Global Forest Change Research Articles

Mining impacts on forest cover change in a tropical forest using remote sensing and spatial information from 2001–2019: A case study of Odisha (India)

Worldwide mining activities are one of the major anthropogenic activities that have caused high forest cover loss (FCL). In this study, we have quantified FCL in Odisha State due to mining activities analyzing Hansen Global Forest Change (HGFC) time series data for the period of 2001–2019 in Google Earth Engine platform. Our analysis suggests that Nabarangpur, Puri, Kendrapara, and Kalahandi districts lost more than 20% of their forest cover during this period. Rayagada and Koraput were the top two districts that recorded the highest FCL with mean change rates of 13.81 km2/year and 7.17 km2/year, respectively. The results point out that mining operations have grown in recent years in Odisha State, and the increase in these activities has contributed to the increase in FCL. This study offers a cost-effective methodology to monitor FCL in mining areas which will eventually contribute to the protection of forest biodiversity and forest dwelling tribal population.

Tree Cover Loss in the Mediterranean Region—An Increasingly Serious Environmental Issue

The Mediterranean Region currently faces major environmental issues that require constant analysis and monitoring. This study presents a thorough approach based on the application of Landsat imagery from Global Forest Change during 2001–2019. Spatial distribution mapping was one of the objectives of the study. We approached the analysis of tree cover loss areas by analyzing the cumulative tree cover loss and Tree Cover Loss Rate. This indicator offers information about the trend of tree cover loss in each Mediterranean country. A total of 581 Mha of deforested area was mapped during the analyzed period. Analysis was further supplemented by some statistical operations (distributions shown via histograms, validation via Shapiro–Wilk normality test, and testing via one-sample t-test). Agricultural expansion, intense forest fires, illegal logging, overgrazing (especially in the northern part of Africa), and extensive livestock farming have influenced the Mediterranean forest ecosystem’s stability. The continuation of these activities could cause extreme climatic events, severe degradation, and desertification.

Spatio-temporal analysis of remotely sensed forest loss data in the Cordillera Administrative Region, Philippines

The Cordillera Administrative Region (CAR) in the Philippines is among the last forest frontiers in the country and is also home to 13 major watersheds in Northern Luzon that supply irrigation and hydroelectricity to other regions. However, it is faced with the deterioration of the quality of its watersheds due to forest loss driven mainly by agricultural expansion and illegal logging. Thus, this study was conducted to analyze the spatial and temporal patterns of forest loss that could serve as a basis for policy decisions. Also, this paper determined the strength of relationships using Pearson’s correlation coefficient (r) between forest loss and seven independent variables, which includes forest cover, agricultural areas, built-up, road network, and socio-economic data. This study utilized the Hansen Global Forest Change (HGFC), a Landsat-derived dataset from 2001 to 2019. Results revealed that 70,925 hectares (ha) of forest loss were detected with an annual deforestation rate of 3,744 ha/year across the region. Based on the validation, the accuracy of the HGFC data is 72%, but great caution should be observed when using the data with less than 0.2 ha due to very low accuracy. On a region-wide analysis, only the forest cover had a strong association with the forest loss with a computed r-value of 0.78. Conversely, on a provincial level, the explanatory variables had a strong to moderately strong correlation with deforestation. Hence, immediate, science-based, and sustained regional and multi-stakeholder efforts are necessary to conserve and protect the remaining forest cover in the region.

Does leakage exist in China's typical protected areas? Evidence from 13 national nature reserves.

There is profound interest in knowing the degree to which the effectiveness of China's nature reserves, and whether leakage is common around the reserves, in the face of the most drastic conflicts between conservation and development in the world. To answer these questions, we employed the Landsat-derived Global Forest Change Dataset with 30-m resolution to examine forest change patterns during 2001 and 2017 both inside and outside of 13 typically national nature reserves in China. The average forest loss rates inside the reserves were significantly lower than those of outside the reserves (i.e., both in buffer and landscape zones), suggesting the success in protecting forest of these reserves in China. We found that the protection practice reduced approximately 10% of deforestation. Protection efficiency may be substantially overestimated (about 13-43%) if failing to control the related variables, such as altitude, climate, and human interference. The forest loss rates in the buffer zones were not significantly higher than those in the broader landscape zones, suggesting that leakage is not a frequent occurrence in the buffer zones of the reserves. However, the forest loss rates showed a slightly increasing tendency from 2001 to 2017, the loss rates increased gradually from inside to their outside buffer zones, and leakage was observed in certain zones of some years for most of the reserves. The conversions of forest to grassland and cultivated land were the primary trajectories of forest loss both inside and outside of the reserves. Though the leakage is not universal in the reserves across the country, forest loss rates are much higher in the buffer zones than those inside the reserves, resulting in increased insulation of the reserves that could undermine the provisioning of ecosystem services and the biodiversity conservation efficiency.

