Study of the desertification index based on the albedo-MSAVI feature space for semi-arid steppe region

Wu, Zhenhua; Lei, Shaogang; Bian, Zhengfu; Huang, Jiu; Zhang, Yong

doi:10.1007/s12665-019-8111-9

Cited by 64 publications

(42 citation statements)

References 34 publications

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“…However, the evaluation accuracy was observed to slightly differ in case of different levels of desertification; mild desertification exhibited the best precision (93.5%), followed by slight desertification (89.6%) and moderate desertification (89.2%). Severe (88.9%) and intensive (87.5%) desertification exhibited considerably lower accuracies because the field-observed points in the severeand intensive-erosion regions were less, affecting the accuracy of the evaluation results (Saowanee 2016;Wang et al 2019;Wu, Lei, et al 2019). The overall precision of the albedo-MSAVI point-to-line model was larger (93.8%) than that of the albedo-MSAVI point-to-point model.…”

Section: Resultsmentioning

confidence: 99%

“…Based on the above analysis, the two categories of the feature space models derived from albedo-MSAVI exhibited better applicability to monitor the desertification in Naiman Banner because the albedo could appropriately reflect the process of land degradation, which intensified with serious desertification (Wei et al 2018;Wu, Shi, et al 2019). Moreover, considering the bare soil line problem, MSAVI could considerably eliminate or reduce the influence of the soil and vegetation canopy background (Wu, Lei, et al 2019). However, the inversion accuracy of the albedo-MSAVI pointto-line model was larger than that of the albedo-MSAVI point-to-point model because the nonlinear relations between the albedo and MSAVI and the effects of soil background were fully considered (Zeng and Feng 2005;Xia et al 2019).…”

Section: Resultsmentioning

confidence: 99%

“…Wei et al (2018) analyzed the effects and applicability of different feature space models with respect to the extraction of the desertification information of the Mongolian plateau based on four sensitive parameters, i.e., NDVI, topsoil grain size index, and land-surface albedo. Wu, Lei, et al (2019) proposed a desertification monitoring index based on the albedo-modified soiladjusted vegetation index (MSAVI) feature space for semi-arid steppe regions and obtained improved results. observed that the albedo-MSAVI feature space model exhibited better applicability when compared with other models to monitor the desertification information in Naiman Banner.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Desertification detection model in Naiman Banner based on the albedo-modified soil adjusted vegetation index feature space using the Landsat8 OLI images

Wen

Guo

Zang

et al. 2020

Geomatics, Natural Hazards and Risk

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Desertification detection model in Naiman Banner based on the albedo-modified soil adjusted vegetation index feature space using the Landsat8 OLI images

Wen

Guo

Zang

et al. 2020

Geomatics, Natural Hazards and Risk

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“…Desertification studies show that, with the aggravation of desertification, the surface vegetation is seriously damaged and biomass and surface vegetation coverage are reduced, resulting in a decrease in the Vigor of the landscape ecosystem. Therefore, Semi-Arid Steppe Desertification Index (SASDI) [ 41 ] was chosen as an indicator to measure the Vigor of natural ecosystems. Water Loss and Soil Erosion (WLSE) is an important surface process.…”

Section: Methodsmentioning

confidence: 99%

Assessment of Landscape Ecological Health: A Case Study of a Mining City in a Semi-Arid Steppe

Lei

et al. 2019

IJERPH

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The ecological status of the semi-arid steppes in China is fragile. Under the long-term and high-intensity development of mining, the ecological integrity and biodiversity of steppe landscapes have been destroyed, causing soil pollution, grassland degradation, landscape function defect, and so on. Previous studies have mainly focused on ecosystem health assessment in mining areas. Landscape ecological health (LEH) pays more attention to the interactions between different ecosystems. Therefore, the ecological assessment of mining cities is more suitable on a landscape scale. Meanwhile, the existing LEH assessment index systems are not applicable in ecologically fragile areas with sparse population, underdeveloped economy, and in relatively small research areas. The purpose of this study was to construct a LEH assessment index system and evaluate the LEH of a mining city located in a semi-arid steppe. Xilinhot is a typical semi-arid steppe mining city in China. The contradictions between the human, land and ecological environment are serious. A new model Condition, Vigor, Organization, Resilience, and Ecosystem (CVORE) model was constructed that integrated five subsystems (services) from the perspectives of ecology, landscape ecology, mining science, and geography. This study used the CVORE model to systematically evaluate the LEH in Xilinhot city in terms of five LEH levels, including very healthy, healthy, sub-healthy, unhealthy and morbid landscape. Research results show that the areas of the very healthy, healthy, sub-healthy, unhealthy and morbid landscapes are 13.23, 736.35, 184.5, 66.76 and 20.63 km2, respectively. The healthy landscapes area accounts for 72.08% and most grasslands are healthy. The sub-healthy landscapes are mainly located around areas with higher disturbances due to human activities. The morbid or unhealthy landscapes are concentrated in the mining areas. The proposed CVORE model can enrich the foundations for the quantitative assessment of Landscape Ecological Health of Mining Cities in Semi-arid Steppe (LEHMCSS). This study provided a new LEH assessment approach (CVORE model), which can support landscape ecological restoration, ecological environmental protection and urban planning of the semi-arid steppe mining cities.

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“…In recent years, the feature space model has been widely utilized to quantitatively obtain the desertification information, which could better reflect the land surface change information of desertification [8], [10], [14]. The feature space models have been constructed with some sensitive parameters for desertification, such as normalized difference vegetation index (NDVI), topsoil grain size index (TGSI), modified soil adjusted vegetation index (MSAVI), land surface albedo [15], [16]. However, the proposed monitoring models were almost linear, which ignored the complicated and non-linear relationships between different parameters of feature space.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Monitoring Model of Desertification in Naiman Banner Based on Feature Space Utilizing Landsat8 Oli Image

Guo

Wen

2020

IEEE Access

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Current feature space models of desertification were almost linear, which ignored the complicated and non-linear relationships among variables for monitoring desertification. Fully considering the influencing factors of the desertification process in Naiman Banner, four sensitive indices including MSAVI, NDVI, TGSI, and Albedo have been selected to construct five feature spaces. Then, the precisions of different feature space models for monitoring desertification information (including non-linear and linear models) have been compared and analyzed. The non-linear Albedo-MSAVI feature space model for Naiman Banner has higher efficiency with the overall precision of 90.1%, while that of Albedo-TGSI had the worst precision with 0.69. Overall, the feature space model (non-linear) of Albedo-MSAVI has the highest applicability for monitoring the desertification information in Naiman Banner.INDEX TERMS Albedo-MSAVI, monitoring model, feature space, Landsat8 OLI, Naiman Banner.

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Study of the desertification index based on the albedo-MSAVI feature space for semi-arid steppe region

Cited by 64 publications

References 34 publications

Desertification detection model in Naiman Banner based on the albedo-modified soil adjusted vegetation index feature space using the Landsat8 OLI images

Desertification detection model in Naiman Banner based on the albedo-modified soil adjusted vegetation index feature space using the Landsat8 OLI images

Assessment of Landscape Ecological Health: A Case Study of a Mining City in a Semi-Arid Steppe

An Optimal Monitoring Model of Desertification in Naiman Banner Based on Feature Space Utilizing Landsat8 Oli Image

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