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Net primary production (NPP) supplies matter, energy, and services to facilitate the sustainable development of human society and ecosystem. The response mechanism of NPP to land use and climate changes is essential for food security and biodiversity conservation but lacks a comprehensive understanding, especially in arid and semi‐arid regions. To this end, taking the middle‐reaches of the Heihe River Basin (MHRB) as an example, we uncovered the NPP responses to land use and climate changes by integrating multisource data (e.g., MOD17A3 NPP, land use, temperature, and precipitation) and multiple methods. The results showed that (a) land use intensity (LUI) increased, and climate warming and wetting promoted NPP. From 2000 to 2014, the LUI, temperature, and precipitation of MHRB increased by 1.46, 0.58°C, and 15.76 mm, respectively, resulting in an increase of 14.62 gC/m2 in annual average NPP. (b) The conversion of low‐yield cropland to forest and grassland increased NPP. Although the widespread conversion of unused land and grassland to cropland boosted both LUI and NPP, it was not conducive to ecosystem sustainability and stability due to huge water consumption and human‐appropriated NPP. Urban sprawl occupied cropland, forest, and grassland and reduced NPP. (c) Increase in temperature and precipitation generally improved NPP. The temperature decreasing <1.2°C also promoted the NPP of hardy vegetation due to the simultaneous precipitation increase. However, warming‐induced water stress compromised the NPP in arid sparse grassland and deserts. Cropland had greater NPP and NPP increase than natural vegetation due to the irrigation, fertilizers, and other artificial inputs it received. The decrease in both temperature and precipitation generally reduced NPP, but the NPP in the well‐protection or less‐disturbance areas still increased slightly. [ABSTRACT FROM AUTHOR]

Present study has produced first detailed land‐cover map of Socotra Island. A Landsat 7 ETM+ dataset was used as a main source of remotely sensed data. From numerous reference points (more than 250) coming from the ground data verification the set of training fields and the set of evaluation fields were digitised. As a classification method the supervised maximum likelihood classification without prior probabilities was used in combination with rule‐based post‐classification sorting, providing results of sufficient accuracy and subject resolution. Estimates of the area and degree of coverage of particular land‐cover classes within Socotra Island have brought excellent overview on state of island biotopes. Overall accuracy of the map achieved is more than 80%, 19 terrestrial land‐cover classes (including three types of Shrublands, three types of Woodlands, two types of Forests and Mangroves) have been distinguished. It consequently allows estimates of the current and potential occurrence of endemic plant populations, proposals of management and conservation plans and agro‐forestry planning. [ABSTRACT FROM AUTHOR]

Fire is a major driver of land surface transformation in California Mediterranean-type shrublands (i.e. chaparral). The re-growth of leaves following fire impacts a wide variety of ecosystem processes and information on the post-fire recovery of leaf area index (LAI) is often required in eco-hydrologic modelling studies. A few studies have reported LAI values for chaparral, but none have tracked LAI dynamics over the entire post-fire recovery sequence. In this study we used a chronosequence approach with satellite imagery to determine the post-fire development sequence of LAI for chaparral shrublands in central California. Moreover, we explored how LAI varied with differences in annual antecedent precipitation conditions (APC) and physical site factors. LAI recovery following fire was most rapid during the first 15 years, after which it remained relatively constant with increasing stand age. For a given stand age, LAI varied nonlinearly with annual APC, while spatial variations in LAI were associated with differences in topographic aspect and landscape wetness potential. However, a better understanding of the nature and interaction of these controls on LAI is needed if realistic post-fire LAI trajectories (for historic, present and future periods) for eco-hydrological modelling studies in chaparral catchments are to be developed in the future. [ABSTRACT FROM AUTHOR]