The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019

Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China land cover dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics in China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China's land-use/cover datasets (CLUDs) and visually interpreted samples from satellite time-series data, Google Earth and Google Maps. Using 335 709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial–temporal filtering and logical reasoning to further improve the spatial–temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5463 visually interpreted samples. A further assessment based on 5131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC and GlobeLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China's LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease in cropland (−4.85 %) and grassland (−3.29 %), and increase in forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g. Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at https://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).

Integration of Sentinel-2 Derived Spectral Indices and In-situ Forest Inventory to Predict Forest Biomass


 Forest biomass estimation is the central part of sustainable forest management to assess carbon stocks and carbon emissions from forest ecosystem. Sentinel-2 is state-of-art sensor with refined spatial and recurrent temporal resolution data. The present study explored the potential of Sentinel-2 derived vegetation indices for above ground biomass prediction using four regression models (linear, exponential, power and logarithmic). Sentinel-2 indices includes Global environmental monitoring index, transformed normalized difference vegetation index, normalized difference water index, normalized difference infrared index and red-edge normalized difference vegetation index. The performances of Sentinel-2 indices were assessed by simple single variable (index) based regression for GEMI, TNDVI, NDII, NDWI and RENDVI versus AGB values. Further, stepwise linear regression was also developed in which used all indices entered into stepwise selection and the best index was selected in the final model. Results showed that linear model of all indices performance best compared to the rest three models and R2 values 0.12, 0.39, 0.46, 0.44 and 0.37 for Global environmental monitoring index, transformed normalized. Vegetation index, normalized difference water index, infrared index and red-edge vegetation index, respectively. Normalized difference water index was considered the best index among five computed indices in simple linear as well as in stepwise linear regression, whereas rest of the indices were removed because they were not significant under the stepwise criteria. Further, the accuracy of normalized difference water index model was determined by root mean square error and final prediction model has 28.27 t/ha error for both simple linear and stepwise linear regression. Therefore, normalized difference water index was selected for biomass mapping and resultant biomass showed up to 339 t/ha in the study area. The resultant biomass map also showed consistency with global datasets which include global forest canopy height and global forest tree cover change maps. The study suggest that Sentinel-2 product has great potential to estimate above ground biomass with accuracy and can be used for large scale mapping in combination with national forest inventory for carbon emission accounting.
 

Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems

This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.

Identifying economically relevant forest types from global satellite data

Satellite data offer a transformative view of global forest resources but observe a highly restricted set of forest attributes, limiting both theoretical and applied research. In response to this problem, we propose the use of ‘economic forests’ as analytical units and show how key characteristics of forests that determine human decision-making can be retrieved by combining open earth observation datasets. To operationalize the economic forest approach, we draw on classical microeconomic theory (von Thünen's land use model) to identify two economically relevant variables (market accessibility and forest productivity) that can be proxied in open earth observation data. We then post-process the Global Forest Change dataset (v1.5) to produce wall-to-wall maps of forest harvest for the period 2000–2015, obtain proxy measures of our two variables for a stratified random sample, and classify the resulting dataset of global forest harvest into categories of economic forest using Gaussian Mixture Models. We map the resulting categories, demonstrating a scalable and low-cost approach to analyzing global forest resources which captures key features that determine economic use. We employ this approach to empirically test Hyde's tripartite typology of global forest resources, circumventing the data challenge posed by the theoretical importance of spatial value functions, and find evidence that an expanded typology is required. We also produce a first approximation of Earth's economically inaccessible forests, drawing on previous mapping of Intact Forest Landscapes. To demonstrate the value of our approach for knowledge-discovery, we allocate FAOSTAT-reported timber supply for 23 European countries to the categories of economic forests we identify and employ these data to analyze the Heckscher-Ohlin-Vanek relationship in forestry. This exercise reveals plausible and significant relationships between production structure and comparative advantage that are obscured by aggregated data, providing direct evidence of the value of our approach. We conclude by discussing potential applications of the economic forests concept.

