Ecosystem health assessment is an important part of improving the management of national parks. In this paper, Shennongjia National Park is taken as the study region. By using satellite remote sensing data from 2000 to 2018, based on the Vitality Organization Resilience (VOR) model, an ecosystem health assessment is created and its spatiotemporal characteristics are analyzed. In the whole region, the ecosystem’s health level has gradually improved; the rate of improvement of the ecosystem’s health level from 2016 to 2018 has been 2.5-times that of the overall rate and the trend of improvement has been obvious. The rate of improvement of the ecosystem’s health level of non-nature protection areas has improved two-fold; the same is true of nature protection areas, and the stability change trend of the two areas has basically been the same. The establishment of national parks has played a significant role in promoting the health of the regional ecosystem. In future planning, relevant departments should pay attention to the ecological protection and restoration of the area and optimize the traditional area layout of Shennongjia National Park.
The normalized differential vegetation index (NDVI) for Landsat is not continuous on the time scale due to the long revisit period and the influence of clouds and cloud shadows, such that the Landsat NDVI needs to be filled in and reconstructed. This study proposed a method based on the genetic algorithm–artificial neural network (GA-ANN) algorithm to reconstruct the Landsat NDVI when it has been affected by clouds, cloud shadows, and uncovered areas by relying on the MODIS characteristics for a wide coverage area. According to the self-validating results of the model test, the RMSE, MAE, and R were 0.0508, 0.0557, and 0.8971, respectively. Compared with the existing research, the reconstruction model based on the GA-ANN algorithm achieved a higher precision than the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible space–time data fusion algorithm (FSDAF) for complex land use types. The reconstructed method based on the GA-ANN algorithm had a higher root mean square error (RMSE) and mean absolute error (MAE). Then, the Sentinel NDVI data were used to verify the accuracy of the results. The validation results showed that the reconstruction method was superior to other methods in the sample plots with complex land use types. Especially on the time scale, the obtained NDVI results had a strong correlation with the Sentinel NDVI data. The correlation coefficient (R) of the GA-ANN algorithm reconstruction’s NDVI and the Sentinel NDVI data was more than 0.97 for the land use types of cropland, forest, and grassland. Therefore, the reconstruction model based on the GA-ANN algorithm could effectively fill in the clouds, cloud shadows, and uncovered areas, and produce NDVI long-series data with a high spatial resolution.
As the important coal bases in northwestern China, the hydrological and ecological environment of Ordos, northern Shaanxi (Shanbei) and Shanxi Province has attracted more and more attention. Terrestrial water storage anomaly (TWSA) and precipitation, as important hydrological elements, play an important role in the distribution and growth of vegetation. In this paper, the Gravity Recovery and Climate Experiment (GRACE) satellite data, Tropical Rainfall Measuring Mission (TRMM) precipitation data, and the Remote Sensing Ecological Index (RSEI) were used to analyze the spatial-temporal changes and coupling relationships of TWSA, precipitation and ecological environment from 2002 to 2020. The numerical results showed the TWSA in the study area has a decreasing trend and the rates are -6.19mm/a, -7.67mm/a and -16.92mm/a for Ordos, Shanbei and Shanxi Province, respectively. On the contrary, the precipitation appeared an increasing trend and the rates are 0.35mm/a, 0.63mm/a and 0.18mm/a for these three sub-regions. It is found that the precipitation is not the main factor causing the variation of TWSA, but the coal mining activities and artificial irrigation activities, which is especially clear in the Taihang Mountains in eastern of Shanxi Province. The ecological environment has been improving, and TWSA and precipitation are the important hydrological factors causing this change. Precipitation is the main reason for improving the ecological environment in three sub-regions on a seasonal scale, especially in summer. The research results are helpful to understand the impact of hydrological changes on the ecological environment, which play an important role in environmental governance in coal mining areas.
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