This research examines the distribution features of 4960 caves across Guizhou Province, while probing the relationship between the caves' spatial patterns and geographic elements. This study is based on hydrogeological and topographic maps of Guizhou. ArcGIS software was used to process the adjacent index, spatial analysis, and coupling analysis of the caves altitude and longitude, as well as the rock properties, lithology, drainage and tectonic division of almost 5000 caves. Based on a point pattern analysis of Guizhou caves, the adjacent index is 0.53, and the coefficient of variation verified by Tyson polygon reached 72.469%. This figure reflects the clustered distribution pattern of the caves. Across the entire province, caves are divided into four concentrated areas and one weakly affected area. The four concentrated areas are Zunyi-Tongren, Bijie, Qianxinan-Liupanshui, and Guiyang-Anshun-Qinan. The one weakly affected zone is Qiandongnan. The most concentrated among them is the Guiyang-Anshun-Qiannan area, which covers 24.67% of the total province area, and accounts for 36.63% of the total province's caves. Cave distribution in Guizhou is characterized as dense in the western part and sparse in the eastern part. Under this study background, the natural elements of formation, including lithology, structure, climate, hydrology, and altitude, and their effects on the distribution, number, and spatial pattern of cave development is analyzed.

Abstract:The purpose of this study is to propose a GIS-based mechanism for diagnosing karst rocky desertification (KRD) ecosystem health. Using the Huajiang Demonstration Area in Guizhou Province as a case study, this research offers a multi-factor indicator system for diagnosing KRD ecosystem health. A set of geologic, environmental, and socio-economic health indicators were developed based on remote sensing images from field-investigation, hydrological, and meteorological monitoring data. With the use of grid GIS technology, this study gives an indicator for diagnosing the spatial expression of desertification at a 5 m × 5 m grid scale. Using spatial overlaying technology based on grid data, the temporal and spatial dynamics of ecosystem health in the Huajiang Demonstration Area were tracked over a 10 year time span. The results of the analysis indicate that ecosystem health in the Huajiang Demonstration Area varies regionally, and has overall improved over time. The proportion of healthy area increased from 3.7% in 2000 to 8.2% in 2010. However, unhealthy and middle-health areas still accounted for 78.7% of the total area by 2010. The most obvious improvement of ecosystem health was in an area where comprehensive control measures for curbing KRD were implemented. These results suggest that comprehensive control of KRD can effectively mitigate ecosystem deterioration and improve ecosystem health in karst regions of South China.

Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.

Non-point source pollution is an important source of ecological risk in karst lakes. The process of source–sink landscapes is the main pathway of pollution migration and plays an important role in water quality. In this study, the ecological risk evolution in the past 30 years was studied in a karst lake watershed with 495 sub-basins as the basic evaluation unit, and the risk assessment model of non-point source pollution was improved by using rainfall and fertilizer application. The results show that (1) the area of cultivated land shrank significantly, with forest land and construction land showing an upward trend in general; (2) the layout of the sink landscape continuously shrank, while the source landscape gradually expanded, and the space of high load values further increased and shifted from a flower-like layout distribution to concentrated contiguity, with some values exceeding 0.5; (3) the 252 sub-watersheds of the sink landscape migrated from very low risk to low risk, while the risk of the source landscape changed from medium risk to high and very high risk in 48 sub-watersheds; and (4) in terms of the overall trend of ecological risk transformation of the source–sink landscape, the transformation of sink landscapes to source landscapes was greater than that of source landscapes to sink landscapes, and the overall ecological risk showed an increasing trend.

Wang, K.; Zhou, Z.; Liao, J., and Fu, Y., 2015. The application of high resolution SAR in mountain area of Karst tobacco leaf area index estimation model.Karst mountain is cloudy and rainy weather, surface broken, and land distribution is not concentrated, crop inter-planting, which cause the conventional remote sensing monitoring methods cannot meet the need of tobacco real-time monitoring. In order to realize the real-time monitoring of modern tobacco agriculture, study on the selection of high resolution synthetic aperture radar was applied in the mountainous area of Karst tobacco monitoring model. Guizhou qingzhen national modern tobacco agriculture base as the research area, in growth and maturity of tobacco, analysis different polarization of HH/VV and the bands HH/VV radar brightness value and the tobacco leaf area index correlation, construct the linear regression model and the two order polynomial model to inversion tobacco leaf area index. The results show that different polarization combinations of two kinds of model SAR brightness and LAI have better fitting degree, a linear HH/VV model inversion accuracy ratio, which achieve 91%.The results of the study can be timely access to tobacco growth status information, to provide data support and policy decision for tobacco monitoring and yield estimation. ADDITIONAL INDEX WORDS: Karst mountain, SAR brightness, leaf area index, estimation model, tobacco. . Commonly used radar band name and its frequency, wavelength range control and division. Aerospace Electronic Warfare, 2, 60-64. Kang, Q.; Jiang, S.B., and Zhang, R., 2008. Synthetic aperture radar image and camouflage technology research. Journal of the Institute of Logistics Engineering, 24(3), 108. Kovacs, J.M.; Vandenberg, C.V.; Wang, J.F., and Flores-Verdugo, F., 2008. The use of multipolarized spaceborne sar backscatter for monitoring the health of a degraded mangrove forest.