The Three Indices Three Dimensions (3I3D) algorithm: a new method for forest disturbance mapping and area estimation based on optical remotely sensed imagery

ABSTRACT Although estimating forest disturbance area is essential in the context of carbon cycle assessments and for strategic forest planning projects, official statistics are currently not available in several countries. Remotely sensed data are an efficient source of auxiliary information for meeting these needs, and multiple algorithms are commonly used worldwide for this purpose. However, both more accurate maps and precise area estimates are strongly required, especially in Mediterranean ecosystems, and scientific research in this topic area is anything but concluded. In this study, we present the new Three Indices Three Dimensions (3I3D) algorithm for the automated prediction of forest disturbances using statistical analyses of Sentinel-2 data. We tested 3I3D in Tuscany, Italy, for the year 2016, and we compared the results to those obtained using the Global Forest Change Map (GFC), LandTrendr (LT), and the Two Thresholds Method (TTM). The 3I3D map was the most accurate (omissions = 27%, commissions = 30%) followed by TTM (omissions = 35%, commissions = 39%), LT (omissions = 41%, commissions = 43%) and lastly GFC with slightly fewer omissions than LT (39%) but with many more commissions (69%). We also presented a probability sampling framework to estimate the forest harvested area using a model-assisted estimator that can be used at an operational level to produce large-scale statistics. 3I3D and TTM produced the smallest standard errors of the area estimates (8%) followed by LT (13%) and GFC (17%).

30 m annual land cover and its dynamics in China from 1990 to 2019

Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China Land Cover Dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics of China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China’s Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Using 335,709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial-temporal filtering and logical reasoning to further improve the spatial-temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5,463 visually-interpreted samples. A further assessment based on 5,131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC, and GlobaLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China’s LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease of cropland (−4.85 %) and grassland (−3.29 %), increase of forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g., Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at http://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).

Improved Mapping of Long-Term Forest Disturbance and Recovery Dynamics in the Subtropical China Using All Available Landsat Time-Series Imagery on Google Earth Engine Platform

After implementations of many economic and forestry policies during recent 30 years, forest cover in China has experienced significant changes. The fine scale and long-term information on forest temporary (disturbance) loss, persistent loss (deforestation), and gains (afforestation/reforestation) will provide a guide for forest management and policies at regional scale. Using Jiangxi Province as a pilot study region, here we assessed forest cover dynamics in the subtropical China during 1986–2019 by integrating time-series Landsat images, Landsat-based Detection of Trends in Disturbance and Recovery algorithm and Random Forest classifier on the Google Earth Engine Platform. The accuracy assessment indicated a high overall accuracy (>90%) and Kappa coefficient (>0.9) for both forest gain and loss detections as evaluated against various sources of plot-level sample data and the existing global forest change product. The total forest loss area was 18 697.79 km2, with persistent loss area of 3394.31 km2 due to land conversion during 1986–2019, while persistent (net) forest gain area was 45 656.96 km2, accounting for 57.70% of the forest area in 1986. Forest loss area exhibited large interannual variations, but showed a general increase trend from 1986 to 2019. The annual variation patterns of forest gain and loss area were associated with the changes in forestry policies and large disturbance events. Our assessments on the long-term and fine scale forest dynamic patterns will help evaluate the effectiveness of forest management practices and forestry polices on forest resource sustainability, and climate change and greenhouse gases mitigation in Jiangxi Province and China.

