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The Sentinel-2A satellite having embedded advantage of red edge spectral bands offers multispectral imageries with improved spatial, spectral and temporal resolutions as compared to the other contemporary satellites providing medium resolution data. Our study was aimed at exploring the potential of Sentinel-2A imagery to estimate Above Ground Biomass (AGB) of Subtropical Pine Forest in Pakistan administered Kashmir. We developed an AGB predictive model using field inventory and Sentinel 2A based spectral and textural parameters along with topographic features derived from ALOS Digital Elevation Model (DEM). Field inventory data was collected from 108 randomly distributed plots (0.l ha each) across the study area. The stepwise linear regression method was employed to investigate the potential relationship between field data and corresponding satellite data. Biomass and carbon mapping of the study area was carried out through established AGB estimation model with R (0.86), R2 (0.74), adjusted R2 (0.72) and RMSE value of 33 t/ha. Our results showed that first order textures (mean, standard deviation and variance) significantly contributed in AGB predictive modeling while only one spectral band ratio made contribution from spectral domain. Our study leads to the conclusion that Sentinel-2A optical data is a potential source for AGB estimation in subtropical pine forest of the area of interest with added benefit of its free of cost availability, higher quality data and long-term continuity that can be utilized for biomass carbon distribution mapping in the resource constraint study area for sustainable forest management.

Poverty restricts rural sustainable development and urban&ndash, rural integration. Agricultural development is an engine of rural economic development and poverty elimination. Agricultural structure adjustment and rural poverty alleviation in Yulin City from the agro-pastoral transition zone of Northern China were studied using statistical data. Our results showed that the rural poverty ratio in Yulin was 7.70% in 2017, with clearly higher trends in southeast regions and lower trends in northeast regions. Northern Yulin had a lower proportion of primary industry to gross domestic production and ratio of agriculture to animal husbandry than southern Yulin. The agricultural structure variability index and agricultural specialization index have changed little, while the actual growth rate of the rural per capita net income has dramatically declined in the last decade. The rural poverty ratio was positively correlated with proportion of primary industry and ratio of agriculture to animal husbandry. These results suggest that a reduced proportion of primary industry and animal husbandry development will promote rural poverty alleviation and future rural revitalization.

Land greening in China is regarded as contributing a great deal to greening of the Earth. The phenomenon is mainly attributed to climate change, arising atmospheric CO2 and ‘Grain for Green’ (GFG) land management policies. However, limited knowledge is known how much land greening is from contributions of the GFG practice. Therefore, the study took the typical region of the GFG practice, the Loess Plateau, as the study area, and used 1982–2015 satellite-observed GIMMS3g normalized difference vegetation index (NDVI) data, ERA-Interim climatic variables (precipitation, temperature and solar radiation) and atmospheric CO2 concentration data with the help of a developed TPRC-based NDVI model to derive GFG-induced NDVI after 1999. Furthermore, this study tracked the spatial-temporal dynamics of GFG-induced NDVI and assessed contributions of the GFG practice to regional vegetation changes. Results showed that satellite-observed NDVI and TPRC-based NDVI both exhibited an increasing spatial pattern from the northwestern to southeastern Loess Plateau, but their greening trends were separately 0.0022 and 0.0009 per year in 1982–2015 (p

An ecosystem model serves as an important tool to understand the carbon cycle in the forest ecosystem. However, the sensitivities of parameters and uncertainties of the model outputs are not clearly understood. Parameter sensitivity analysis (SA) and uncertainty analysis (UA) play a crucial role in the improvement of forest gross primary productivity GPP simulation. This study presents a global SA based on an extended Fourier amplitude sensitivity test (EFAST) method to quantify the sensitivities of 16 parameters in the Flux-based ecosystem model (FBEM). To systematically evaluate the parameters&rsquo, sensitivities, various parameter ranges, different model outputs, temporal variations of parameters sensitivity index (SI) were comprehensively explored via three experiments. Based on the numerical experiments of SA, the UA experiments were designed and performed for parameter estimation based on a Markov chain Monte Carlo (MCMC) method. The ratio of internal CO2 to air CO2 ( , canopy quantum efficiency of photon conversion , maximum carboxylation rate at 25 ( were the most sensitive parameters for the GPP. It was also indicated that were influenced by temperature throughout the entire growth stage. The result of parameter estimation of only using four sensitive parameters (RMSE =1.657) is very close to that using all the parameters (RMSE = 1.496). The results of SA suggest that sensitive parameters, such as strongly influence on the forest GPP simulation, and the temporal characteristics of the parameters` SI on GPP and NEE were changed in different growth. The sensitive parameters were a major source of uncertainty and parameter estimation based on the parameter SA could lead to desirable results without introducing too great uncertainties.

