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Globally, the loss of forest vegetation is a significant concern due to the crucial roles that forests play in the Earth’s system, including the provision of ecosystem services, participation in biogeochemical cycles, and support for human well-being. Forests are especially critical in mountains environments, where deforestation can lead to accelerated biodiversity loss, soil erosion, flooding, and reduced agricultural productivity, as well as increased poverty rates. In response to these problems, China has implemented a series of ecological restoration programs aimed at restoring forests. However, there is a lack of knowledge as to whether the forest cover is increasing or decreasing, as well as the relative roles played by natural and human factors in forest change. Here, we aim to address these issues by analyzing the pattern and process of the forest changes in Guizhou province, a typical mountainous karst area with a fragile environment in southwestern China, between 1980 and 2018, and evaluating the extent to which these forest changes were influenced by natural and anthropogenic driving forces. Using a temporal sequence of satellite images and a Markov model, we found that the forest cover increased by 468 km2, and that over 33% of the cropland in Guizhou province was converted into forest between 1980 and 2018, with the most significant increases in the forest cover occurring in Qiandongnan. Through correlation analyses and generalized linear model (GLM) regression, we demonstrate that management factors exerted a more significant positive impact on the forest cover than climate change. While the mean annual precipitation and temperature were mostly stable during the period studied, the effects of population and gross domestic product (GDP) on the forest changes weakened, and the influence of land-use change markedly increased. These findings provide valuable information for resource managers engaging in forest protection, deforestation prevention, and ecological restoration in similar regions.

As a leading natural wealth, forests play an essential role in the development and prosperity of countries. Hence, monitoring their changes can lead to proper management and planning in conserving ...

There are many events that took place in Al Mosul province between 2013 and 2018. These events led to many changes in the area under study. These changes involved a decrease in agricultural crops and water due to the population leaving the area. Therefore, it is imperative that planners, decision-makers, and development officials intervene in order to restore the region's activity in terms of environment and agriculture. The aim of this research is to use remote sensing (RS) technique and geographic information system (GIS) to detect the change that occurred in the mentioned period. This was achieved through the use of the ArcGIS software package for the purpose of assessing the state of lands of agricultural crops and forests. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) were adopted in the current calculations. This can help the decision-maker take the necessary measures to avoid the problems caused by the emergency events. The results obtained through this research showed that the region had rate changes in farms, water, and forests of about 1%, as it was found that there was a decrease in the level of the Tigris River and an increase in the area of ​​ carrot crop farms. Also, the results indicated a decrease in areas of agricultural crops in specific regions, while they increased in others.

Understanding terrestrial water storage (TWS) dynamics and associated drivers (e.g., climate variability, vegetation change, and human activities) across climate zones is essential for designing water resources management strategies in a changing environment. This study estimated TWS anomalies (TWSAs) based on the corrected Gravity Recovery and Climate Experiment (GRACE) gravity satellite data and derived driving factors for 214 watersheds across six climate zones in China. We evaluated the long-term trends and stationarities of TWSAs from 2004 to 2014 using the Mann–Kendall trend test and Augmented Dickey-Fuller stationarity test, respectively, and identified the key driving factors for TWSAs using the partial correlation analysis. The results indicated that increased TWSAs were observed in watersheds in tropical and subtropical climate zones, while decreased TWSAs were found in alpine and warm temperate watersheds. For tropical watersheds, increases in TWS were caused by increasing water conservation capacity as a result of large-scale plantations and the implementation of natural forest protection programs. For subtropical watersheds, TWS increments were driven by increasing precipitation and forestation. The decreasing tendency in TWS in warm temperate watersheds was related to intensive human activities. In the cold temperate zone, increased precipitation and soil moisture resulting from accelerated and advanced melting of frozen soils outweigh the above-ground evapotranspiration losses, which consequently led to the upward tendency in TWS in some watersheds (e.g., Xiaoxing’anling mountains). In the alpine climate zone, significant declines in TWS were caused by declined precipitation and soil moisture and increased evapotranspiration and glacier retreats due to global warming, as well as increased agriculture activities. These findings can provide critical scientific evidence and guidance for policymakers to design adaptive strategies and plans for watershed-scale water resources and forest management in different climate zones.

Forest loss is an environmental issue that threatens ecosystems in the Dominican Republic (the DR). Although shifting agriculture by slash-and-burn methods is thought to be the main driver of forest loss in the DR, empirical evidence of this relationship is still lacking. Since remotely sensed data on fire occurrence is a suitable proxy for estimating the spread of shifting agriculture, here I explore the association between forest loss and fire during the first 18 years of the 21st Century using zonal statistics and spatial autoregressive models on different spatio-temporal layouts. First, I found that both forest loss and fire were spatially autocorrelated and statistically associated with each other at a country scale over the study period, particularly in the western and central part of the DR. However, no statistical association between forest loss and fire was found in the eastern portion, a region that hosts a large international tourism hub. Second, deforestation and fire showed a joint cyclical variation pattern of approximately four years up to 2013, and from 2014 onwards deforestation alone followed a worrying upward trend, while at the same time fire activity declined significantly. Third, I found no significant differences in forest loss patterns between the deforested area of small (1 ha) clearings of forest. I propose these findings hold potential to inform land management policies that help reduce forest loss, particularly in protected areas, mountain areas, and the vicinity of tourism hubs.

