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Highlights • In Ghana's forest zone, 2016 was the driest year during the period 2003–2017. • In 2016, forest reserves had more active fire detections than in any other year. • Most fires inside forest reserves occurred in the moist semi-deciduous type. • The percent area burned was highest in the most degraded forest reserves. • Drought led to more fire in 2016 and degradation influenced the locations of fires. Abstract Forest reserves in Ghana are the only significant refugia of natural tropical forests, but they are threatened by land use pressures that have led to widespread forest degradation. Additional stress from climate perturbations, such as droughts, can reduce fuel moisture and lead to fires that render the reserves more vulnerable to further degradation. Here, we explore recent drought-associated wildfires in the forest zone of Ghana to better understand the combined effects of forest degradation and drought stress on fire in the forest reserves. We used remotely sensed Earth observations from MODIS and Landsat 8 along with Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) precipitation data, to study fires that occurred during drought conditions in 2016. Fires were widespread in 2016, with an estimated 2137 km2 of forest reserve area burned. During the period 2003–2017, forest reserves experienced the most active fire detections in 2016. The majority of these fires were located in the moist semi-deciduous forests, the largest and most economically valuable forest type, accounting for more than 50% of all active fires and burned area. Forest reserves within the moist semi-deciduous forest type also had the highest standardized fire anomalies in 2016. There was a higher percentage of burned area in degraded forest reserves than in more intact reserves. These results underscore the vulnerability of Ghana's forest reserves, particularly in the moist semi-deciduous type, to fires during severe droughts. Although drought predisposes moist tropical forests to fire, forest degradation also influences the spatial pattern and extent of burned forests. Fire-induced forest degradation presents a major challenge for conservation of humid tropical forests in the region. Within the context of REDD+ and other climate change mitigation and adaptation programs in West Africa, it will be imperative to incorporate effective fire management strategies into ongoing efforts to reduce forest degradation and sustain forest resilience. [ABSTRACT FROM AUTHOR]

Estimation of fire impacts and forest recovery using remote sensing is difficult because of the heterogeneity of fire history (frequency, severity, and time since last fire) across burned forest landscapes. The authors analyzed impacts of fire frequency and severity within recovering forests in the Amazon region using remote sensing. A multispectral Landsat time series dataset was used to reconstruct the fire history from 1990 to 2002 in a portion of Mato Grosso, Brazil. Five narrowband vegetation indices were then calculated from a hyperspectral Earth Observing One (EO-1) Hyperion image for spectral analysis of physiological characteristics of fire-disturbed forests and their recovery. A total of 30%% of the forests burned during the study period, with 72%% burned once, 24%% burned twice, and less than 4%% burned three times. In terms of severity, 70%% of burned forest was lightly burned, 21.1%% was moderately burned, and 9.1%% was severely burned. Analyses of spectral indices [normalized difference vegetation index (NDVI), carotenoid reflectance index (CRI), and photochemical reflectance index (PRI)] showed that those related to canopy greenness and pigment contents can discriminate between burned forests and undisturbed forest for the first 3 years after forest fire, whereas the effectiveness of canopy water content indices [normalized difference water index (NDWI) and normalized difference infrared index (NDII)] varied from 1 to 3 years, depending on the fire severity. Despite the relatively low signal-to-noise ratios of Hyperion imagery, we show that narrowband-derived indices provide useful information for monitoring degraded forests beyond what is currently possible with Landsat. This illustrates the great potential for environmental monitoring using satellite-borne hyperspectral sensors, such as the Hyperspectral Infrared Imager (HyspIRI), which have better signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

Abstract: Forest edges in the Amazon are very dynamic with ongoing deforestation adding new edges as older edges are eroded. Rates of edge erosion and the composition of edge ages, together with distance from edges, are very important factors in determining the magnitude of forest degradation such as biomass collapse and carbon flux. However, we lack an understanding of how these factors change through time and over the different stages of deforestation. In this study, we quantify the spatial and temporal structures of forest edge in Rondônia, southwestern Amazon, by analyses of 22 years of annual satellite imagery, and discuss the implications for biomass dynamics and forest degradation caused by edges. Our results from three different stages of deforestation (early, intermediate and advanced) reveal that more than 50% of forest edges were eliminated in the first four years after edge creation and only 20% of edges survived more than 10 years. High edge erosion rates in the first year imply that many edges disappear before going through the process of edge-induced biomass collapse. At the early stage of deforestation, young forest edges are predominant, while at the advanced deforestation stage more than 50% of total edges are >10 years old. Rapid erosion is more prevalent in early stages, when young forest edges dominate the landscape. Edge-related biomass collapse is substantially more advanced in heavily deforested regions where forests are mostly surrounded mostly by older edges, but relatively few edges remain at this point. [Copyright &y& Elsevier]

Remote sensing has the potential of improving our ability to map and monitor pasture degradation. Pasture degradation is one of the most important problems in the Amazon, yet the manner in which grazing intensity, edaphic conditions and land‐use age impact pasture biophysical properties, and our ability to monitor them using remote sensing is poorly known. We evaluate the connection between field grass biophysical measures and remote sensing, and investigate the impact of grazing intensity on pasture biophysical measures in Rondônia, in the Brazilian Amazon. Above ground biomass, canopy water content and height were measured in different pasture sites during the dry season. Using Landsat Thematic Mapper (TM) data, four spectral vegetation indices and fractions derived from spectral mixture analysis, i.e., Non‐Photosynthetic Vegetation (NPV), Green Vegetation (GV), Soil, Shade, and NPV+Soil, were calculated and compared to field grass measures. For grazed pastures under dry conditions, the Normalized Difference Infrared Index (NDII5 and NDII7), had higher correlations with the biophysical measures than the Normalized Difference Vegetation Index (NDVI) and the Soil‐Adjusted Vegetation Index (SAVI). NPV had the highest correlations with all field measures, suggesting this fraction is a good indicator of pasture characteristics in Rondônia. Pasture height was correlated to the Shade fraction. A conceptual model was built for pasture biophysical change using three fractions, i.e., NPV, Shade and GV to characterize possible pasture degradation processes in Rondônia. Based upon field measures, grazing intensity had the most significant impact on pasture biophysical properties compared to soil order and land‐use age. The impact of grazing on pastures in the dry season could be potentially measured by using remotely sensed measures such as NPV. [Copyright &y& Elsevier]

