification (Stehman et al. 2011;Kuemmerle et al. 2006). ...
... To address this issue, a common process is to stratify the study area into subregions (Cai et al. 2011;Stehman 2009). The purpose of the stratification is to reduce class signature heterogeneity, minimize intra-class variability, and maximize inter-class separability (Gertner et al. 2007;Delincé 2001;Stehman et al. 2011;Gallego and Stibig 2013). It has been widely used to improve the accuracy of image classification (Homer et al. 1997;Lillesand 1996;Pettinger 1982). ...
... A wide and heterogeneous territory is thus commonly partitioned in bioclimatic zones (Reese et al. 2002;Homer et al. 2004). The strata are delineated according to landscape, soil, vegetation, and climatic conditions homogeneity (Stehman et al. 2011;Broich et al. 2009;Congalton 1991;Homer et al. 2004). This process is suitable to map forest cover, where spatial distribution largely depends on these factors (Cai et al. 2011). ...
Article
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.
... Крім цього в дослідженнях лісового покриву звертається увага на універсальність територіальних схем відбору, які організовують у вигляді регулярної прямокутної сітки, побудованої через однакові інтервали широти та довготи (Stehman, Hansen, Broich, & Potapov, 2011). Зокрема, починаючи з 1990 року FAO в програмі FRA (Forest Resources Assessment) для цього використовує прямокутну сітку розміром 10 × 10 км, що розташовуються на перетині географічної довготи і широти з інтервалом 1º. ...
... Зокрема, починаючи з 1990 року FAO в програмі FRA (Forest Resources Assessment) для цього використовує прямокутну сітку розміром 10 × 10 км, що розташовуються на перетині географічної довготи і широти з інтервалом 1º. Такі системи також можна трансформувати в локальні територіальні основи вибірки, зменшуючи зазначений інтервал (Stehman, Hansen, Broich, & Potapov, 2011). ...
Book
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... where D is the total area of the study region. Rather than the usual variance estimation of the mean for systematic sampling [26] we used a local estimation of the variance as follows: ...
Article
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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.
... The weight of the sample unit is w i and m is the sum of the sample weights. The usual variance estimation of the mean is known to have a positive bias (Stehman et al., 2011). Alternative estimators based on a local estimation of the variance have been shown to reduce the bias. ...
Article
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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.
... It is often questionable how well the reference data used for the accuracy assessment represents the whole area of interest. A sampling based framework that is the standard approach in forest inventories is seldom applied in satellite based tropical forest mapping [31]. The poor resolution of the reference data is a source of additional uncertainty as regards the reported accuracies [32]. ...
Article
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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 weight of the sample unit is w i and m is the sum of the sample weights. The usual variance estimation of the mean is known to have a positive bias (Stehman et al., 2011). Alternative estimators based on a local estimation of the variance have been shown to reduce the bias. ...
Article
Full-text available
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.
... These sample sites were selected from the systematic sample database of the global remote sensing survey of the FAO FAO and JRC 2012). Stratified sampling has been demonstrated to be a robust approach for forest cover monitoring (Richards, Gallego, and Achard 2000;Stehman 2001;Stehman et al. 2011). However our sub-sample (of the systematic sample) was selected empirically in order to incorporate challenging areas from the point of view of land-cover mapping within the TerraNorte map and to represent the large latitudinal, longitudinal, and climatic heterogeneity that characterizes the forested landscape across the Russian Federation. ...
Article
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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.
... The usual variance estimation of the mean is known to have a positive bias (Stehman et al., 2011). Alternative estimators based on a local estimation of the variance have been shown to reduce the bias. ...
... Ground-truth data is used to evaluate how well the classification represents the real 156 world. A random stratified sample design was used for assessing change detection 157 accuracy [41]. Table 2, Table 3, Table 4, and Table 7 and Figure 4, respectively. ...
Preprint
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.
... Nevertheless, very few have actually quantified the contribution of the remotely sensed data to improve the precision of the estimates that would allow a comparison of costeffectivity among different techniques and between remote sensing and conventional field-based estimation. A few and recent exceptions include studies by Broich, Stehman, Hansen, Potapov, and Shimabukuro (2009) ;Stehman, Hansen, Broich, and Potapov (2011);Vibrans, McRoberts, Moser, and Nicoletti (2013); Potapov et al. (2014), and Sannier, McRoberts, Fichet, and Makaga (2014) who all studied stringent designs and efficiencies related to use of remotely sensed data from Landsat or existing forest or deforestation maps derived from remotely sensed data to quantify forest area or areas of change. For example found great improvements in precision of estimates of net deforestation by using mainly Landsat data to assist in the estimation. ...
Article
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.
... Such analysis is important for selecting optimal number of sampling sites. In addition, a sampling design stratified by a variable correlated with the target variable has been demonstrated to obtain precise regional estimates (Stehman et al., 2011). For example, the land use has been considered to play a significant role in the spatio-temporal pattern of soil moisture (Jia et al., 2013;Wang et al., 2013). ...
Article
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.
... One hundred validation blocks were chosen for each study area based on stratified random selection. Following Potapov et al. (2014), Broich et al. (2009);Stehman et al. (2011) andPortillo-Quintero et al. (2012), the Neyman optimal allocation formula was used to calculate the proportion of blocks per strata to further evaluate in a second stage. Optimal allocation (proportion of blocks) was determined using perstratum standard deviations of the percentage of change of all blocks in each stratum. ...
Article
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.
... Selection of a sampling strategy is important including the selection of sampling unit, sampling design, and sample size (Schelin & Sjöstedt-De Luna, 2014). In this study, we used a stratified random sampling (Priebe et al., 2001;Rougier, Puissant et al., 2016;Stehman, Hansen et al., 2011) with a total sample of 250 samples for the field test. After conducting a field test, the calculation of accuracy testing was done based on a pixel-based approach, which is a calculation based on pixel values. ...
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... Selection of a sampling strategy is important including the selection of sampling unit, sampling design, and sample size (Schelin & Sjöstedt-De Luna, 2014). In this study, we used a stratified random sampling (Priebe et al., 2001;Rougier, Puissant et al., 2016;Stehman, Hansen et al., 2011) with a total sample of 250 samples for the field test. After conducting a field test, the calculation of accuracy testing was done based on a pixel-based approach, which is a calculation based on pixel values. ...
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
Article
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.
Article
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.
Article
Incl. bibliographical references, index
Article
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
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
  • 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.
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Monitoramento da Floresta Amazônica Brasileira por Satélite. Projeto PRODEShttp
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
  • 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.
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