scholarly journals STUDY ON KARST INFORMATION IDENTIFICATION OF QIANDONGNAN PREFECTURE BASED ON RS AND GIS TECHNOLOGY

2018 ◽  
Vol XLII-3 ◽  
pp. 2121-2125
Author(s):  
M. Yao ◽  
G. Zhou ◽  
W. Wang ◽  
Z. Wu ◽  
Y. Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Karst Area ◽  
Karst Region ◽  
Karst Collapse ◽  
Distribution Map ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Karst Areas

Karst area is a pure natural resource base, at the same time, due to the special geological environment; there are droughts and floods alternating with frequent karst collapse, rocky desertification and other resource and environment problems, which seriously restrict the sustainable economic and social development in karst areas. Therefore, this paper identifies and studies the karst, and clarifies the distribution of karst. Provide basic data for the rational development of resources in the karst region and the governance of desertification. Due to the uniqueness of the karst landscape, it can’t be directly recognized and extracted by computer in remote sensing images. Therefore, this paper uses the idea of “RS + DEM” to solve the above problems. this article is based on Landsat-5 TM imagery in 2010 and DEM data, proposes the methods to identify karst information research what is use of slope vector diagram, vegetation distribution map, distribution map of karst rocky desertification and other auxiliary data in combination with the signs for human-computer interaction interpretation, identification and extraction of peak forest, peaks cluster and isolated peaks, and further extraction of karst depression. Experiments show that this method achieves the “RS + DEM” mode through the reasonable combination of remote sensing images and DEM data. It not only effectively extracts karst areas covered with vegetation, but also quickly and accurately locks down the karst area and greatly improves the efficiency and precision of visual interpretation. The accurate interpretation rate of karst information in study area in this paper is 86.73 %.

scholarly journals The Impact of Urbanization on Ecosystem Health in Typical Karst Areas: A Case Study of Liupanshui City, China

2020 ◽  
Vol 18 (1) ◽  
pp. 93
Author(s):  
Yangling Zhao ◽  
Rui Han ◽  
Nan Cui ◽  
Jingbiao Yang ◽  
Luo Guo
Keyword(s):  
Ecosystem Health ◽  
Raw Materials ◽  
Management Policy ◽  
Karst Area ◽  
Karst Region ◽  
Rocky Desertification ◽  
Rapid Urbanization ◽  
Karst Areas ◽  
Impact Of Urbanization ◽  
The Impact

The karst region of Southwest China is one of the largest continuous karst areas in the world, and the ecosystem in the karst region is extremely fragile. The city of Liupanshui, a typical karst area in southwestern China, has provided the main energy and raw materials during China’s rapid urbanization in the past few decades. With the continuous deterioration of the environment in Liupanshui and from the viewpoint of sustainable development strategies, research on ecosystem health (ESH) and the assessments of correlations between urbanization and ESH plays an important role in regional sustainable eco-environmental development. Therefore, the impact of urbanization on the ecosystem health of the study area was discussed in this study using a series of remote sensing images and socio-economic data from 1990 to 2015. Studies showed that Liupanshui is undergoing rapid urbanization, and the growth of urbanized land reached a peak between 2010 and 2015. From 1990 to 2015, the level of ESH in Liupanshui trended downward and then increased. During 2000 to 2010, due to the policy of returning farmland to grassland and forestland, the substantial increase in woodland and grassland and the management policy of mining areas have caused a turn in ESH. Although the value of ecosystem health in 2010–2015 increased, the process of urbanization is rapid, so we should pay more attention to the trend in future ecosystem health changes. The findings revealed that urbanization significantly negatively affects the ecosystem health of Liupanshui, and mining has the greatest impact. Therefore, in future urban development, strengthening the management of resource extraction and the supervision of environmental protection, continuing to return farmland to grassland and forestry, and controlling rocky desertification can improve the health of the urban ecosystem in the study area.

scholarly journals Crops planting structure and karst rocky desertification analysis by Sentinel-1 data

2021 ◽  
Vol 13 (1) ◽  
pp. 867-879
Author(s):  
Lingyu Wang ◽  
Quan Chen ◽  
Zhongfa Zhou ◽  
Xin Zhao ◽  
Jiancheng Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Arable Land ◽  
Strong Support ◽  
Karst Area ◽  
Good Effect ◽  
Mountainous Areas ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Spatial Coupling