Although pasture degradation has been a regional concern in Amazonian ecosystems, our ability to characterize and monitor pasture degradation under different environmental and human-related conditions is still limited. Regional analysis of pasture dynamic patterns was conducted using high-frequency temporal satellite data and ancillary data to better understand pasture degradation under varied soil, environmental, and pasture management conditions in the state of Rondônia, Brazil. The 10-day normalized difference vegetation index (NDVI) composite derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m resolution was used to characterize different grass phenological patterns for 32 counties in Rondônia between 2001 and 2003. Six pasture greenness classes showed that high greenness pasture classes dominated in young pastures, while low greenness pasture classes were least common. As pastures aged, the proportion of high greenness pasture classes decreased and the proportion of low greenness pastures increased, indicating a decrease in forage productivity over time in Rondônia. The magnitude of productivity decline depended on environmental constraints and land use systems. To refine this analysis, trajectories of pasture change were determined using spectral mixture analysis applied to Landsat time series data from 1988 to 2001 with the focus on two counties that show contrasting patterns of potential of grass production: Pimenteira, representing the “degraded” pasture category, and Governador Jorge Teixeira, as the “productive” pasture category. The results revealed a clear pasture degradation pattern in Pimenteira, related to low soil fertility and dry climate conditions, while Governador Jorge Teixeira, with better soil fertility and intermediate precipitation, did not show signs of pasture degradation through time. [ABSTRACT FROM AUTHOR]

The dynamic change and spatial–temporal distribution of vegetation coverage are of great significance for regional ecological evolution, especially in the subtropics and tropics. Identifying the heterogeneity in vegetation activities and its response to climate factors is crucial for projecting ecosystem dynamics. We used long-term (2001–2018) satellite-derived enhanced vegetation index (EVI) datasets and climatic factors to analyze the spatiotemporal patterns of vegetation activities in an experimental area in Guangdong Province (China), as well as their links to changes in temperature (TEM), relative humidity (HUM), precipitation (PRE), sunshine duration (SUN), and surface runoff. The pruned exact linear time change point detection method (PELT) and the disturbance lag model (DLM) were used to understand the detailed ecological coverage status and time lag relationships between the EVI and climatic factors. The results indicate the following. (1) At the whole regional scale, a significant overall upward trend in the EVI variation was observed in 2001–2018. More specifically, there were two distinct periods with different trends, which were split by a turning point in 2005. PRE was the main climate-related driver of the rising EVI pre-2005, and the increase in TEM was the main climate factor influencing the forest EVI variation post-2006. (2) A three-month time lag effect was observed in the EVI response to relative humidity. The same phenomenon was found in the sunshine duration factor. (3) The EVI of farmlands (one type of land use) exhibited the largest lags between relative humidity and the sunshine duration factor, followed by grasslands and forests. (4) The comprehensive index of surface runoff could explain the time lags of vegetation activities, and the surface runoff value showed an apparently negative relationship with the vegetation coverage change. [ABSTRACT FROM AUTHOR]

Boreal forest areas in the Russian Far East contained very large intact forests. This particular area is considered one of the most productive and diverse forests in the boreal biome of the world, and it is also home to many endangered species. Zeya State Nature Reserve is located at the southern margin of the boreal forest area in the Russian Far East and has rich fauna and flora. However, the forest in the region faced large-scale forest fires and clearcutting for timber recently. The information of disturbances is rarely understood. This study aimed to explore the effects of disturbance and forest dynamics around the reserve. Our study used two-year overlaid Landsat images from Landsat 5 Thematic Mapper (TM) and 8 Operational Land Imager (OLI), to generate forest-cover-change maps of 1988–1999, 1999–2010, and 2010–2016. In this paper, we analyze the direction of forest successional stages, to demonstrate the effectiveness of this protected area in terms of preventing human-based deforestation on the vegetation indices. The vegetation indices included the normalized burn ratio (NBR), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI). The study provided information on the pattern of forest-cover change and disturbance area around the reserve. The NDWI was used to differentiate between water and non-water areas. The mean values of NBR and NDVI were calculated and determine the forest successional stages between burn, vegetation recovery, grass, mixed forest, oak forest, and birch and larch forest. The accuracy was assessed by using field measurements, field photos, and high-resolution images as references. Overall, our classification results have high accuracy for all three periods. The most disturbed area occurred during 2010–2016. The reserve was highly protected, with no human-disturbance activity. However, large areas from fire disturbance were found (137 km2) during 1999–2010. The findings also show a large area of disturbance, mostly located outside of the reserve. Mixed disturbance increased to almost 50 km2 during 2010–2016, in the buffer zone and outside of the reserve. We recommend future works to apply our methods to other ecosystems, to compare the forest dynamics and disturbance inside and outside the protected area. [ABSTRACT FROM AUTHOR]