In order to give full play to the application efficiency of land remote sensing satellites for natural resources and strengthen the protection of cultivated land in China, this paper introduces a two-stage intelligent extraction strategy for erosion gullies. This strategy integrates deep learning object detection with interactive semantic segmentation.Initially, an iterative optimization method of object detection samples and models based on incremental learning and confidence filtering is proposed, achieving automatic identification of erosion gullies. Then, an interactive semantic segmentation method based on edge constraint is adopted to extract the precise contours of erosion gullies semi-automatically. An erosion gully extraction experiment was conducted in Heilongjiang Province of China, utilizing satellite images with a spatial resolution of 2 meters. Accuracy assessment was performed using GF-7 satellite imagery and verified through field verification. The results indicate that the Precision of erosion gully extraction in test area was 95.4%, the Recall was 93.0%, and the F1_score was 94.2%. The feasibility and accuracy of the proposed method has been verified by the experiments.

As an important part of the renewable energy, photovoltaic power generation industry has developed rapidly all around China in recent years, however some land use problems have also emerged. Therefore it is of great significance to monitor the number and distribution of photovoltaic power stations timely and accurately with high-resolution satellite images for the healthy development of photovoltaic industry. Combined with the improved DeepLab V3+ model and the ResNeSt-50 backbone network, the paper designs an effective photovoltaics extraction semantic segmentation algorithm and trains the new extraction model iteratively by making full use of big and various photovoltaic land samples. Photovoltaics are extracted accurately all over China with Chinese high-resolution satellite images, following a series of post-processing algorithms, such as binarizing, small and pseudo targets automatic removing, etc. Results show that the accuracy rate of photovoltaic land extraction is about 72.36% and the recall rate is about 91.06%. This precision is good enough for photovoltaic land extraction nationwide annually and the proposed deep learning model is efficient, small and can widely be used with other natural resources target extraction.

Surface water is an irreplaceable strategic resource for human survival and social development. It is of great significance to fully grasp the quantity, spatial distribution and dynamic changes of surface water in China accurately, quickly and timely. With the rapid development of Chinese remote sensing satellite industry, monitoring surface water in whole China quarterly has become a task of challenge but achievable. This paper analyzes and compares the advantages and disadvantages of existing algorithms for surface water extraction. And a practical and useful algorithm workflow is proposed. Based on 2-meter resolution multi-spectral satellite images, the paper adopts the regional fast-growing water extraction algorithm combined with water element buffers. 1,265 above level 3 rivers, their associated 2,128 reservoirs and 2,953 above-1-km2 natural lakes in China have been fast extracted quarterly with high-precision. Practice has shown that the automatic extraction algorithm is one of the most effective algorithms for realizing the automatic monitoring of large-scale surface water.

At present, the Synthetic Aperture Radar (SAR) is one of the few remote sensing methods that can realize rapid all-day and all-weather mapping in difficult areas of surveying and mapping, and has unique advantages incomparable with traditional optical remote sensing technology. And SAR polarimetric scattering mechanism provides important theoretical basis for surface radar target recognition. The paper adopts the P-band polarization data acquired by Chinese Academy of Surveying and Mapping SAR system (CASMSAR) as well as the C-band polarization data from Radarsat-2 and use the Freeman-Durden decomposition method to analyze and compare the difference of six typical terrain targets' scattering characteristics of different band SAR images. Results are as follows: (1) the volume scattering values of vegetation at different height are relatively high in C-band SAR image, which can be distinguished from non-vegetation, but the scattering characteristics between vegetations are confusing. (2) For P-band SAR image, there is a great difference in the volume scattering characteristics between vegetations at different height, while little difference in the odd scattering characteristics among bare soil, water area, and burnt wheat fields. (3) The volume scattering characteristics of vegetations at high height are obvious in P-band SAR image, while those at low height are obvious in C-band SAR image.

Remote sensing derived water area and volume have been widely used in large lakes monitoring. The seed point method was used to monitor the water area and volume of Poyang Lake, as an important international wetland and the largest fresh water lake in China, from 2019 to the flood season in 2020 using ZY3, GF-1, GF-3, GF-6, and BJ-2 data. Furthermore, historical water area and water volume changes of Poyang Lake were analyzed utilizing public data set. The results showed that the water area and water volume of Poyang Lake changed significantly in recent sixty years. It also fluctuated violently during the high water and low water seasons from 2019 to 2020. In the past sixty years, the minimum and maximum water area was 1190.73 km2in 1960 and 31 79.31 km2in 2019, respectively, and the water volume increased by 1853.62 million cubic meters. The water area difference of Poyang Lake between the high water season and the low water season was more than quadruple, which varied from 3179.21 km2in the third quarter in 2019 to 674.01 km2in the first quarter in 2020, and the water volume decreased by 489.95 million cubic meters. The flood in 2020 inundated 7834.15 hectares of planting land, 2300.81 hectares of forest and grass coverage, 906.51 hectares of desert and bare land, 2170.87 hectares of water area, 101.78 hectares of housing construction area, and 73.15 hectares of railway and highway. The relevant results provide a scientific basis for the construction of Poyang Lake ecological economic zone and the ecological protection of Poyang Lake wetland and a decision-making reference for flood control and disaster relief in Poyang Lake area.

Based on high resolution satellite images (ZY3, GF1) and Landsat MSS/TM/ETM+ data, the spatial and temporal variation of mangroves in Jiulong River estuary, Fujian from 1978 to 2018 were extracted by combining spectral prior knowledge and visual interpretation. The results showed that the area of mangrove forests has experienced three stages, respectively, slow increase (1980-1990), rapid expansion (1990-2013) and the rate of growth slowdown (2013-2018). In 1978, the area of mangrove resources was only 98.09 hectares, and it increased to 404.85 hectares in 2018. The constant combination of artificial planting and natural recovery was the main reason for maintaining the steady growth of mangrove areas. Meanwhile, land use policies, human activities, and ecological factors also exerted influence on the variation of mangrove in Jiulong River estuary.

Investigating the characteristics of dynamic changes of land use in the water source area of South-to-North Water Transfer Project (SNWTP) is of great significance to ameliorate the environmental quality to guarantee adequate, high quality water diverted into Beijing and Tianjin. The spatial and temporal characteristics of land use in the water source core area of middle route of South-to-North Water Transfer Project from 2009 to 2015 were analyzed by RS and GIS technologies based on national geoinformation survey data and basic surveying and mapping data, the results as follow: (1) the land was mainly covered with forest, which was accounting for about 80% of the total area. The area of cropland was reduced obviously, contrarily, the area of grassland increased prominently during the period from 2009 to 2015. The total coverage of wood and grass land rose lightly. (2) the land use transforming mainly occurred among cropland, woodland and grassland. Cropland was mainly changed to woodland and grassland, distributed in the altitude below 1000m and the slope above 15°. (3) the ecological engineering construction and reservoir resettlement policy had provided an effective backdrop for above changes. 53.47% of slope cropland was changed to woodland and grassland. However, there was still a considerable area of woodland, grassland transformation into slope cropland. The government should strengthen the protection of ecological environment, especially in the altitude bellow 1000m and the slope between 15° to 25°, in order to reduce the man-made environment destruction to the water supplying area.