Abstract Accurate crop planting structure (CPS) information and its relationship with the surrounding special environment can provide strong support for the adjustment of agricultural structure in areas with limited cultivated land resources, and it will help regional food security, social economy, and ecological balance adjustment. However, due to the perennial cloudy, rainy, and scattered arable land in Karst mountainous areas, the monitoring of planting structure by traditional remote sensing methods is greatly limited. In this regard, we focus on synthetic aperture radar (SAR) remote sensing, which can penetrate clouds and rain, without light constraints to image. In this article, based on parcel-based temporal sequence SAR, the CPS in South China karst area was extracted by deep learning technology, and the spatial coupling relationship between CPS and karst rocky desertification (KRD) was analyzed. The results showed that: (a) The overall accuracy of CPS classification was 75.98%, which proved that the geo-parcel-based time series SAR has a good effect for the CPS mapping in the karst mountainous areas; (b) Through the analysis of the spatial relationship between the planting structure and KRD, we found that the lower KRD level caused the simpler CPS and the higher KRD grade caused more complex CPS and more richer landscape types. The spatial variation trend of CPS landscape indicates the process of water shortage and the deepening of KRD in farmland; (c) The landscape has higher connectivity (Contagion Index, CI 0.52–1.73) in lower KRD level and lower connectivity (CI 0.83–2.05) in higher KRD level, which shows that the degree of fragmentation and connection of CPS landscape is positively proportional to the degree of KRD. In this study, the planting structure extraction of crops under complex imaging environment was realized by using the farmland geo-parcels-based time series Sentinel-1 data, and the relationship between planting structure and KRD was analyzed. This study provides a new idea and method for the extraction of agricultural planting structure in the cloudy and rainy karst mountainous areas of Southwest China. The results of this study have certain guiding significance for the adjustment of regional agricultural planting structure and the balance of regional development.

scholarly journals KARST ROCKY DESERTIFICATION INFORMATION EXTRACTION BASED ON THE DECISION TREE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 367-373
Author(s):  
C. J. Su ◽  
T. Yue ◽  
L. Jiang ◽  
X. M. Li ◽  
W. G. Wang
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Information Extraction ◽  
Karst Area ◽  
Accuracy Evaluation ◽  
Remote Sensing Images ◽  
Rocky Desertification ◽  
Autonomous Region ◽  
Guangxi Zhuang Autonomous Region ◽  
Wide Range

Abstract. Rocky desertification is a common geo-ecological disasters in China are mainly distributed in southwest karst region, and a wide range of further deterioration. Based on the theory of decision tree Guangxi rocky information extraction, selection of experimental data of Guangxi Zhuang Autonomous Region in 2005 TM image. First of remote sensing images after geometric correction image registration and other pretreatment. Secondly based on binary model of pixel, the Guangxi Zhuang Autonomous Region NDVI values and vegetation cover and slope analysis combining the results of Guangxi Zhuang Autonomous Region, the use of decision tree classification of remote sensing images, and finally get different levels of Guangxi Zhuang Autonomous Region rocky area and spatial distribution. The experimental results showed that: 2005 Guangxi rocky area of about 22,000 km2, accounting for 9% of the total land area in Guangxi, accounting for 24.30% of the karst area the overall classification accuracy of 89.03%, Kappa coefficient was 0.8417. From the classification results and the accuracy evaluation shows that the use of the information extracted rocky achieve better results.

scholarly journals KARST ROCKY DESERTIFICATION ANALYSIS BASED ON HISTORICAL DISP DATA TAKE TWO REGIONS FOR EXAMPLE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 871-874
Author(s):  
J. Liu ◽  
G. Q. Zhou ◽  
B. Jia ◽  
T. Yue ◽  
X. Y. Peng
Keyword(s):  
Southwest China ◽  
Temporal Evolution ◽  
Driving Forces ◽  
The United States ◽  
Living Environment ◽  
Karst Area ◽  
Visual Interpretation ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Hydrogeological Data

Abstract. Karst rocky desertification (KRD) is used to characterize the processes that transform a karst area covered by vegetation and soil into a rocky landscape almost devoid of soil and vegetation. This situation seriously affects and threatens the living environment and standards of local people, which results in a series of social problems. In view of the importance and harmfulness of KRD, many scholars have studied the spatial and temporal evolution of KRD and its driving forces. In this paper, the Visual Interpretation Marks of Rocky Desertification in Southwest China in 1960s are constructed by using the DISP image of the United States, combined with DEM data and Hydrogeological data. The area of rocky desertification in Guangnan and Funing counties, where rocky desertification is more serious, is about 2457.729 km2. The area of rocky desertification can be used as the basic data for studying the historical changes in southwestern China by researchers.