STUDY OF THE STATE OF FORESTS OF THE ROSTOV REGION WITH THE USE OF MODERN GEOINFORMATION TECHNOLOGIES

The work is devoted to the study of the forests of the Rostov region, the determination of the spatio-temporal dynamics of the area of the territory covered with forest, using remote sensing data and geoinformation systems. The relevance of the study is due to the active anthropogenic impact on forests in the steppe zone, in a region with a forest deficit cover. The purpose of the study is to study the state of forests based on the use of modern geoinformation technologies, to assess the dynamics of forest cover in the forest fund of the Rostov region. The object of research is one of the most wooded areas of the Rostov region – the Verkhnedonsky. To study the state of the forests of the Rostov region, satellite images obtained using the Sentinel-2 spacecraft and data from the Global Forest Change application were used. Earth Engine. The efficiency of application of various methods of classification of space images has been investigated. It was revealed that despite the forest fires that affect the forests of the region due to climatic and natural factors, the area of gum massifs since 2015, in accordance with the classification carried out, has increased by about 300 hectares.

A satellite-derived database for stand-replacing windthrow events in boreal forests of European Russia in 1986–2017

Abstract. Severe winds are among the main causes of disturbances in boreal and temperate forests. Here, we present a new geographic information system (GIS) database of stand-replacing windthrow events in the forest zone of European Russia (ER) for the 1986–2017 period. The delineation of windthrow areas was based on the full Landsat archive and two Landsat-derived products on forest cover change, namely the Global Forest Change and the Eastern Europe's forest cover change datasets. Subsequent verification and analysis of each windthrow was carried out manually to determine the type of related storm event, its date or date range, and geometrical characteristics. The database contains 102 747 elementary areas of damaged forest that were combined into 700 windthrow events caused by 486 convective or non-convective storms. The database includes stand-replacing windthrows only with an area > 0.05 and > 0.25 km2 for the events caused by tornadoes and other storms, respectively. Additional information such as weather station reports and event descriptions from media sources is also provided. The total area of stand-replacing windthrows amounts to 2966 km2, which is 0.19 % of the forested area of the study region. Convective windstorms contribute 82.5 % to the total wind-damaged area, while tornadoes and non-convective windstorms are responsible for 12.9 % and 4.6 % of this area, respectively. Most of the windthrow events in ER happened in summer, which is in contrast to Western and Central Europe, where they mainly occur in autumn and winter. Due to several data and method limitations, the compiled database is spatially and temporally inhomogeneous and hence incomplete. Despite this incompleteness, the presented database provides a valuable source of spatial and temporal information on windthrow in ER and can be used by both science and management. The database is available at https://doi.org/10.6084/m9.figshare.12073278.v6 (Shikhov et al., 2020).

A New Method for Automated Clearcut Disturbance Detection in Mediterranean Coppice Forests Using Landsat Time Series

A Landsat time series has been recognized as a viable source of information for monitoring and assessing forest disturbances and for continuous reporting on forest dynamics. This study focused on developing automated procedures for detecting disturbances in Mediterranean coppice forests which are characterized by rapid regrowth after a cut. Specifically, new methods specific to Mediterranean coppice forests are needed for mapping clearcut disturbances over time and for estimating related indicators in the context of Sustainable Forest Management and Biodiversity International monitoring frameworks. The aim of this work was to develop a new change detection algorithm for mapping clearcut disturbances in Mediterranean coppice forests with Landsat time series (LTS) using a short time window. Accuracy for the new algorithm, characterized as the Two Thresholds Method (TTM), was evaluated using an independent clearcut reference dataset over a temporal period of the 13 years between 2001 and 2013. TTM was also evaluated against two benchmark approaches: (i) LandTrendr, and (ii) the forest loss category of the Global Forest Change Map. Overall Accuracy for LandTrendr and TTM were greater than 0.94. Meanwhile, smaller accuracies were always obtained for the GFC. In particular, Producer’s Accuracy ranged between 0.45 and 0.84 for TTM and between 0.49 and 0.83 for LT, while for the GFC, PA ranged between 0 and 0.38. User’s Accuracy ranged between 0.86 and 0.96 for TTM and between 0.73 and 0.91 for LT, while for the GFC UA ranged between 0.19 and 1.00. Moreover, to illustrate the utility of TTM for mapping clearcut disturbances in Mediterranean coppice forests, we applied TTM to a Landsat scene that covered almost the entirety of the Tuscany region in Italy.

