Relationship among vegetation variables and vegetation features of arid lands derived from satellite data

Ishiyama, Takashi; Tanaka, S.; Uchida, K.; Fujikawa, S.; Yamashita, Yasuhiro; Kato, Masatane

doi:10.1016/s0273-1177(01)00343-x

Cited by 8 publications

(2 citation statements)

References 3 publications

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“…This paper takes a direct approach to the analysis of desertification by combining multi-temporal NDVI data with a ground-based ecological survey to address the issue of desertification in Mongolia, a strategy recently employed in other dryland regions (Washington-Allen et al 2008). This approach, centered at water points, documents vegetation cover percentage at individual water sources while average values, both for the study area as well as a series of years, is provided by NDVI, often used as a proxy for pasture biomass in other countries (Ishiyama et al 2001, Kogan et al 2004, Kawamura et al 2005. If the statistical significance of the match between the two datasets is significant then NDVI should provide an historical perspective to land cover trends.…”

Section: Introductionmentioning

confidence: 99%

Tracking desertification on the Mongolian steppe through NDVI and field-survey data

Sternberg

Renchin

Middleton

et al. 2011

International Journal of Digital Earth

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Changing environmental and socio-economic conditions make land degradation, a major concern in Central and East Asia. Globally satellite imagery, particularly Normalized Difference Vegetation Index (NDVI) data, has proved an effective tool for monitoring land cover change. This study examines 33 grassland water points using vegetation field studies and remote sensing techniques to track desertification on the Mongolian plateau. Findings established a significant correlation between same-year field observation (line transects) and NDVI data, enabling an historical land cover perspective to be developed from 1998 to 2006. Results show variable land cover patterns in Mongolia with a 16% decrease in plant density over the time period. Decline in cover identified by NDVI suggests degradation; however, continued annual fluctuation indicates desertification Á irreversible land cover change Á has not occurred. Further, in situ data documenting greater cover near water points implies livestock overgrazing is not causing degradation at water sources. In combination of the two research methods Á remote sensing and field surveys Á strengthen findings and provide an effective way to track desertification in dryland regions.

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Section: Introductionmentioning

confidence: 99%

Tracking desertification on the Mongolian steppe through NDVI and field-survey data

Sternberg

Renchin

Middleton

et al. 2011

International Journal of Digital Earth

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“…According to the spectral characteristics of green plants, visible and near-infrared bands can be combined to form various vegetation indices (VIS). The vegetation index method is derived from the analysis of satellite multispectral data [61]. It is also a very important tool in the process of hyperspectral data processing of medium-and near-range plant diseases.…”

Section: Vegetation Indexmentioning

confidence: 99%

Hyperspectral Sensing of Plant Diseases: Principle and Methods

Wan

et al. 2022

Agronomy

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Pathogen infection has greatly reduced crop production. As the symptoms of diseases usually appear when the plants are infected severely, rapid identification approaches are required to monitor plant diseases at early the infection stage and optimize control strategies. Hyperspectral imaging, as a fast and nondestructive sensing technology, has achieved remarkable results in plant disease identification. Various models have been developed for disease identification in different plants such as arable crops, vegetables, fruit trees, etc. In these models, important algorithms, such as the vegetation index and machine learning classification and methods have played significant roles in the detection and early warning of disease. In this paper, the principle of hyperspectral imaging technology and common spectral characteristics of plant disease symptoms are discussed. We reviewed the impact mechanism of pathogen infection on the photo response and spectrum features of the plants, the data processing tools and algorithms of the hyperspectral information of pathogen-infected plants, and the application prospect of hyperspectral imaging technology for the identification of plant diseases.

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Linearized Vegetation Indices

Ünsalan

Boyer

2011

Multispectral Satellite Image Understanding

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Relationship among vegetation variables and vegetation features of arid lands derived from satellite data

Cited by 8 publications

References 3 publications

Tracking desertification on the Mongolian steppe through NDVI and field-survey data

Tracking desertification on the Mongolian steppe through NDVI and field-survey data

Hyperspectral Sensing of Plant Diseases: Principle and Methods

Linearized Vegetation Indices

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