New Hampshire’s forests are vitally important to the state’s economy; however, there are indications that the state is experiencing a continuous loss in forest cover. We sought to investigate forest cover trends in New Hampshire. A baseline trend in forest cover between 1996 and 2010 was established using National Oceanic and Atmospheric Administration Coastal Change Analysis Program land cover data. A land cover map was then generated from Landsat imagery to extend the baseline trend to 2018. Results show that the state has experienced a continual decline in forest cover with the annual net loss steadily increasing from 0.14% between 1996 and 2001 to 0.27% between 2010 and 2018. Additionally, the more urbanized counties in southern New Hampshire are experiencing some of the greatest rates of net forest loss, most likely because of urbanization and agricultural expansion. This study demonstrated an effective methodology for tracking forest cover change and will hopefully inform future forest use policies.

Forest plays a pivotal role in maintaining the ecological balance. It is necessary to detect the changes in forest cover as the forests have a significant role in promoting carbon cycle. Remote sen...

Aim: At the elevational limit of forest distribution, montane forests show diverse responses to environmental change with upward shifts, increased tree density and lateral expansion reported. To enable informed analysis of the consequences forest advance will have on montane biodiversity, we quantify changes in the area and elevation of the tree line ecotone and identify how patterns of forest advance are modified by topography and over time. Location: Central Mountain Range, Taiwan. Time period: 1963–2016. Major taxa studied: Montane Forests. Methods: Changes in the area and elevation of montane forest at the tree line ecotone were quantified using a stratified random sample of aerial photography captured in 1963, 1980, 2001 and 2016. Weighted estimates of habitat area and elevation for each time step were used to quantify the influence of slope aspect and inclination on tree line ecotone change and identify how the rate of habitat change varies over time. Results: Non‐forest area declined by 29% between 1963 and 2016 driven by a 295.0 ha increase in forest area within the study region. Despite no change in mean forest elevation, the mean elevation of establishing forest has increased at a rate of 2.17 m/yr. Changes in forest area and elevation are spatially variable, driven by the complex montane topography. East and south facing slopes show the largest gains in forest area and 0–20° slopes show an increasing rate of forest establishment up to 2016, while slopes facing west or with incline > 46° show negligible change. Main conclusions: Climate‐linked montane forest expansion in the Central Mountain Range in Taiwan is dominated by infilling rather than increases in forest elevation. Forest expansion has significantly reduced non‐forest habitat area in this endemic species‐rich region. However, considerable terrain‐dependent variation in forest advance occurs, offering the potential that non‐forest species will continue to persist at high elevations with reduced population size.

Forests in Mongolia yield low productivity and are vulnerable to disturbances from drought, fire, pests, and illegal logging. Such forests can quickly lose their ecological balance. Logging activities in these areas are limited in monitoring and controls. This study assesses two different logging operations for their natural regeneration capacity by comparing the composition of the soil, soil organisms, physical and chemical properties, and forest cover change after the completion of logging operations. The logging operations were analyzed in two different regions, the Khartsai and Tariakhtai threshold in Selenge soum, Bulgan province. A skyline logging operation was undertaken on Khartsai threshold in 1983 and a tractor logging operation (clear-cutting) on Tariakhtai threshold in 1987. After the completion of the logging, the forests were naturally regenerated. In 2002, soil samples were collected and soil organisms and physical and chemical properties were examined. Satellites images were also used to evaluate forest cover changes after the end of the logging operations. Significant differences in the naturally regenerated tree species in the skyline logging, tractor logging, and natural forest areas were observed. Average tree ring growth was 0.9 mm in the skyline logging site, 0.6 mm in the tractor logging site, and 1.2 mm in the natural forest. Based on forest cover changes observed in satellite images, the density of naturally regenerated tree species in the natural forest area was higher than that in the skyline logging area. In contrast, the latter recorded a higher density than that in the tractor logging area. Therefore, processing of satellite images of forest cover changes with high-resolution data provides valuable information for the local forest community and helps decision-makers in their further actions.

Activity systems determine the land-use strategies that ultimately shape landscapes. Identifying the reasoning behind the decision making on land use strategies can help to understand how humans contribute to shaping landscapes and to design appropriate land management plans. We studied changes in land-use strategies employed by rural communities, as well as their impact on the landscape in Atzalan, Mexico. For this, we conducted interviews in households distributed throughout the municipality, documenting their activities, resources, and motivations related to land use. We also compared local perceptions of landscape change with that detected by remote sensing analysis. Our results indicate that the activity system in Atzalan features a multiplicity of economic strategies with traditional activities at their core, as well as other more intensified systems such as monocultures and pastures. However, traditional activities are largely giving way to intensified land uses, leading to landscape homogenization. The spatial and temporal distribution of the dominant land covers throughout the municipality reflects a dependency on the environmental context, social structure and land accessibility. People are aware of changes in the landscape and recognize their role in these transformations, mostly due to impacts on the forest and crop cover. Simplification of the activity system has been reflected in the homogenization of the landscape and could prevent households from maintaining diversified strategies of multiple uses of resources.

