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Vegetation is an important type of land cover. Long-term, large-scale, and high-precision vegetation monitoring is of great significance for ecological environment investigation and regional sustainable development in protected areas. This paper develops a long-term remote sensing monitoring method for vegetation by calculating the normalized difference vegetation index (NDVI) based on the Google Earth Engine (GEE) cloud platform and Landsat satellite remote sensing images. First, based on Landsat long-term satellite images and GEE, the spatiotemporal distribution map of the NDVI is accurately drawn. Subsequently, the NDVI is accurately classified, and the time trend analysis of the NDVI is conducted based on the NDVI mean trend graphs, transition matrices, etc. Then, combined with Moran's I, high/low clusters, and other methods, the spatial pattern characteristics of the NDVI are analyzed. Finally, climate factors, terrain factors, and anthropologic factors are considered comprehensively. An analysis of the factors affecting the evolution of the NDVI is performed. Taking Zhoushan Island, China, as an example, an experiment is conducted, and the results reveal that (1) the average NDVI exhibits a decreasing trend from 1985 to 2022, decreasing from 0.53 in 1985 to 0.46 in 2022. (2) Regarding vegetation index transitions, the high NDVI areas (0.6–1) exhibit the most substantial shift toward moderately high NDVI values (0.4–0.6), covering an area of 83.10 km2. (3) There is an obvious spatial agglomeration phenomenon in the NDVI on Zhoushan Island. The high-high NDVI clusters and the significant hot spots are predominantly concentrated in the island's interior regions, while the low-low NDVI clusters and the significant cold spots are mainly situated along the coastal areas. (4) The DEM, slope, and temperature have a greater influence among the single factors on the spatial pattern distribution of the NDVI in 2015. There are significant differences in the spatial pattern distribution of the NDVI between the temperature and DEM, temperature and slope, DEM and precipitation, slope and precipitation, aspect and population, and aspect and gross domestic product (GDP). The DEM and slope, DEM and temperature, and DEM and population are three sets of factors with a strong influence on spatial pattern interaction. This study provides data support for the scientific management of vegetation resources on Zhoushan Island and is of great significance to the sustainable development of the island region. [ABSTRACT FROM AUTHOR]

Models for bathymetry retrieval from multispectral images have not considered the errors caused by tidal fluctuation. A rigorous bathymetric model that considers the variation in tide height time series, including the tide height calculation and instantaneous tide height correction at the epoch of satellite flight into the bathymetric retrieval model, is proposed in this paper. The model was applied on Weizhou Island, located in Guangxi Province, China, and its accuracy verificated with four check lines and seven checkpoints. A scene from the Landsat 8 satellite image was used as experimental data. The reference ("true") water depth data collected by a RESON SeaBat 7125 multibeam instrument was used for comparison analysis. When satellite-derived bathymetry is compared, it is found that maximum absolute error, mean absolute error, and RMSE have decreased 54, 45, and 30% relative to that of the traditional model in the entire test field. The accuracy of the water depths retrieved by our model increased 30 and 56% when validated using four check lines and seven checkpoints, respectively. Therefore, it can be concluded that the model proposed in this paper can effectively improve the accuracy of bathymetry retrieved from Landsat 8 images. [ABSTRACT FROM AUTHOR]

Due to the country's rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city. [ABSTRACT FROM AUTHOR]

The first successful sand-control was achieved in the Mu Us Desert by local people in the 1950–1960s, and their experience and approach have been extended to the whole Ordos and Northern China since then. The objective of this paper is to assess comprehensively the effectiveness of sand-control in 15 counties in and around Mu Us using multitemporal satellite images and socioeconomic data. After atmospheric correction, Landsat TM and OLI images were harnessed for land cover classification based on the ground-truth data and for derivation of the GDVI (generalized difference vegetation index) to extract the biophysical changes of the managed desert and desertification. Climatic, socioeconomic, environmental and spatial factors were selected for coupling analysis by multiple linear and logistic regression models to reveal the driving forces of desertification and their spatial determinants. The results show that from 1991 to 2020, 8712 km2 or 63% of the desert has been converted into pastures and shrublands with a greenness increase of 0.3509 in GDVI; the effectiveness of sand-control is favored by the rational agropastoral activities and policies; though desertification occurs locally, it is associated with both climatic and socioeconomic factors, such as wind speed, precipitation, water availability, distance to roads and animal husbandry. [ABSTRACT FROM AUTHOR]

Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs. [ABSTRACT FROM AUTHOR]