The Kaxgar River Basin, a key section of the Tarim River Basin, is a typical ecologically fragile region that has undergone rapid changes to its spatial patterns over the preceding few decades. In particular, the expansion of salinized land has posed a severe threat to ecological restoration and economic development. This study monitored the rates and patterns of land use and land cover (LULC) changes in the plain area of Aketao County in the middle reaches of the Kaxgar River Basin. Five Landsat images (captured in 1990, 1998, 2002, 2013, and 2018) were divided into seven LULC types: built-up land, cultivated land, woodland and grassland, light-moderate salinized land, heavy salinized land, water areas, and bare land. Subsequently, their dynamic processes were analyzed. The results revealed that in 1990, the dominant LULCs were cultivated land, woodland and grassland, and bare land. Throughout the study period (from 1990 to 2018), the coverage of built-up land, cultivated land, bare land, water areas, and light-moderate salinized land increased; by contrast, that of the other LULC types decreased. The most marked LULC changes were the expansion of light-moderate salinized land (by 6.2% of the study area) and the shrinkage of woodland and grassland (by 9.4% of the study area). Almost all the analyzed LULC types underwent conversion to other types; such conversion occurred most frequently between 1998 and 2018. The conversions of woodland and grassland into cultivated land and light-moderate salinized land were the most notable phenomena. Another highly evident change was the conversion of heavy salinized land into bare land. These results revealed that the expansion of salinized land and the shrinkage of woodland and grassland in the study area were the most severe environmental changes. Therefore, ecological protection and salinization control are urgently required to enable local economic development while not exceeding the environmental carrying capacity and ensuring the safety of the "green corridor" in the lower reaches of the Kaxgar River Basin. [ABSTRACT FROM AUTHOR]

High-precision land use/land cover classification mapping derived from remote sensing supplies essential datasets for scientific research on environmental assessment, climate change simulation, geographic condition monitoring, and environmental management at global and regional scales. It is an important issue in the study of earth system science, and the coastal area is a hot spot region in this field. In this paper, the coastal areas of the Maritime Silk Road were used as the research object and a fusion method based on agreement analysis and fuzzy-set theory was adopted to achieve the fusion of three land use/land cover datasets: MCD12Q1-2010, CCI-LC2010, and GlobeLand30-2010. The accuracy of the fusion results was analyzed using an error matrix, spatial confusion, average overall consistency, and average type-specific consistency. The main findings were as follows. (1) After the establishment of reference data based on Google Earth, both the producer accuracy and user accuracy of the fusion data were improved when compared with those of the three input data sources, and the fusion data had the highest overall accuracy and Kappa coefficient, with values of 90.37% and 0.8617, respectively. (2) Various input data sources differed in terms of the correctly classified contributions and misclassified influences of different land use/land cover types in the fusion data; furthermore, the overall accuracy and Kappa coefficient between the fusion data and any one of the input data sources were far higher than those between any two of the input data sources. (3) The average overall consistency of the fusion data was the highest at 89.29%, which was approximately 5% higher than that of the input data sources. (4) The average type-specific consistencies of cropland, forest, grassland, shrubland, wetland, artificial surfaces, bare land, and permanent snow and ice in the fusion data were the highest, with values of 69.95%, 74.41%, 21.24%, 34.22%, 97.62%, 51.83%, 84.39%, and 2.46%, respectively; compared with the input data sources, the average type-specific consistencies of the fusion data were 0.61–20.32% higher. This paper provides information and suggestions for the development and accuracy evaluation of future land use/land cover data in global and regional coastal areas. [ABSTRACT FROM AUTHOR]

Understanding the spatial distribution of populations at a finer spatial scale has important value and foundation for many applications such as ecological environment protection, disaster risk assessment and rescue, business decision-making, regional planning. Remote sensing image products such as nighttime lighting and land cover/land use type, have been widely used to disaggregate a large number of population (such as China) with significant differences in distribution to generate a gridded population map of large scales. However, the natural factors such as the topography, the river network and road network were considered less in previous studies. At the same time, geographic big data (such as POIs) can also be high accurately distinguish population distribution. In addition, the remarkable progress in machine learning provides toolkits for demographers. Therefore, Using existing land use products, nighttime lighting, DEM, POIs and census data, we used Random Forest(RF) models to simulate complex nonlinear correlation between population dependent variable and other independent variables. Finally, we disaggregated China's county-level census data into a 1KM × 1KM grid. The gridded population density datasets obtained by this method can be used to analysis spatial-temporal patterns of population density and provide the potential of geospatial big data for mapping other socioeconomic parameters in the future. [ABSTRACT FROM AUTHOR]

In West Africa, more than 40 million people struggle to survive as subsistence farmers on the dry Sahel plateau bordering the Saharan desert. That number grows by approximately 3% every year. Across the region, fields of sorghum and millet are replacing shrubland as cropland expands and wood is harvested for cooking fuel. On the ground, the transformation is obvious, but its overall extent and impact were hard to measure until resource managers in Mali, Niger, Burkina Faso, and six other countries in the region began using sophisticated satellite photographs and vegetation maps to assess land-use changes and model the effect on soil erosion and fertility.