Dynamic detection of forest change is the fundamental method of monitoring forest resources and an essential means of preserving the accuracy and timeliness of forest land resource data. This study focuses on a deep learning-based method for dynamic forest change detection using Sentinel-2 satellite data, especially within mountainous areas. First, the performance of various deep learning models (U-Net++, U-Net, LinkNet, DeepLabV3+, and STANet) and various loss functions (CrossEntropyLoss(CELoss), DiceLoss, FocalLoss, and their combinations) are compared on a self-made dataset. Next, the best model and loss function is used to predict the annual forest change in Hunan Province from 2017 to 2021, and the detection results are evaluated in 12 sample areas. Finally, forest changes are detected in Sentinel-2 images for each quarter of 2017–2021. In addition, a dynamic detection map of forest change in Hunan Province from 2017 to 2021 is drawn. The results reveal that the U-Net++ model and the CELoss performed the best on the self-made dataset, with a Precision of 0.795, a Recall of 0.748, and an F1-score of 0.771. The results of annual and quarterly forest change detection were consistent with the changes in the Sentinel-2 images with accurate boundaries. This result demonstrates the high practicality and generalizability of the method used in this paper. This paper achieves a rapid and accurate extraction of multi-temporal Sentinel-2 image forest change areas based on the U-Net++ model, which can be used as a benchmark for future large territorial areas monitoring and management of forest resources.

The rate of tropical deforestation is increasing globally, and the fragmentation of remaining forests is particularly high in arable landscapes of West Africa. As such, there is an urgent need to map and monitor these remnant forest patches/fragments and so identify their multiple benefits and values. Indeed, recognizing their existence will help ensure their continued provision of ecosystem services while facilitating their conservation and sustainable use. The aim of this study is therefore to inventory and characterise the current extent and change of remnant forest patches of West Africa, using multi-source remote sensing products, time-series analyses, and ancillary datasets. Specifically, we collate and analyse descriptive and change metrics to provide estimates of fragment size, age, biophysical conditions, and relation to social-ecological change drivers, which together provide novel insights into forest fragment change dynamics for over four decades. We map forests patches outside protected areas with a tree cover ≥30%, a tree height of ≥5 m, an area ≥1 km2 and ≤10 km2. Appended to each patch are descriptive and change dynamics attributes. We find that most fragments are small, secondary forest patches and these cumulatively underwent the most forest loss. However, on average, larger patches experience more loss than smaller ones, suggesting that small patches persist in the landscape. Primary forest patches are scarce and underwent fewer losses, as they may be less accessible. In 1975 most patches were mapped as secondary, degraded forests, savanna, woodland, and mangrove, and relatively few comprised cropland, settlements, and agriculture, suggesting that new forest patches rarely emerged from arable land over the past 45 years (1975–2020), but rather are remnants of previously forested landscapes. Greening is widespread in larger secondary fragments possibly due to regrowth from land abandonment and migration to urban areas. Forest loss and gain are greater across fragments lying in more modified landscapes of secondary forests, while forest loss increases with distance to roads. Finally, larger forest patches harbour a denser tree cover and higher trees as they may be less impacted by human pressures. The number and extent of West African forest patches are expected to further decline, with a concurrent heightening of forest fragmentation and accompanying edge effects. Lacking any conservation status, and subject to increasing extractive demands, their protection and sustainable use is imperative.

Author(s): Turner, Monica G; Braziunas, Kristin H; Hansen, Winslow D; Hoecker, Tyler J; Rammer, Werner; Ratajczak, Zak; Westerling, A Leroy; Seidl, Rupert

The analysis of changes in the catchment vegetation cover as a result of industrial development of its territory, primarily deforestation, was carried out. For processing Landsat images, the spectral space modeling method was used, which provides higher reliability of the classification results compared to traditional methods. The most significant changes in the catchment occurred from the 1930s (the beginning of large-scale logging), until the end of the 1990s. Significantly, by more than 60 %, the area of old-growth forests decreased, their large fragments remained only in nature protected areas, as well as around large lakes and along large rivers, due to the presence of water protection zones. In the period from 2000 to 2018 due to deforestation, the area of productive forests decreased even more. Nevertheless, by 2019 the occurrence of secondary reforestation on significant areas (up to 70 %) is noted, which indicates a sufficiently high regeneration potential of the catchment ecosystems. To restore the spatial structure of the forest cover in the period before the start of the Landsat 5 operation, the method of tracking the trajectories of reforestation successions in the spectral space was used. A comparison with the results of the Global Forest Change 2000—2018 data (loss/gain) showed almost complete coincidence in cuttings (loss), but the method of reforestation trajectories showed many times higher results in forest gain, since it reveals forest regeneration at a much earlier stage. The developed methodology will be applied on the catchments of other tributaries of the White Sea, and the results will be used as input to an environmental-socio-economic cognitive model that predicts the state of ecosystems under climate changes and economic priorities.

In this study we examined (1) whether there has been significant tree cover change over the period 1960–2010 in a 960,000 km2 subarctic study region in western and central Canada, and (2) the degree to which Global Forest Change (GFC) tree cover data agree with other datasets. We compared GFC tree cover to cover estimates from air photos (c. 1960), ground-level plot data (c. 1982–84), annotated low-level oblique photographs (c. 2005–09), and air photo footprints on the World Imagery Base Map (c. 2010). Tree cover changes since 1960 varied by physiographic and ecological regions. Afforestation was modest to non-significant depending on the region. We observed no evidence of northward tree migration. An increase in the areal extent of burned forests, mostly in areas south of the forest-tundra, was the largest change detected. We documented systematic discrepancies between our tree cover estimates and GFC data. GFC underestimates of tree cover typically occurred in areas of low tree density. Areas where GFC data overestimated tree cover were common, especially near the northern limits of trees and in areas dominated by dense or tall shrubs. Predictions of climate-driven vegetation response derived solely from remotely sensed data may not be reliable.