Sustainable land use, targeted poverty alleviation (TPA) and integrated urban/town development are key issues for the poverty-stricken counties of China. This paper explored the spatio-temporal pattern and driving forces of construction land change in Longzhou, a typical poverty-stricken border county in southwest China, based on high-resolution remote sensing images and field investigations. The results showed that, from 2011 to 2016, the construction land in Longzhou increased by 10.08%. The largest increase shown by urban built-up areas (37.74%) followed by rural residential (25.48%), industrial and mining (20.96%), and transportation areas (15.49%) as well by tourist facility areas (0.33%). The accelerated urban development, rural housing construction, and construction of industrial parks and transportation facilities are main factors driving construction land expansion, while the implementation of TPA strategy and the booming of border trade activities are important policy drivers of the construction land expansion. Some of the major implications for improving poverty-alleviation-oriented land use policies were further discussed. The authors argue that land use policy innovation could play a key role in breaking the Matthew effect (poor get poorer, rich get richer) of China's uneven regional development and generating and accelerating the transformation development of poverty-stricken counties. It is fundamental to meet the demand of construction land for TPA, meanwhile raise the intensive level of land utilization. The authors suggest that the government need to further improve and implement preferential land use policies for poverty-stricken counties from the provincial level, appropriately increase the quotas of construction land, and effectively strengthen the rational, efficient and intensive use of the quotas of construction land, and provide solid land policy support for poverty alleviation and thus achieve the Sustainable Development Goals with higher quality.

Sugarcane is an important type of cash crop and plays a crucial role in global sugar production. Clarifying the magnitude of sugarcane planting will likely provide very evident supports for local land use management and policy-making. However, sugarcane growth environment in complex landscapes with frequent rainy weather conditions poses many challenges for its rapid mapping. This study thus tried and used 10-m Sentinel-2 images as well as crop phenology information to map sugarcane in Longzhou county of China in 2018. To minimize the influences of cloudy and rainy conditions, this study firstly fused all available images in each phenology stage to obtain cloud-free remote sensing images of three phenology stage (seedling, elongation and harvest) with the help of Google Earth Engine platform. Then, the study used the fused images to compute the normalized difference vegetation index (NDVI) of each stage. A three-band NDVI dataset along with 4000 training samples and 2000 random validation samples was finally used for sugarcane mapping. To assess the robustness of the three-band NDVI dataset with phenological characteristics for sugarcane mapping, this study employed five classifiers based on machine learning algorithms, including two support vector machine classifiers (Polynomial-SVM and RBF-SVM), a random forest classifier (RF), an artificial neural network classifier (ANN) and a decision tree classifier (CART-DT). Results showed that except for ANN classifier, Polynomial-SVM, RBF-SVM, RF and CART-DT classifiers displayed high accuracy sugarcane resultant maps with producer’s and user’s accuracies of greater than 91%. The ANN classifier tended to overestimate area of sugarcane and underestimate area of forests. Overall performances of five classifiers suggest Polynomial-SVM has the best potential to improve sugarcane mapping at the regional scale. Also, this study observed that most sugarcane (more than 75% of entire study area) tends to grow in flat regions with slope of less than 10°. This study emphasizes the importance of considering phenology in rapid sugarcane mapping, and suggests the potential of fine-resolution Sentinel-2 images and machine learning approaches in high-accuracy land use management and decision-making.

The biogeophysical effects of land cover change (LCC) on land surface temperature are largely understood, especially in tropical and boreal zones, but fewer studies have reported the biogeophysical consequences of ecological restoration of mining areas located in arid and semi-arid temperate zones. In this study, we employed 2000–2015 satellite-based net radiation (Rn) and latent heat flux (LE) data from mining areas (taking Dongsheng in Inner Mongolia as an example) and adjacent ecological restoration areas to understand the biogeophysical effect of revegetation around mining areas. The results showed that revegetation increased Rn (0.25 ± 0.17 W/m2) and LE (0.43 ± 0.26 W/m2). The warming effect of increased Rn (∆Rn), however, was completely offset by the cooling effect of increased LE (∆LE), thus revegetation caused an overall cooling effect of −0.18 ± 0.17 W/m2. Land surface temperature (LST) from mining areas and adjacent ecological restoration areas also showed that revegetation decreased LST (∆LST being −0.2 ± 0.1 K). The difference of ∆Rn and ∆LE explained 27% of the ∆LST variations. These findings reinforce our understanding for the potential climatic benefits of the ecological restoration around mining areas in arid and semi-arid temperate zones.