Mapping thins to identify active forest management in southern pine plantations using Landsat time series stacks

The southeastern United States is unique in terms of both the intensity and scale of forest management, which includes substantial thinning and other forms of harvesting. Because thinning is not a land use transition, and the disturbance signal is relatively subtle compared to a clear cut, there is a dearth of studies that attempt to detect thinning over large areas. Our goal was to detect pine thins as an indicator of active forest management using Landsat data. Areas which undergo thinning are indicative of active forest management in the region.Our approach uses a machine learning method which combines first-order harmonics and metrics from 3-year Fourier regression of Landsat time series stacks, layers from the Global Forest Change product, and other vetted national products into a random forests model to classify forest thins in the southeastern US. Forest Harvest Inspection Records for Virginia were used for training and validation. Models were successful separating thins from clear cuts and non-harvested pines (overall accuracy 86%, clear cut accuracy 90%, thin accuracy 83% for a simplified 10-predictor variable model).Examination of variable importance illustrates the physical meaning behind the models. The curve fit statistics (R2 or RMSE) of the NDVI, Pan, and SWIR1 harmonic curve fits, which are an indication of a departure from typical vegetation phenology caused by thinning or other disturbances, were consistently among the top predictors. The harmonic regression constant, sine and cosine from the Landsat 8 panchromatic band were also important. These describe the visible reflectance (500–680 nm) phenology over the time period at a high spatial resolution (15 m). The Loss Year from the Global Forest Change product, which is an indication of stand replacing disturbance, was also consistently among the most important variables in the classifiers.High performance computing, such as Google Earth Engine, and analysis-ready data are important for this approach. This work has importance for quantification of actively managed forests in a region of the world where production forestry is the dominant land disturbance signal and a significant economic engine.

Evaluating Forest Cover and Fragmentation in Costa Rica with a Corrected Global Tree Cover Map

Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.

Discovering forest height changes based on spaceborne lidar data of ICESat-1 in 2005 and ICESat-2 in 2019: a case study in the Beijing-Tianjin-Hebei region of China

BackgroundThe assessment of change in forest ecosystems, especially the change of canopy heights, is essential for improving global carbon estimates and understanding effects of climate change. Spaceborne lidar systems provide a unique opportunity to monitor changes in the vertical structure of forests. NASA’s Ice, Cloud and Land Elevation Satellites, ICESat-1 for the period 2003 to 2009, and ICESat-2 (available since 2018), have collected elevation data over the Earth’s surface with a time interval of 10 years. In this study, we tried to discover forest canopy changes by utilizing the global forest canopy height map of 2005 (complete global coverage with 1 km resolution) derived from ICESat-1 data and the ATL08 land and vegetation products of 2019 (sampling footprints with 17 m diameter) from ICESat-2.ResultsOur study revealed a significant increase in forest canopy heights of China’s Beijing-Tianjin-Hebei region. Evaluations of unchanging areas for data consistency of two products show that the bias values decreased significantly from line-transect-level (− 8.0 to 6.2 m) to site-level (− 1.5 to 1.1 m), while RMSE values are still relatively high (6.1 to 15.2 m, 10.2 to 12.0 m). Additionally, 58% of ATL08 data are located in ‘0 m’ pixels with an average height of 7.9 m, which are likely to reflect the ambitious tree planting programs in China.ConclusionsOur study shows that it is possible, with proper calibrations, to use ICESat-1 and -2 products to detect forest canopy height changes in a regional context. We expect that the approach presented in this study is potentially suitable to derive a fine-scale map of global forest change.