Spatial modelling of land use change is a technique for understanding changes in terms of the location and amount. In this study, logistic regression and Geomod approaches were used for modelling forest change in Gorgan area in Northern Iran in the time period of 1988-2007. To do this, at first, remotely sensed imagery data of the years 1988, 1998 and 2007 were used to produce land use maps. Land use maps accuracy assessments were achieved using Error matrix method and then the maps were used to implement change detection process in two time periods of 1988-1998 and 1998-2007. Results indicated a reduction in forest areas during the mentioned time period. Next, the independent variables were extracted in order to land use change modeling. The Results of the models implementation showed the ability of both models for forest change modeling in this region. Also, the models were used to predict the future condition of forest area in the years 2016 and 2025. The results revealed that the forest area would be associated with a reduction in the future. Comparison of the results of the models using kappa indices showed the successful implementation of both models for forest change modelling in this region. The results of this research reveal the need for appropriate applications of the proper plans to control land use change in order to preserve the environment and ecological balance of the area. Therefore, careful planning can reduce the land use change and its impacts in the future in this region.

Understanding the interconnection between land use processes and land change patterns is yet to be fully achieved. Land change researchers have posited that the lack of understanding is not due to ...

Achieving a global forest transition, that is, a shift from net deforestation to reforestation, is essential for climate change mitigation. However, both land-based climate change mitigation policy and research on forest transitions neglect key processes that relieve pressure from forests, but cause emissions elsewhere (‘hidden emissions’). Here, we identify three major causes of hidden emissions of forest transitions, that is, emissions from agricultural intensification, from woodfuel substitution, and from land displacement. Taken together, these emissions may compromise the climate change mitigation effect of national forest transitions. We propose to link analyses of hidden emissions of forest transitions with quantifications of full socio-ecological greenhouse-gas accounts and analyses of their politics. Such an integration allows for drawing lessons for effective and just climate change mitigation policies.

Recent advances in high-performance computing (HPC) have promoted the creation of standardized remotely sensed products that map annual vegetation disturbance through two primary methods: (1) conventional approaches that integrate remote sensing-derived vegetation indices with field data and other data on disturbance events reported by public agencies on a year-to-year basis, and (2) “big” data approaches using HPC to automate algorithms and workflows across an entire time series. Given the recent proliferation of these annual products and their potential utility for understanding vegetation dynamics, it is important for product end users (that is, practitioners and researchers in domains other than remote sensing) to understand the differences in their representations of disturbance and the conditions under which they report it. We use fire in California as a case study to compare reported disturbance across three widely used vegetation disturbance products—LANDFIRE (representing the conventional approach), Hansen Global Forest Change (GFC), and North America Forest Dynamics (NAFD), the latter two created from automated approaches. Using Google’s Earth Engine, we compared their total and annual amounts of fire and non-fire disturbance for 2001–2010 and examined the products’ reported disturbance across different environmental and burn conditions. We found that GFC and NAFD reported similar amounts of disturbance that were consistently much lower than LANDFIRE’s reported disturbance across all years, regions, and habitats. We also found that despite the differences in amounts of reported disturbance, the products identified disturbance in similar ranges of bioclimatic conditions and habitat types, and thus, differing environmental conditions in areas reported as disturbed were not the drivers of the difference. Rather, we found that lower sensitivity to fire disturbance for GFC and NAFD, as compared to LANDFIRE, was a key driver of the overall differences in the amounts and locations of reported disturbance; both GFC and NAFD reported much lower amounts of fire disturbance than LANDFIRE across all burn conditions. Furthermore, the difference in reported disturbance between LANDFIRE and GFC/NAFD was greater for fire disturbance than for non-fire disturbance; LANDFIRE reported more than double the total amounts of fire disturbance of GFC and NAFD in the study period. Based on our results, we encourage end users to choose the appropriate disturbance product based not only on spatial extent and habitat but also on the disturbance type of interest (that is, fire and non-fire). Overall, rather than focusing on accuracy, our study quantifies the extent to which the products exhibited differences in the amounts and locations of reported disturbance to provide insight into these products’ representations of disturbance and help end users evaluate and choose the most appropriate product for their needs.

The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition.

Protected areas (PAs) are a cornerstone for conservation biodiversity. Madagascar, as a hotspot for biodiversity, has a network of 114 terrestrial protected areas covering the main forest types occurring on the island. Deforestation continues unabated despite the network covering 11% of the island. Here we present a case study approach reporting on four PAs from the humid forests, dry western forests, and southwestern dry and spiny forests and thickets. To describe deforestation in and around the case sites, we have considered a time window of 30 years for analysis, focusing on six years with reliable data: 1990, 2000, 2010, 2015 (the year of latest PA network update), and 2017. We have considered forest versus other land covers within the PAs in "buffers" at a distance of 500 m, 2.5 km, 5 km, and 10 km from the border of the PA. These buffers were set from the border towards the center of the PA (inside the PAs) and from the border outside the PAs. The smallest PAs, Kasijy (IUCN IV), and Behara Tranomaro (no IUCN category), showed the least forest loss. Tsaratanana (IUCN I) had the highest deforestation rates within the last two years of analysis, with deforestation concentrated in the core area. Ranobe PK-32 (no IUCN category), originally with the largest forest extent, has lost most of its forest cover and showed the highest annual deforestation rate (3.5%) between 2015 and 2017. All four cases prove to be very challenging to manage. Future conservation activities require tailored interventions to account for site-specific current and potential future threats, as detailed in this contribution., Forests, 12 (9), ISSN:1999-4907

