Search

Forest cover rate is the principal indice to reflect the forest acount of a nation and region. In view of the difficulty of accurately calculating large-scale forest area by traditional statistical survey methods, it is proposed to extract China forest area based on Google Earth Engine platform. Trained by the enough samples selected through the Google Earth software, there are nine different random forest classifiers applicable to their corresponding zones. Using Landsat 8 surface reflectance data of 2018 year and the modified forest partition map, China forest cover is generated on the Google Earth Engine platform. The accuracy of China's forest coverage achieves 89.08%, while the accuracy of Global Forest Change datasets of Maryland university and Japan’s ALOS Forest/Non-Forest forest product reach 87.78% and 84.57%. Besides, the precision of tropical/subtropical forest, temperate coniferous forest as well as nonforest region are 83.25%, 87.94% and 97.83%, higher than those of other’s accuracy. Our results show that by means of the random forest algorithm and enough samples, tropical and subtropical broadleaf forest, temperate coniferous forest and nonforest partition can be extracted more accurately. Through the computation of forest cover, our result shows that China has a area of 220.42 million hectare in 2018.

The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition, classification and land surface parameter inversion using remote sensing data, which is the basis of remote sensing data application. In this study, a Control No-Changed Set (CNCS) radiometric normalization method is proposed to realize spectral information transformation of multi-sensor data, which is based on the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), and includes automatic selection and step-by-step optimization of no-change pixels. The No-Changed set (NC) is obtained by selecting the original no-change pixels between the target image and the reference image according to the linear relationship. In the obtained original no-change regions, IR-MAD rules with iterative control are used to fix the final no-change pixels, after regression modeling and calculation, the normalized images are obtained. The method is tested on multi-images from multi-sensors in three groups of experiments (GF-1 WFV and Landsat-8 OLI, GF-1 PMS and Sentinel-2 MSI, and Landsat-8 OLI and Sentinel-2 MSI) with different landcover areas. The results of radiometric normalization are evaluated qualitatively and quantitatively. The data of the three groups of experiments have a high correlation (correlation coefficient r values > 0.85), indicating that they can be used together as complementary data. The Root Mean Squared Error (RMSE) values calculate from the NC between the reference and normalized target images are much smaller than those between the reference and original target images. The radiometric colour composition effects, and the typical ground objects spectral reflective curves of the reference and normalized target images are very similar after radiometric normalization. These results indicate that the CNCS method considers the linear relationship of the no-change pixels and is effective, stable, and can be used to improve the consistency of SR of multi-images from multi-sensors.

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.