Fuzzy model‐based assessment and monitoring of desertification using MODIS satellite imagery

Lin, Meng‐Lung; Chen, Cheng‐Wu; Wang, Qiu‐Bing; Cao, Yu; Shih, Jyh-Yi; Lee, Yung-Tan; Chen, Chen‐Yuan; Wang, Shin

doi:10.1108/02644400910985152

Cited by 70 publications

(35 citation statements)

References 38 publications

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“…gov/data_access/data_pool) (Lin et al, 2009). Census data. Information on the structural and functional properties of vegetation was obtained from MODIS-NDVI (normalized difference vegetation index) satellite data recorded between 2000 and 2010.…”

Section: Datamentioning

confidence: 99%

“…In particular, we used the MOD13 (vegetation indices, Collection 5), available at full spatial resolution (250-m) as a 16-day composite from the NASA LPDAAC dataset (Land Processes Distributed Active Archive Center, https://lpdaac.usgs. gov/data_access/data_pool) (Lin et al, 2009). Census data. Effects of anthropogenic pressure on the soil were estimated by assuming that agriculture and grazing could trigger or exacerbate soil degradation processes.…”

Section: Datamentioning

confidence: 99%

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Indicators for the estimation of vulnerability to land degradation derived from soil compaction and vegetation cover

Imbrenda

D’Emilio

Lanfredi

et al. 2014

European J Soil Science

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The increasing incidence of soil degradation requires monitoring strategies adapted to the scale of interest. We propose an innovative approach to the large-scale investigation of predominantly green areas where healthy vegetation is the clue to identifying vital and protected soil. We started with the well-established environmentally sensitive areas (ESA) model, which provides an evaluation of overall land vulnerability that is based on structural, biophysical and socio-economic indicators. To improve the estimation of soil vulnerability, we integrated information on the potential extent of compaction caused by agricultural machinery. To differentiate the degree of degradation in areas that show similar potential vulnerability we included information on the temporal variation of photosynthetic activity in the model for early detection of the soil's reduced ability to carry out ecosystem services and functions. Satellite data were used to obtain vegetation cover properties to integrate into the ESA model to improve estimation of land degradation in southern Italy, where both natural and agricultural areas are widespread. Inclusion of the temporal dimension is essential for discriminating between different degrees of soil productivity in areas characterized by similar structural conditions. Our procedure is adaptable, user-friendly and easily transferable

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Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Indicators for the estimation of vulnerability to land degradation derived from soil compaction and vegetation cover

Imbrenda

D’Emilio

Lanfredi

et al. 2014

European J Soil Science

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“…Substantial research has been conducted, but there have been no accurate and cost-effective methods available because of the large, remote, and sparsely populated area, large number of mixed pixels on images, and difficulty of Remote Sens. 2019, 11, 3038 5 of 23 collection of field measurements [17][18][19][47][48][49][50][51][52]. Thus, there is a strong need to develop an accurate and cost-effective method to monitor the land degradation and desertification in the northern and northwestern China.…”