scholarly journals EXTRACTION OF ROCKY DESERTIFICATION FROM DISP IMAGERY: A CASE STUDY OF LIUPANSHUI, GUIZHOU, CHINA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1061-1065
Author(s):  
G. Zhou ◽  
Z. Wu ◽  
W. Wang ◽  
Y. Shi ◽  
G. Mao ◽  
...  
Keyword(s):  
Guizhou Province ◽  
Karst Area ◽  
Visual Interpretation ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Correction Model ◽  
Ground Control Points ◽  
Proposed Model ◽  
Typical Type

Karst rocky desertification is a typical type of land degradation in Guizhou Province, China. It causes great ecological and economical implications to the local people. This paper utilized the declassified intelligence satellite photography (DISP) of 1960s to extract the karst rocky desertification area to analyze the early situation of karst rocky desertification in Liupanshui, Guizhou, China. Due to the lack of ground control points and parameters of the satellite, a polynomial orthographic correction model with considering altitude difference correction is proposed for orthorectification of DISP imagery. With the proposed model, the 96 DISP images from four missions are orthorectified. The images are assembled into a seamless image map of the karst area of Guizhou, China. The assembled image map is produced to thematic map of karst rocky desertification by visual interpretation in Liupanshui city. With the assembled image map, extraction of rocky desertification is conducted.

scholarly journals Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach

2021 ◽  
Vol 13 (6) ◽  
pp. 1060
Author(s):  
Luc Baudoux ◽  
Jordi Inglada ◽  
Clément Mallet
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Time Frame ◽  
Superior Performance ◽  
Land Cover Mapping ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Corine Land Cover ◽  
Land Cover Map ◽  
By Products

CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.

Analysis of Ecological Service Value in Vulnerable Karst Area of Southwestern Guizhou

2011 ◽  
Vol 347-353 ◽  
pp. 2241-2244
Author(s):  
Feng Tai Zhang ◽  
La Chun Wang ◽  
Wei Ci Su ◽  
Yu Hua Liang ◽  
Ji Xin Shao ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
Ecosystem Service ◽  
Local Economy ◽  
Karst Area ◽  
Karst Region ◽  
Ecosystem Service Value ◽  
Rocky Desertification ◽  
Service Value ◽  
Unit Value ◽  
Southwestern Guizhou

The domestic and foreign evaluations of ecosystem service value are difficult to draw on results accepted by the public and academia. This reflects the research methods are still not mature, need to continue to be improved. In this paper, an attempt has been made to give urban unit value of ecosystem services and set up the values per unit area in southwestern Guizhou of China, in accordance with unit value of global ecosystem services developed by Costanza, et al., Chinese one by Xie, et al. and the actual situation of karst region. The analysis revealed that in the study area, the total ecosystem service value is $1.876×109 in 2006, equivalent to 104.3% of 2006 GDP (Gross Domestic Product), $1.799×109（1US$=7.8136，2006）. If the rocks change into forest in the study area, ecosystem service value will add $0.221×109, equivalent to 12.28% of GDP in 2006. Therefore, we conclude that the ecosystem services value is higher, compared to the local economy. In addition, the rocky desertification area is larger, and has already seriously influenced ecosystem service function. The tasks of ecological environment protection, propaganda and education in this region are of great significance.

scholarly journals A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images

2020 ◽  
Vol 12 (18) ◽  
pp. 3005
Author(s):  
Maofan Zhao ◽  
Qingyan Meng ◽  
Linlin Zhang ◽  
Die Hu ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Computational Cost ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Segmentation Evaluation ◽  
Parameters Selection ◽  
Object Based ◽  
Geographic Object ◽  
The Difference

The segmentation of remote sensing images with high spatial resolution is important and fundamental in geographic object-based image analysis (GEOBIA), so evaluating segmentation results without prior knowledge is an essential part in segmentation algorithms comparison, segmentation parameters selection, and optimization. In this study, we proposed a fast and effective unsupervised evaluation (UE) method using the area-weighted variance (WV) as intra-segment homogeneity and the difference to neighbor pixels (DTNP) as inter-segment heterogeneity. Then these two measures were combined into a fast-global score (FGS) to evaluate the segmentation. The effectiveness of DTNP and FGS was demonstrated by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. For this experiment, the ‘‘Multi-resolution Segmentation’’ algorithm in eCognition was adopted in the segmentation and four typical study areas of GF-2 images were used as test data. The effectiveness analysis of DTNP shows that it can keep stability and remain sensitive to both over-segmentation and under-segmentation compared to two existing inter-segment heterogeneity measures. The effectiveness and computational cost analysis of FGS compared with two existing UE methods revealed that FGS can effectively evaluate segmentation results with the lowest computational cost.

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