Sensitivity of vegetation indices to substrate brightness in hyper-arid environment: The Makhtesh Ramon Crater (Israel) case study

Schmidt, Heike; Karnieli, Arnon

doi:10.1080/01431160110063779

Cited by 32 publications

(11 citation statements)

References 28 publications

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“…According to Schmidt and Karnieli (2001) NDVI tends to be sensitive to dark surfaces while SAVI shows more sensitivity to bright surfaces. This could be one of the reasons why the NDVI methods outperformed the SAVI method, since the study area had bright, dry background soils, especially in the higher parts of the dry and extremely dry heath areas.…”

Section: Resultsmentioning

confidence: 96%

Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM® and ETM+® imagery

Nordberg

Evertson²

2005

Land Degrad Dev

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This paper describes the extent to which the normalized difference vegetation index (NDVI) in combination with image regression used on satellite data can indicate vegetation cover decreases caused by increased exploitation of Swedish mountains. The methods outlined in this study give a basis for detection of less sustainable mountain ecosystems by using as an indicator bare patches of humus or soil where none existed previously. Landsat 5 TM 1 data from 1984 and 1994 and Landsat 7 ETMþ 1 data from the year 2000 were used in the study. The results show that the NDVI significantly separates areas with vegetation cover decrease from areas with no vegetation cover decrease in sensitive, high-latitude mountain ecosystems, such as mountainous dry heath communities.

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Section: Resultsmentioning

confidence: 96%

Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM® and ETM+® imagery

Nordberg

Evertson²

2005

Land Degrad Dev

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“…O índice mais utilizado, o NDVI, por exemplo, é muito sensível ao solo, o que levou pesquisadores a desenvolver IVs alternativos. (SCHMIDT; KARNIELI, 2001) Em função de suas propriedades físicas, a vegetação densa tem baixa reflectância no vermelho e apresenta alta correlação com o IAF -Índice de Área Foliar, que pode ser entendido como sendo a área ocupada pelas folhas de uma vegetação por unidade de área (XUE; SU, 2017). Os diferentes índices de vegetação criados procuram, portanto, satisfazer às necessidades do usuário em dada situação.…”

Section: Fitzunclassified

Classificação de imagens de satélite e índices espectrais de vegetação: uma análise comparativa

Fitz

2020

Geosul

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Estudos relativos à dinâmica territorial sempre ocuparam papéis de destaque no âmbito da Geografia. As alterações espaciais experimentadas em determinadas regiões estão diretamente ligadas aos cenários físicos, políticos, econômicos, culturais e ambientais desses espaços em um dado momento. Diversas ferramentas têm sido utilizadas para analisar o cenário de um determinado ambiente. O presente estudo procurou comparar, a partir de imagens de satélite, a existência de correlação entre diferentes índices de vegetação (IVs) e uma imagem classificada do município de Mundo Novo, no Estado do Mato Grosso do Sul. Para tal, foram utilizados o software livre QGIS e a planilha Excel da Microsoft. Como resultado, verificou-se haver alta correlação entre os distintos IVs e a imagem classificada.

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“…where ρ NIR and ρ red are the reflectance values in the NIR and red bands, respectively. This index has been used in vegetation studies in arid and semi-arid regions [26][27][28] because it reduces the soil background effect. We chose to use it among other soil-adjusted vegetation indexes because it can be used without any preliminary knowledge of the vegetation cover rate [29].…”

Section: Classification Variables and Variable Selectionmentioning

confidence: 99%

Natural Forest Mapping in the Andes (Peru): A Comparison of the Performance of Machine-Learning Algorithms

Luis

Hirata

Santos³

et al. 2018

Remote Sensing

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The Andes mountain forests are sparse relict populations of tree species that grow in association with local native shrubland species. The identification of forest conditions for conservation in areas such as these is based on remote sensing techniques and classification methods. However, the classification of Andes mountain forests is difficult because of noise in the reflectance data within land cover classes. The noise is the result of variations in terrain illumination resulting from complex topography and the mixture of different land cover types occurring at the sub-pixel level. Considering these issues, the selection of an optimum classification method to obtain accurate results is very important to support conservation activities. We carried out comparative non-parametric statistical analyses on the performance of several classifiers produced by three supervised machine-learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN). The SVM and RF methods were not significantly different in their ability to separate Andes mountain forest and shrubland land cover classes, and their best classifiers showed a significantly better classification accuracy (AUC values 0.81 and 0.79 respectively) than the one produced by the kNN method (AUC value 0.75) because the latter was more sensitive to noisy training data.

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Sensitivity of vegetation indices to substrate brightness in hyper-arid environment: The Makhtesh Ramon Crater (Israel) case study

Cited by 32 publications

References 28 publications

Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM® and ETM+® imagery

Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM® and ETM+® imagery

Classificação de imagens de satélite e índices espectrais de vegetação: uma análise comparativa

Natural Forest Mapping in the Andes (Peru): A Comparison of the Performance of Machine-Learning Algorithms

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