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Globally, animals that are range-restricted are frequently becoming species of conservation concern, in part due to competitive exclusion by phylogenetically and ecologically similar species that are more tolerant of human disturbance. However, climate and land use changes to natural landscapes can create pockets of refugia for range-restricted species. Western gray squirrels (Sciurus griseus) are native to the west coast of North America, principally California and western Oregon. Over the past several decades, Western Gray Squirrel populations have declined in human-dominated areas, with increased competition from introduced congeneric species native to eastern North America cited as a primary driver. Despite declines in their established range west of the Pacific Crest in western North America, western gray squirrels are extending their range into the Great Basin, where they were not historically found. Using a network of remote camera traps deployed across the Sierra Nevada–Great Basin ecotone in northwestern Nevada, we detected western gray squirrels across 16 of 100 camera-trapping sites. The majority of detections were located in piñon–juniper woodland, a land cover type not previously occupied by this species. Occupancy modeling revealed that western gray squirrels were equally likely to occur in piñon–juniper woodland compared to mature pine forest that they occupy elsewhere in their range. A species distribution model parameterized with historical gray squirrel observations (pre-1950), indicated increased climatic suitability for the species on the eastern side of the Sierra Nevada in recent decades, which may have facilitated this range expansion. Our findings reveal the potential for species declining in their historical range to colonize novel habitats that become increasingly suitable as a result of human-driven changes to ecosystems. [ABSTRACT FROM AUTHOR]

• Land cover dynamics in dry tropical environments are often overlooked. • Classification accuracy of 79–86% was attained using Landsat data. • Primary forest declined, while secondary forest increased. • Deforestation has continued to increase with low rates of recovery. • Forest connectivity declined by 22% between 1972 and 2016. Land cover dynamics in the tropical dry environments of sub-Saharan Africa are not well understood compared to humid environments, especially on a national scale. Previous studies describing land cover in the dry tropics are spatially or temporally constrained. This study presents the first long-term (1972–2016) nationwide land cover dynamics and forest connectivity analyses for Zambia. We employed 300 Landsat images and an object-based image analysis (OBIA) approach with the Random Forests (RF) classifier to map nine land covers at six time steps (1972, 1984, 1990, 2000, 2008 and 2016). Post-classification change detection was used to understand the dynamics, while landscape metrics were derived to assess the forest structural connectivity. Overall accuracies ranging from 79 to 86% were achieved. In 1972, 48% of Zambia was covered by primary forest and 16% was covered by secondary forest. By 2016, 62.74% of Zambia had undergone changes, with primary forest decreasing by 32% and secondary forest increasing by 23%. Our results showed that forests have been recovering by 0.03 to 1.3% yr−1 (53, 000–242, 000 ha yr-1); however, these rates are markedly lower than deforestation rates (- 0.54 to - 3.05% yr-1: 83, 000–453, 000 ha yr-1). Annual rates of change varied by land cover, with irrigated crops having the largest increase (+ 3.19 yr-1) and primary forest having the greatest decrease (-2.48% yr-1). Forest connectivity declined by 22%, with primary forest having the greatest decline, while the connectivity for secondary and plantation forest increased. We showed here that land cover in Zambia is highly dynamic with high rates of change, low forest recovery and decline in forest connectivity. These findings will aid in land use planning, reporting for the forthcoming 2020 Global Forest Resources Assessment, and carbon accounting, especially under the reduction in carbon emissions from deforestation and degradation programme (REDD+). [ABSTRACT FROM AUTHOR]

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

The Zagros forests in Western Iran are valuable ecosystems that have been seriously damaged by human interference (harvesting the wood and forest sub-products, converting the forests to the agricultural lands, and grazing) and natural events (drought events and fire). In this study, we generated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests of Shirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field data for protective management. First, we assessed the accuracy of Google Earth data using 300 random field plots in 10 different land cover types. For land cover mapping, we evaluated the performance of four supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD), neural network (NN), and support vector machine (SVM)). The accuracy of the land cover maps was assessed using a set of 150 stratified random plots in Google Earth. We mapped the forest canopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. We calculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots). The linear regression between the NDVI values and the TCC% was used to obtain the predictive model of TCC% based on the NDVI. The results showed that Google Earth data yielded an overall accuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopy cover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, which is highly significant. The results also showed that the linear regression model is a good predictive model for TCC% estimation based on the NDVI (r2 = 0.864). The results can be used as a baseline for decision-makers to monitor land cover change in the region, whether produced by human activities or natural events and to establish measures for protective management of forests. [ABSTRACT FROM AUTHOR]

Land cover changes, especially excessive economic forest plantations, have significantly threatened the ecological security of West Dongting Lake wetland in China. This work aimed to investigate the spatiotemporal dynamics of forests in the West Dongting Lake region from 2000 to 2018 using a reconstructed monthly Landsat NDVI time series. The multi-type forest changes, including conversion from forest to another land cover category, conversion from another land cover category to forest, and conversion from forest to forest (such as flooding and replantation post-deforestation), and land cover categories before and after change were effectively detected by integrating Breaks For Additive Seasonal and Trend (BFAST) and random forest algorithms with the monthly NDVI time series, with an overall accuracy of 87.8%. On the basis of focusing on all the forest regions extracted through creating a forest mask for each image in time series and merging these to produce an 'anytime' forest mask, the spatiotemporal dynamics of forest were analyzed on the basis of the acquired information of multi-type forest changes and classification. The forests are principally distributed in the core zone of West Donting Lake surrounding the water body and the southwestern mountains. The forest changes in the core zone and low elevation region are prevalent and frequent. The variation of forest areas in West Dongting Lake experienced three steps: rapid expansion of forest plantation from 2000 to 2005, relatively steady from 2006 to 2011, and continuous decline since 2011, mainly caused by anthropogenic factors, such as government policies and economic profits. This study demonstrated the applicability of the integrated BFAST method to detect multi-type forest changes by using dense Landsat time series in the subtropical wetland ecosystem with low data availability. [ABSTRACT FROM AUTHOR]