We assess the fulfillment of zero deforestation agreements at farm level, ALOS-1 and ALOS-2 PALSAR fine beam dual imagery for years 2010 and 2016 was processed with ad-hoc routines to estimate stable forest, deforestation, and stable non-forest extension for 2615 participant farms in five regions of Colombia. Farms associated with Meta Foothills regions showed zero deforestation during the period analyzed (2010–2016), while other regions showed low deforestation rates with the exception of the Cesar River Valley (75 ha). Results, suggests that topography and dry weather conditions have an effect on radar-based mapping accuracy. Nevertheless, overall ALOS Phased Array L-band SAR (PALSAR) data provided overall accurate, relevant, and consistent information for forest change analysis for local zero deforestation agreements assessment.

International audience

A short review of research in Canada using aleocharine beetles as indicators of forest change mainly due to different forestry practices is reviewed and discussed. It reletes mainly to boreal forests in eastern and central Canada.

Landslides are common disturbances in forests around the world, and a major threat to human life and property. Landslides are likely to become more common in many areas as storms intensify. Forest vegetation can improve hillslope stability via long, deep rooting across and through failure planes. In the U.S. Rocky Mountains, landslides are infrequent but widespread when they do occur. They are also extremely understudied, with little known about the basic vegetation recovery processes and rates of establishment which restabilize hills. This study presents the first evaluation of post-landslide vegetation recovery on forested landslides in the southern Rocky Mountains. Six years after a major landslide event, the surveyed sites have very little regeneration in initiation zones, even when controlling for soil coverage. Soils are shallower and less nitrogen rich in initiation zones as well. Rooting depth was similar between functional groups regardless of position on the slide, but deep-rooting trees are much less common in initiation zones. A lack of post-disturbance tree regeneration in these lower elevation, warm/dry settings, common across a variety of disturbance types, suggests that complete tree restabilization of these hillslopes is likely to be a slow or non-existent, especially as the climate warms. Replacement by grasses would protect against shallow instabilities but not the deeper mass movement events which threaten life and property.

Rabigh Lagoon, located on the eastern coast of the Red Sea, is an ecologically rich zone in Saudi Arabia, providing habitat to Avicennia marina mangrove trees. The environmental quality of the lagoon has been decaying since the 1990s mainly from sedimentation, road construction, and camel grazing. However, because of remedial measures, the mangrove communities have shown some degree of restoration. This study aims to monitor mangrove health of Rabigh Lagoon during the time it was under stress from road construction and after the road was demolished. For this purpose, time series of EVI (Enhanced Vegetation Index), MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and NDMI (Normalized Difference Moisture Index) have been used as a proxy to plant biomass and indicator of forest disturbance and recovery. Long-term trend patterns, through linear, least square regression, were estimated using 30 m annual Landsat surface-reflectance-derived indices from 1986 to 2019. The outcome of this study showed (1) a positive trend over most of the study region during the evaluation period, (2) most trend slopes were gradual and weakly positive, implying subtle changes as opposed to abrupt changes, (3) all four indices divided the times series into three phases: degraded mangroves, slow recovery, and regenerated mangroves, (4) MSAVI performed best in capturing various trend patterns related to the greenness of vegetation, and (5) NDMI better identified forest disturbance and recovery in terms of water stress. Validating observed patterns using only the regression slope proved to be a challenge. Therefore, water quality parameters such as salinity, pH/dissolved oxygen should also be investigated to explain the calculated trends.

Obtaining high-quality information on forest and land use is essential to analysis of climate change, sustainable forest and land use planning. Papua New Guinea’s (PNG) forest and land cover/land use has been well documented using different methods, land classifications and forest definitions. These studies have delivered significant results indicating a general decline in the forest extent, with the drivers of land use changes attributed to demographic and economic development. This study is a component of the larger National Forest Inventory for PNG within which we sought to stratify and quantify forest and land use change by applying a systematic point-based sampling approach utilizing Open Foris—Collect Earth and freely available satellite images. A total of 25,279 sample points was assessed to determine the PNG’s forest extent and the forest change drivers based on the national forest definition. Analysis revealed that in 2015, about 78% of the country was covered with 12 forest types, and more than 23% of the total forest area has been degraded through anthropogenic activities. Analysis also revealed that between 2000 and 2015, about 0.66% of the total forest area was deforested, and subsistence agriculture was the main driver (0.45%), followed by palm oil planting (0.23%). During the same period, about 6.6% of the total forest area was degraded, and commercial logging was the main cause (6.1%). Apart from Global Forest Watch, this study established more forest than previously estimated in earlier studies. This is due to the fundamental differences in the purposes and methodologies used.

