Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ($R^2=0.74$) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

Accurate information on grassland above-ground biomass (AGB) is critical to better understanding the carbon cycle and conserve grassland resources. As a climate-sensitive key ecological function area, it is important to accurately estimate the grassland AGB of the Tibetan Plateau. Sentinel-2 (S2) images have advantages in reducing mixed pixels and the scale effect for remote sensing, while the data volume is correspondingly larger. In order to improve the estimation accuracy while reducing the data volume required for AGB estimation and improving the computational efficiency, this study used the Recursive Feature Elimination (RFE) algorithm to find the optimal feature set and compared the performance of the Cubist, Gradient Boosting Regression Tree (GBRT), random forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms for estimating AGB. In this study, ten S2 bands, ten S2-derived vegetation indexes, 218 pieces of AGB field survey data, four types of meteorological data and three types of topographic data were used as the alternative input features for the AGB estimation model. The impurity and permutation importance were used as the feature importance calculation method input to the RFE, and the Cubist, GBRT, RF and XGBoost algorithms were used to construct the AGB estimation models. The results showed that the RF algorithm based on the monthly average temperature (T), elevation, Normalized Difference Phenology Index (NDPI), Normalized Difference Infrared Index (NDII) and Palmer Drought Severity Index (PDSI) performed best ($R^2$ = 0.8838, RMSE = 35.05 g/m${}^2$, LCCC = 2.44, RPPD = 0.91). The above findings suggest that the RF model based on the features related to temperature, altitude, humidity and leaf water content is beneficial to estimate the grassland AGB on the Tibetan Plateau.

In the context of carbon neutrality, forest cover change detection has become a key topic of global environmental monitoring. As a large-scale monitoring technique, remote sensing has received obvious attention in various land cover observation applications. With the rapid development of deep learning, remote sensing change detection combined with deep neural network has achieved high accuracy. In this paper, the deep neural network is used to study forest cover change with Landsat images. The main research ideas are as follows. (1) A Siamese detail difference neural network is proposed, which uses a combination of concatenate weight sharing mode and subtract weight sharing mode to improve the accuracy of forest cover change detection. (2) The self-inverse network is introduced to detect the change of forest increase by using the sample data set of forest decrease, which realizes the transfer learning of the sample data set and improves the utilization rate of the sample data set. The experimental results on Landsat 8 images show that the proposed method outperforms several Siamese neural network methods in forest cover change extraction.

Keywords: Spatiotemporal Principles/Patterns, Eco-environmental Assessment, Eco-environmental Quality Index (EQI), Land Cover/Use, Buffers，Township Administrative Units， Daye City Mine exploitation has a significant impact on the ecological environment status of the surroundings. To analyze the impact of Tonglvshan Mining area to its surroundings, this paper adopted the spatiotemporal methodology based on the extracted Eco-environmental Quality Index (EQI) to analysis the extent and degree of the effect. The spatiotemporal methodologies are based on two scales: buffers and administrative units. EQI includes Biological Abundance Index (BAI), Vegetation Index (VI), Water Network Density Index (WNDI), and Land Degradation Index (LDI). The weight of each Index was determined by the analytic hierarchy process (AHP) and scores of the experts. The calculating of EQI was referenced to the standard “Technical criterion for Eco-environment Status Evaluation” （HJ/T192-2006）and the “Standards for Classification and Gradation of Soil Erosion” （SL 190-96）. Considering ecological and environmental characteristics relevant to China, this method has been widely used to study the environment status of specific regions in China. The assessment based on buffers adopted the radius of 300m, 500m, 700m, 1000m, 1500m, 2000m, 2500m, 3000m, 3500m, and 4000m as the buffers in 3 typical miners respectively. The calculated result indicates that, the REI is increasing with the radius and the increasing rate becoming smaller until REI is stable. Which means the effect of miner is getting weaker with the distance to the miner is increasing and the effect is diminished when the distance is far enough. The analysis of the 3 typical miner shows that the extent and degree of the effect of miner relates not only with the area of the miner, but also with type of mineral resource, the status of mining and the ecological restoration. The assessment was also carried out by calculating the EQI in 14 administrative units in Daye city in 2000, 2005, and 2010. The study shows that the EQI is decreasing in 14 units from 2000 to 2010. The spatiotemporal analysis of the type and area of land cover in 14 units within ten years period ranging from 2000 to 2010 shows that the mainly factor to affect the eco-environment status is mine exploitation and urban expansion.