Deforestation leakage undermines conservation value of tropical and subtropical forest protected areas

AbstractAimThe establishment of protected areas is among the most widespread responses to mitigate species loss. Although protected areas are often assumed to have conservation benefits, negative impacts have also been documented. One potential negative outcome is leakage, whereby protected areas displace land‐use activities harmful to conservation into adjacent areas. This can undermine protection by accelerating loss of species or skewing judgements of effectiveness. This study assessed the prevalence of deforestation leakage in a pan‐tropical and subtropical selection of 120 protected areas.LocationTropical and subtropical forest regions of America, Africa and Asia.Time period2001–2017.Major taxa studiedThreatened species of amphibians, birds and terrestrial mammals.MethodsWe used Global Forest Change data to assess the average yearly rate of deforestation in protected areas, protected area buffer zones and statistically matched, unprotected control areas. We calculated and compared irreplaceability of habitat for threatened amphibian, bird and terrestrial mammal species between protected areas and buffer zones.ResultsIn 55 cases, deforestation rates were higher in buffer zones than in protected and control areas, suggesting a relatively high prevalence of deforestation leakage stemming from protected areas. In 78.2% of documented leakage cases, reduced deforestation in protected areas was not sufficient to offset the amount of deforestation in 10 km buffer zones to a level that would be expected without protection. In 90.9% of leakage cases, the irreplaceability of species in the 10 km buffer zone was greater than that of the protected area, implying a negative impact of leakage on threatened species.Main conclusionsThe results suggest that protected areas are generally effective at preventing deforestation within their jurisdiction; however, leakage patterns can undermine conservation success because buffer zones often contain habitat for threatened species. We recommend accounting for the possibility of leakage when designing new protected areas and networks.

Causal Analysis of Accuracy Obtained Using High-Resolution Global Forest Change Data to Identify Forest Loss in Small Forest Plots

Identifying areas of forest loss is a fundamental aspect of sustainable forest management. Global Forest Change (GFC) datasets developed by Hansen et al. (in Science 342:850–853, 2013) are publicly available, but the accuracy of these datasets for small forest plots has not been assessed. We used a forest-wide polygon-based approach to assess the accuracy of using GFC data to identify areas of forest loss in an area containing numerous small forest plots. We evaluated the accuracy of detection of individual forest-loss polygons in the GFC dataset in terms of a “recall ratio”, the ratio of the spatial overlap of a forest-loss polygon determined from the GFC dataset to the area of a corresponding reference forest-loss polygon, which we determined by visual interpretation of aerial photographs. We analyzed the structural relationships of recall ratio with area of forest loss, tree species, and slope of the forest terrain by using linear non-Gaussian acyclic modelling. We showed that only 11.1% of forest-loss polygons in the reference dataset were successfully identified in the GFC dataset. The inferred structure indicated that recall ratio had the strongest relationships with area of forest loss, forest tree species, and height of the forest canopy. Our results indicate the need for careful consideration of structural relationships when using GFC datasets to identify areas of forest loss in regions where there are small forest plots. Moreover, further studies are required to examine the structural relationships for accuracy of land-use classification in forested areas in various regions and with different forest characteristics.

Accuracy Assessments of Local and Global Forest Change Data to Estimate Annual Disturbances in Temperate Forests

Forest disturbances are generally estimated using globally available forest change maps or locally calibrated disturbance maps. The choice of disturbance map depends on the trade-offs among the detection accuracy, processing time, and expert knowledge. However, the accuracy differences between global and local maps have still not been fully investigated; therefore, their optimal use for estimating forest disturbances has not been clarified. This study assesses the annual forest disturbance detection of an available Global Forest Change map and a local disturbance map based on a Landsat temporal segmentation algorithm in areas dominated by harvest disturbances. We assess the forest disturbance detection accuracies based on two reference datasets in each year. We also use a polygon-based assessment to investigate the thematic accuracy based on each disturbance patch. As a result, we found that the producer’s and user’s accuracies of disturbances in the Global Forest Change map were 30.1–76.8% and 50.5–90.2%, respectively, for 2001–2017, which corresponded to 78.3–92.5% and 88.8–97.1%, respectively in the local disturbance map. These values indicate that the local disturbance map achieved more stable and higher accuracies. The polygon-based assessment showed that larger disturbances were likely to be accurately detected in both maps; however, more small-scale disturbances were at least partially detected by the Global Forest Change map with a higher commission error. Overall, the local disturbance map had higher forest disturbance detection accuracies. However, for forest disturbances larger than 3 ha, the Global Forest Change map achieved comparable accuracies. In conclusion, the Global Forest Change map can be used to detect larger forest disturbances, but it should be used cautiously because of the substantial commission error for small-scale disturbances and yearly variations in estimated areas and accuracies.