Boreal forest is a sensitive indicator of the influence of climate change. It can quantify the level and spatial divergence of forest change for forest resources and carbon cycle research. This study selected a typical boreal forest affected by few human activities as a research area, in Siberia, with a latitude span of 51°N–69°N. A total of 150 Landsat images of this area acquired in 1985 and 2015 were collected. A hierarchical classification approach was first established to retrieve the information of forest cover and species. The forested and nonforested lands were discriminated by the decision tree method and, furthermore, the forested land was classified to broad-leaved and coniferous forests by a random forest algorithm. The overall accuracy was 90.37%, which indicates the validity of the approach. Finally, the quantitative information of the forest cover and species changes in each latitude zone of every 2° was analyzed. The results show that the overall boreal forest cover increased by 5.11% over the past three decades, with broad-leaved forest increasing by 3.54% and coniferous forest increasing by 1.57%. In addition, boreal forest increased in every latitude zone, and the spatial divergence of the changes of the boreal forest cover and species in different latitude zones were significant. Finally, broad-leaved forest increased more rapidly than coniferous forest, and the greatest increase, of up to 5.77%, occurred in the zone of 55°N–57°N.

The palm oil industry is one of the major producers of vegetable oil in the tropics. Palm oil is used extensively for the manufacture of a wide variety of products and its production is increasing by around 9% every year, prompted largely by the expanding biofuel markets. The rise in annual demand for biofuels and vegetable oil from importer countries has caused a dramatic increase in the conversion of forests and peatlands into oil palm plantations in Malaysia. This study assessed the area of forests and peatlands converted into oil palm plantations from 1990 to 2018 in the states of Sarawak and Sabah, Malaysia, and estimated the resulting carbon dioxide (CO2) emissions. To do so, we analyzed multitemporal 30-m resolution Landsat-5 and Landsat-8 images using a hybrid method that combined automatic image processing and manual analyses. We found that over the 28-year period, forest cover declined by 12.6% and 16.3%, and the peatland area declined by 20.5% and 19.1% in Sarawak and Sabah, respectively. In 2018, we found that these changes resulted in CO2 emissions of 0.01577 and 0.00086 Gt CO2-C yr&minus, 1, as compared to an annual forest CO2 uptake of 0.26464 and 0.15007 Gt CO2-C yr&minus, 1, in Sarawak and Sabah, respectively. Our assessment highlights that carbon impacts extend beyond lost standing stocks, and result in substantial direct emissions from the oil palm plantations themselves, with 2018 oil palm plantations in our study area emitting up to 4% of CO2 uptake by remaining forests. Limiting future climate change impacts requires enhanced economic incentives for land uses that neither convert standing forests nor result in substantial CO2 emissions.

The landscape context (i.e., anthropogenic setting) of forest change partly determines the social-ecological outcomes of the change. Furthermore, forest change occurs within, is constrained by, and contributes to a dynamic landscape context. We illustrate how information about local landscape context can be incorporated into regional assessments of forest area change. We examined the status and change of forest area in the continental United States from 2001 to 2016, quantifying landscape context by using a landscape mosaic classification that describes the dominance and interface (i.e., juxtaposition) of developed and agriculture land in relation to forest and other land. The mosaic class changed for five percent of total land area and three percent of total forest area. The least stable classes were those comprising the developed interface. Forest loss rates were highest in developed-dominated landscapes, but the forest area in those landscapes increased by 18 percent as the expansion of developed landscapes assimilated more forest area than was lost from earlier developed landscapes. Conversely, forest loss rates were lowest in agriculture-dominated landscapes where there was a net loss of five percent of forest area, even as the area of those landscapes also increased. Exposure of all land to nearby forest removal, fire, and stress was highest in natural-dominated landscapes, while exposure to nearby increases in developed and agriculture land was highest in developed- and agriculture-dominated landscapes. We discuss applications of our approach for mapping, monitoring, and modeling landscape and land use change.

Island ecosystems are particularly susceptible to climate change and human activities. The change of land use and land cover (LULC) has considerable impacts on island ecosystems, and there is a critical need for a free and open-source tool for detecting land cover fluctuations and spatial distribution. This study used Google Earth Engine (GEE) to explore land cover classification and the spatial pattern of major land cover change from 1990 to 2019 on Haitan Island, China. The land cover classification was performed using multiple spectral bands (RGB, NIR, SWIR), vegetation indices (NDVI, NDBI, MNDWI), and tasseled cap transformation of Landsat images based on the random forest supervised algorithm. The major land cover conversion processes (transfer to and from) between 1990 and 2019 were analyzed in detail for the years of 1990, 2000, 2007, and 2019, and the overall accuracies ranged from 88.43% to 91.08%, while the Kappa coefficients varied from 0.86 to 0.90. During 1990&ndash, 2019, other land, cultivated land, sandy land, and water area decreased by 30.70%, 13.63%, 3.76%, and 0.95%, respectively, while forest and built-up land increased by 30.94% and 16.20% of the study area, respectively. The predominant land cover was other land (34.49%) and cultivated land (26.80%) in 1990, which transitioned to forest land (53.57%) and built-up land (23.07%) in 2019. Reforestation, cultivated land reduction, and built-up land expansion were the major land cover change processes on Haitan Island. The spatial pattern of forest, cultivated land, and built-up land change is mainly explained by the implementation of a &lsquo, Grain for Green Project&rsquo, and &lsquo, Comprehensive Pilot Zone&rsquo, policy on Haitan Island. Policy and human activities are the major drivers for land use change, including reforestation, population growth, and economic development. This study is unique because it demonstrates the use of GEE for continuous monitoring of the impact of reforestation efforts and urbanization in an island environment.