Section: Introductionmentioning

confidence: 99%

A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

et al. 2019

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China has been facing serious land degradation and desertification in its north and northwest arid and semi-arid areas. Monitoring the dynamics of percentage vegetation cover (PVC) using remote sensing imagery in these areas has become critical. However, because these areas are large, remote, and sparsely populated, and also because of the existence of mixed pixels, there have been no accurate and cost-effective methods available for this purpose. Spectral unmixing methods are a good alternative as they do not need field data and are low cost. However, traditional linear spectral unmixing (LSU) methods lack the ability to capture the characteristics of spectral reflectance and scattering from endmembers and their interactions within mixed pixels. Moreover, existing nonlinear spectral unmixing methods, such as random forest (RF) and radial basis function neural network (RBFNN), are often costly because they require field measurements of PVC from a large number of training samples. In this study, a cost-effective approach to mapping PVC in arid and semi-arid areas was proposed. A method for selection and purification of endmembers mainly based on Landsat imagery was first presented. A probability-based spectral unmixing analysis (PBSUA) and a probability-based optimized k nearest-neighbors (PBOkNN) approach were then developed to improve the mapping of PVC in Duolun County in Inner Mongolia, China, using Landsat 8 images and field data from 920 sample plots. The proposed PBSUA and PBOkNN methods were further validated in terms of accuracy and cost-effectiveness by comparison with two LSU methods, with and without purification of endmembers, and two nonlinear approaches, RF and RBFNN. The cost-effectiveness was defined as the reciprocal of cost timing relative root mean square error (RRMSE). The results showed that (1) Probability-based spectral unmixing analysis (PBSUA) was most cost-effective and increased the cost-effectiveness by 29.3% 29.3%, 33.5%, 50.8%, and 53.0% compared with two LSU methods, PBOkNN, RF, and RBFNN, respectively; (2) PBSUA, RF, and RBFNN gave RRMSE values of 22.9%, 21.8%, and 22.8%, respectively, which were not significantly different from each other at the significance level of 0.05. Compatibly, PBOkNN and LSU methods with and without purification of endmembers resulted in significantly greater RRMSE values of 27.5%, 32.4%, and 43.3%, respectively; (3) the average estimates of the sample plots and predicted maps from PBSUA, PBOkNN, RF, and RBFNN fell in the confidence interval of the test plot data, but those from two LSU methods did not, although the LSU with purification of endmembers improved the PVC estimation accuracy by 25.2% compared with the LSU without purification of endmembers. Thus, this study indicated that the proposed PBSUA had great potential for cost-effectively mapping PVC in arid and semi-arid areas.

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“…United Nations Convention to Combat Desertification ( UNCCD ) [2004] defines desertification as land degradation in the drylands (e.g., arid, semiarid and dry subhumid areas), caused by various factors including natural climatic variations and human intervention. Deserts in Gobi and Taklamakan in Mongolia and in northwestern China across East Asia have expanded substantially over the past several decades [ Yu et al , 2004; Lin et al , 2009; Jeong et al , 2011b]. The socioeconomic and health impacts of the desertification, and consequent transboundary transports of enormous yellow sands originating from those regions are serious concerns not only for China and the adjacent countries in East Asia but also for the western U.S. [ Husar et al , 2001; Zhang et al , 2003], so that desertification mitigation has been a vital environmental issue across these regions.…”

Section: Introductionmentioning

confidence: 99%

Impact of vegetation on land‐atmosphere coupling strength and its implication for desertification mitigation over East Asia

Myoung

Choi

et al. 2012

J. Geophys. Res.

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[1] Desertification of the East Asian drylands and the consequent dust transport have been serious concerns for adjacent Asian countries as well as the western United States. Tree planting has been considered one applicable strategy to mitigate the desertification. However, the desired effect of the tree planting would not be brought to fruition unless the newly planted trees change the coupling characteristics between the land and the atmosphere. Based on this perception, we attempt to clarify the effects of vegetation on the coupling strength between the atmosphere and land surface, and we suggest the most efficient areas of tree planting for desertification mitigation in East Asia. Using regional vegetation-atmosphere coupled model simulations, coupling strength with and without vegetation was computed and compared with each other. An increased vegetation fraction reduces the coupling strength in June, July, and August (JJA), primarily due to decreased evapotranspiration variability. This effect is pronounced over the Manchurian Plains and the highly populated areas of Beijing and Tianjin. The reduced coupling strength tends to weaken feedback between soil moisture and precipitation as a maintenance mechanism of warm season droughts in the midlatitudes and subsequently decrease the probability of droughts, a finding that is reflected in the enhanced JJA mean soil moisture. However, some drylands like the eastern edges of the Gobi desert present marginal or even opposite changes in coupling strength, meaning a limited effect of vegetation on relieving droughts. Therefore, given limited financial and human resources, acupuncture-like afforestation, i.e., concentrated tree planting in a particular region where the coupling strength can be substantially reduced by vegetation, is an effective strategy to secure long-standing desertification mitigation.

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Fuzzy model‐based assessment and monitoring of desertification using MODIS satellite imagery

Abstract: 2009),"Fuzzy model-based assessment and monitoring of desertification using MODIS satellite imagery", Engineering Computations, Vol. 26 Iss 7 pp. 745 -760 Permanent link to this document: http://dx.

Cited by 70 publications

References 38 publications

Indicators for the estimation of vulnerability to land degradation derived from soil compaction and vegetation cover

Indicators for the estimation of vulnerability to land degradation derived from soil compaction and vegetation cover

A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

Impact of vegetation on land‐atmosphere coupling strength and its implication for desertification mitigation over East Asia

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