scholarly journals RETRACTED ARTICLE: Using remote sensing to quantify the fractional cover of vegetation and exposed bedrock within a complex landscape: applications for karst rocky desertification monitoring

2012 ◽  
Vol 185 (1) ◽  
pp. 1025-1025
Author(s):  
Yuemin Yue ◽  
Bo Liu ◽  
Kelin Wang ◽  
Ru Li ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Fractional Cover ◽  
Retracted Article ◽  
Complex Landscape ◽  
Desertification Monitoring

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 Comparing Methods of Rocky Desertification Monitoring at the Sub-pixel Scale in a Highly Heterogeneous Karst Region

2018 ◽  
Author(s):  
Xiangkun Qi ◽  
Chunhua Zhang ◽  
Kelin Wang
Keyword(s):  
Remote Sensing ◽  
Anthropogenic Disturbances ◽  
Spectral Mixture Analysis ◽  
Karst Region ◽  
Mixture Analysis ◽  
Rocky Desertification ◽  
Fractional Cover ◽  
Rocky Outcrop ◽  
Rocky Outcrops ◽  
Spectral Mixture

Karst rocky desertification (KRD) is a process where strong anthropogenic disturbances and exposure of carbonate bedrock occurs in fragile karst ecosystems. The fractional cover of rocky outcrops is a key indicator and mechanistic driver of KRD and can be accurately assessed using remote sensing technology. Nevertheless, rugged karst terrain relief can cause shadow effects on satellite imagery and combine with high heterogeneity of karst landscapes to prevent fractional cover retrievals. In this study, we explored the feasibility of applying multispectral high spatial resolution ALOS imagery for fractional cover extraction of rocky outcrops. We selected the dimidiate pixel model (DPM), which has been applied in previous studies, and spectral mixture analysis (SMA; including simple endmember spectral mixture analysis (SESMA) and multiple endmember spectral mixture analysis (MESMA)) to explore the feasibility of using remote images for KRD monitoring and improve accuracy for estimating fractions. Results from MESMA achieved high overall accuracy (76.4%) in monitoring percentage of rocky outcrop fraction in the study area. SESMA appears to underestimate percentage of rocky outcrop likely because the development of KRD was driven by complex factors (soil erosion, dissolution and anthropogenic disturbances). This results in spectral reflectance of rocky outcrop being variable in different settings. Predicted exposed bedrock coverage using SESMA and MESMA was similar in sun-lit and shaded areas although predictions from SESMA were smaller than reference data. DPM underestimated the fractional cover of rocky outcrops on south-facing slopes and overestimated it in shaded areas. Furthermore, SESMA and MESMA effectively reduced topographic effects. We conclude that it is better to extract percentage of rocky outcrop using MESMA in the karst region of southwestern China. Remote sensing is emerging as a feasible method to extract surface condition information in heterogeneous and rugged landscapes.

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.

Karst Rocky Desertification Remote Sensing Monitoring by Integrating Land Use Diagnoses in Southeast of Yunnan Province

2013 ◽  
Vol 444-445 ◽  
pp. 869-873
Author(s):  
Shu Gan ◽  
Xi Ping Yuan ◽  
Gang Sun ◽  
Xiao Lun Zhang ◽  
Ying Li
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Yunnan Province ◽  
Actual Condition ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Remote Sensing Monitoring ◽  
Case Study Area ◽  
Remote Sensing Image Processing

Karst rocky desertification is one of the serious environment problems in southwest of China. In this study, a typical county with karst rocky desertification which located in Southeast of Yunnan province is selected as a work area at first. Based on the datum collection about land use status and field verification surveying in study area, the technique of remote sensing image processing and GIS spatial analysis was integrated used to monitor the karst rocky desertification status and got its information in different degree. Analysis for karst rocky desertification spatial distributing, the main result is that there is more amount proportion of karst rocky desertification land cover in case study area and these large numbers patches of karst rocky desertification mosaic beset in the different land use types, such as forest, plantation and artificial town or other infrastructure building. So it is stringent need to deepen research the karst rocky desertification development and its spatial expand. Another result include that remote sensing monitoring for the karst rocky desertification is one of the important advance technique method, but it also need to fuse more another assistant information according to the actual condition in case study area, for example, the land use status in quo is a good means to assistant remote sensing monitoring karst rocky desertification by spatial restrict effect.

scholarly journals Comparing Remote Sensing Methods for Monitoring Karst Rocky Desertification at Sub-pixel Scales in a Highly Heterogeneous Karst Region

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xiangkun Qi ◽  
Chunhua Zhang ◽  
Kelin Wang
Keyword(s):  
Satellite Imagery ◽  
Landscape Heterogeneity ◽  
Spectral Mixture Analysis ◽  
Karst Region ◽  
Topographic Effects ◽  
Mixture Analysis ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Fractional Cover ◽  
Spectral Mixture

Abstract Rugged karst terrain relief that creates shadows in satellite imagery, combined with high karst landscape heterogeneity stand in the way of fractional cover retrieval on karst rocky desertification (KRD) monitoring. In this study, we explored the feasibility of applying multispectral high spatial resolution Advanced Land Observing Satellite (ALOS) imagery for the fractional cover extraction of rocky outcrops. Dimidiate pixel model (DPM) and spectral mixture analysis (SMA) approaches (including simple endmember spectral mixture analysis and multiple endmember spectral mixture analysis) were selected to explore their feasibility for KRD monitoring through accuracy improvement for fraction estimation. Results showed fractional cover retrievals at the sub-pixel scale is essential in highly heterogeneous karst landscapes. Indeed, mixed pixels accounted for 93.7% of the study area in southwest China. Multiple endmember spectral mixture analysis achieved high overall accuracy (80.5%) in monitoring the percentage of rocky outcrop land cover. Furthermore, the predicted exposed bedrock coverage via spectral mixture analysis were similar in sunlit and shadow areas for the same surface types. This reflected that SMA methods could effectively reduce topographic effects of satellite imagery to improve the accuracy of fractional cover extraction at sub-pixel level in heterogeneous and rugged landscapes.

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