Forests are important oxygen sources and carbon sinks. They provide a series of ecosystem services that are crucial to eco-island sustainability, such as the protection of soils, conservation of biodiversity, and development of the eco-tourism, etc. Interestingly, Chongming eco-island is located in the borderlands between fresh- and saltwater environments, where the Yangtze River meets the East China Sea. Most forests in Chongming island are therefore man-made and very vulnerable to the ecological environment mixing of freshwater streams and rivers with salty ocean tides, and are affected by climate and human activity. Estimating and monitoring forest change within an estuary is required for the sustainable management of forest resources and forest-based eco-tourism. Most optical satellites are unsuitable for continuous forest mapping due to cloud cover and their relatively low spatial and temporal resolution. Here, using Sentinel-1 satellite carrying an imaging C-band synthetic aperture radar, radar vegetation index was derived to detect the forest dynamics on the island. Furthermore we quantified forest area change in three well known and the most strictly protected and representative areas, namely Dongping National Forest Park, Dongtan National Wetland Park, and Xisha National Wetland Park, in the Chongming eco-island over the past five years at 10-metre resolution. We recorded the early and mid summers when the forest canopies grew to the peak in the study areas. The planted forest in Dongping National Forest Park grew an area of 7.38 hectares from 2015 to 2019, and disappeared from an area of almost 2.59 hectares in 2018. The man-made forest of Xisha National Wetland Park increased at an area of almost 20.19 hectares over the past five years. The forest in Dongtan National Wetland Park increased to an area of almost 2.12 hectares in the period of 2015–2017 and 2018–2019. However, from 2017 to 2018, the area of planted forests in Dongtan National Wetland Park decreased by 1.35 hectares. This study shows man-made forest change can be measured and that evidence can be collected to show how the forest is altered by human activities, and informs forest management decision-making for Chongming eco-island.

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.

Forest cover change is critical in the regulation of global and regional climate change through the alteration of biophysical features across the Earth’s surface. The accurate assessment of forest cover change can improve our understanding of its roles in the regulation processes of surface temperature. In spite of this, few researchers have attempted to discern the varying effects of multiple satellite-derived forest changes on local surface temperatures. In this study, we quantified the actual contributions of forest loss and gain associated with evapotranspiration (ET) and albedo to local surface temperature in Guangdong Province, China using an improved spatiotemporal change pattern analysis method, and explored the interrelationships between surface temperature and air temperature change. We specifically developed three forest change products for Guangdong, combining satellite observations from Landsat, PALSAR, and MODIS for comparison. Our results revealed that the adjusted simple change detection (SCD)-based Landsat/PALSAR forest cover data performed relatively well. We found that forest loss and gain between 2000 and 2010 had opposite effects on land surface temperature (LST), ET, and albedo. Forest gain led to a cooling of −0.12 ± 0.01 °C, while forest loss led to a warming of 0.07 ± 0.01 °C, which were opposite to the anomalous change of air temperature. A reduced warming to a considerable cooling was estimated due to the forest gain and loss across latitudes. Specifically, mid-subtropical forest gains increased LST by 0.25 ± 0.01 °C, while tropical forest loss decreased LST by −0.16 ± 0.05 °C, which can demonstrate the local differences in an overall cooling. ET induced cooling and warming effects were appropriate for most forest gain and loss. Meanwhile, the nearby temperature changes caused by no-change land cover types more or less canceled out some of the warming and cooling. Albedo exhibited negligible and complex impacts. The other two products (i.e., the GlobeLand30 and MCD12Q1) affect the magnitude of temperature response due to the discrepancies in forest definition, methodology, and data resolution. This study highlights the non-negligible contributions of high-resolution maps and a robust temperature response model in the quantification of the extent to which forest gain reverses the climate effects of forest loss under global warming.

National monitoring of forestlands and the processes causing canopy cover loss, be they abrupt or gradual, partial or stand clearing, temporary (disturbance) or persisting (deforestation), are necessary at fine scales to inform management, science and policy. This study utilizes the Landsat archive and an ensemble of disturbance algorithms to produce maps attributing event type and timing to &gt, 258 million ha of contiguous Unites States forested ecosystems (1986&ndash, 2010). Nationally, 75.95 million forest ha (759,531 km2) experienced change, with 80.6% attributed to removals, 12.4% to wildfire, 4.7% to stress and 2.2% to conversion. Between regions, the relative amounts and rates of removals, wildfire, stress and conversion varied substantially. The removal class had 82.3% (0.01 S.E.) user&rsquo, s and 72.2% (0.02 S.E.) producer&rsquo, s accuracy. A survey of available national attribution datasets, from the data user&rsquo, s perspective, of scale, relevant processes and ecological depth suggests knowledge gaps remain.

We examine the effect of forest cover change on agricultural productivity using household panel data and remote sensing data on forest change. The focus of the study is rural Indonesia, where deforestation is causing intensive biodiversity loss while agriculture is the main industry. We estimate an agricultural production function and find that farmers in rural Indonesia experienced a reduction in agricultural productivity of 45% between 2001 and 2014 or US$2.63 billion in 2014. In addition, we explore the mechanisms underlying the productivity loss and find that biological pest control is the most plausible explanation., World Development, 114, pp.196-207; 2019

Water resources from rivers are essential to humans. The discharge of rivers is demonstrated to be significantly affected by climate change in the literature, particularly in the boreal and subboreal climate zones. The Da Hinggan Mountains in subboreal northeast China form the headwaters of the Heilongjiang River and the Nenjiang River, which are important water resources for irrigation of downstream agriculture and wetlands. In this study, long-term (44 years) hydrologic, climate and forest dynamics data from the Tahe were analyzed using the soil and water assessment tool (SWAT) model to quantify the effects of climate and forest change on runoff depth. Meanwhile, downscaled precipitation and temperature predictions that arose from global climate models (GCMs) under four representative concentration pathways (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) were forced using the SWAT model to investigate the climate change impacts on the Tahe River flows in the future. The results indicated that compared with the 1972&ndash, 1982 period, the forest biomass in the 1984&ndash, 1994 period was reduced by 17.6%, resulting in an increase of 16.6% in mean annual runoff depth. On the contrary, with reforestation from the 1995&ndash, 2005 period to the 2006&ndash, 2016 period, the mean forest biomass was increased by 9.8%, resulting in the mean runoff depth reduction of 11.9%. The tree species composition shift reduced mean annual runoff depth of 13.3% between the 1984&ndash, 1994 period and the 2006&ndash, 2016 period. Compared with base years (2006&ndash, 2016), projections of GCM in the middle of the 21st century indicated that both mean annual temperature and precipitation were expected to increase by &minus, 0.50 &deg, C and 43 mm under RCP 2.6, 0.38 &deg, C and 23 mm under RCP 4.5, 0.67 &deg, C and 36 mm under RCP 6.0 and 1.00 &deg, C and 10 mm under RCP 8.5. Simulated results of the SWAT model showed that annual runoff depth would increase by 18.1% (RCP 2.6), 11.8% (RCP 4.5), 23.6% (RCP 6.0), and 11.5% (RCP 8.5), compared to the base years. Such increased runoff was mainly attributed to the increase in April, July, August, September and October, which were consistent with the precipitation prediction. We concluded that the future climate change will increase the water resources from the river, thereby offsetting the possible decline in runoff caused by the forest recovery. The findings of this study might be useful for understanding the impacts of climate and forest change on runoff and provide a reasonable strategy for managers and planners to mitigate the impact of future climate change on water resources in the subboreal forested watersheds.

Forecasts of forest responses to climate variability are governed by climate exposure and ecosystem sensitivity, but ecosystem model projections and process representations are under-constrained by data at multidecadal and longer timescales. Here, we assess ecosystem sensitivity to centennial-scale hydroclimate variability, by comparing dendroclimatic and pollen-inferred reconstructions of drought, forest composition and biomass for the last millennium with five ecosystem model simulations. In both observations and models, spatial patterns in ecosystem responses to hydroclimate variability are strongly governed by ecosystem sensitivity rather than climate exposure. Ecosystem sensitivity was highest in simpler models and higher than observations, suggesting that interactions among biodiversity, demography, and ecophysiology processes dampen the sensitivity of forest composition and biomass to climate variability and change. By integrating ecosystem models with observations from timescales extending beyond the instrumental record, we can better understand and forecast the mechanisms regulating forest sensitivity to climate variability in a complex and changing world.

Natura 2000 network, the world's largest network of protected areas, is considered a success for habitat and biodiversity protection, in the last decades. Our objective is to develop an algorithm for satellite data temporal analysis of protected areas, and to apply subsequently this algorithm for analysis of all Natura 2000 sites in Europe. We have developed an algorithm for satellite data temporal analysis of protected areas using JavaScript in Google Earth Engine, which is a web interface for the massive analysis of geospatial data, providing access to huge amount of data and facilitating development of complex workflows. This work focused on analysis of Global Forest Change dataset representing forest change, at 30 meters resolution, globally, between 2000 and 2018. Our results show that at least regarding forest protection, the network is not very successful, the 25350 sites losing 35246.8 km2 of forest cover between 2000 and 2018, gaining only 9862.1 km2. All 28 countries recorded a negative forest net change, with a mean value of -906.6 km2, the largest forest area change recording Spain (-5106.4 km2 in 1631 sites), Poland (-4529 km2 in 962 sites), Portugal (-2781.9 km2 in 120 sites), Romania (-1601.4 km2 in 569 sites), Germany (-1365.7 km2 in 5049 sites) and France (-1270.9 km2 in 1520 sites). Among countries with the lowest values in net forest change is Ireland (-17.4 km2 in 447 sites), Estonia (-104.1 km2 in 518 sites), Netherlands (-132.3 km2 in 152 sites), Finland (-268.6 km2 in 1722 sites) and Sweden (-341.6 km2 in 3786 sites).

Extreme climatic and anthropogenic disturbance events are driving forces of regional and global forest change. This special issue is dedicated towards evaluating tropical dry forest resilience to such events in Mexico at the landscape, ecosystem, community and population levels. Collectively, the articles herein suggest that tropical dry forests are highly resilient to these extreme disturbances, at least in the short-term, because different patterns and processes across ecological levels can recover relatively quickly to pre-disturbance conditions. However, because forest recovery is strongly controlled by water availability, extreme dry years after disturbance may limit its resilience capacity. Understanding the precipitation regime in these seasonally dry forests will be crucial for improving their management as the frequency of extreme events increases. A common theme of several articles in this special issue is that resilience of tropical dry forests to the interacting effects of climatic and anthropogenic disturbances seems so far idiosyncratic and unpredictable and merits further research in the long-term.