Performance evaluation of spectral vegetation indices using a statistical sensitivity function

Ji, Lei; Peters, Albert J.

doi:10.1016/j.rse.2006.07.010

Cited by 122 publications

(56 citation statements)

References 22 publications

(25 reference statements)

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“…In doing so the aim is the identification of an analytical relationship between on-the-ground AGB estimates and spectral indices sensitive to photosynthetically active radiation and scaling with amount of biomass [39][40][41][42]. A vegetation index is generally derived from the spectral reflectance of two or more bands, and proportional to the value of biophysical parameters like leaf area index (LAI), net primary productivity (NPP), and absorbed photosynthetically active radiation (APAR) [43]. The choice of adequately performing indices (VIs) and satellite data type depends on the scale of analysis, type of ecosystem and environmental conditions, vegetation density, spectral information available, and the nature of the field information available.…”

Section: Introductionmentioning

confidence: 99%

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Clerici

Rubiano

Abd-Elrahman

et al. 2016

Forests

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Periurban forests are key to offsetting anthropogenic carbon emissions, but they are under constant threat from urbanization. In particular, secondary Neotropical forest types in Andean periurban areas have a high potential to store carbon, but are currently poorly characterized. To address this lack of information, we developed a method to estimate periurban aboveground biomass (AGB)-a proxy for multiple ecosystem services-of secondary Andean forests near Bogotá, Colombia, based on very high resolution (VHR) GeoEye-1, Pleiades-1A imagery and field-measured plot data. Specifically, we tested a series of different pre-processing workflows to derive six vegetation indices that were regressed against in situ estimates of AGB. Overall, the coupling of linear models and the Ratio Vegetation Index produced the most satisfactory results. Atmospheric and topographic correction proved to be key in improving model fit, especially in high aerosol and rugged terrain such as the Andes. Methods and findings provide baseline AGB and carbon stock information for little studied periurban Andean secondary forests. The methodological approach can also be used for integrating limited forest monitoring plot AGB data with very high resolution imagery for cost-effective modelling of ecosystem service provision from forests, monitoring reforestation and forest cover change, and for carbon offset assessments.

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Section: Introductionmentioning

confidence: 99%

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Clerici

Rubiano

Abd-Elrahman

et al. 2016

Forests

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“…RVI, the first ratio-based vegetation index described by Jordan (1969), is a very similar index to NDVI but with higher sensitivity to dense forests. RVI has been found to have the highest sensitivity when LAI is greater than 1.8, while NDVI has the highest sensitivity when LAI is less than 1.8 (Ji and Peters 2007). Therefore, RVI may better differentiate levels of damage to wind-disturbed forests, as opposed to timber harvesting or fire disturbed forests.…”

Section: Introductionmentioning

confidence: 99%

Comparison of remote sensing change detection techniques for assessing hurricane damage to forests

Wang

2009

Environ Monit Assess

101

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This study compared performance of four change detection algorithms with six vegetation indices derived from pre- and post-Katrina Landsat Thematic Mapper (TM) imagery and a composite of the TM bands 4, 5, and 3 in order to select an optimal remote sensing technique for identifying forestlands disturbed by Hurricane Katrina. The algorithms included univariate image differencing (UID), selective principal component analysis (PCA), change vector analysis (CVA), and postclassification comparison (PCC). The indices consisted of near-infrared to red ratios, normalized difference vegetation index, Tasseled Cap index of greenness, brightness, and wetness (TCW), and soil-adjusted vegetation index. In addition to the satellite imagery, the "ground truth" data of forest damage were also collected through field investigation and interpretation of post-Katrina aerial photos. Disturbed forests were identified by classifying the composite and the continuous change imagery with the supervised classification method. Results showed that the change detection techniques exerted apparent influence on detection results with an overall accuracy varying between 51% and 86% and a kappa statistics ranging from 0.02 to 0.72. Detected areas of disturbed forestlands were noticeable in two groups: 180,832-264,617 and 85,861-124,205 ha. The landscape of disturbed forests also displayed two unique patterns, depending upon the area group. The PCC algorithm along with the composite image contributed the highest accuracy and lowest error (0.5%) in estimating areas of disturbed forestlands. Both UID and CVA performed similarly, but caution should be taken when using selective PCA in detecting hurricane disturbance to forests. Among the six indices, TCW outperformed the other indices owing to its maximum sensitivity to forest modification. This study suggested that compared with the detection algorithms, proper selection of vegetation indices was more critical for obtaining satisfactory results.

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“…However, there were irregular high and low EVI values between the three vegetation. The variability in the EVI values of the three vegetation (high and low values), especially during defoliation stage of rubber trees suggests that as compared to EVI, NDVI is more sensitive to low-density canopy and EVI is more sensitive to high-density canopy (Ji & Peters 2007). The performance of EVI metrics used in this study was slightly increased with the increased in the amount of foliage cover starting from rubber trees' foliation period to their growing period.…”

Section: Computed Vegetation Indices Of Rubber Evergreen and Oil Palmentioning

confidence: 68%

Mapping rubber trees based on phenological analysis of Landsat time series data-sets

Razak

Shariff

Ahmad

et al. 2017

Geocarto International

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This study proposes a strategy for accurate mapping of rubber trees through the analysis of Landsat time series datasets. The phenological dynamics of rubber trees were derived from the Normalized Difference Vegetation Index (NDVI) to verify the three important phenological metrics of rubber trees; defoliation, foliation and their growing stages. A decade (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) ago, Landsat time series NDVIs were used to study the strength of relationship between rubber trees, evergreen trees and oil palm trees. Two important results that could discriminate these three types of vegetation were found; firstly, a weak relationship of NDVIs between rubber trees and evergreen trees during the defoliation period (r 2 = 0.1358) and secondly between rubber trees and oil palm trees during the growing period (r 2 = 0.2029). This analysis was verified using Support Vector Machine to map the distribution of the three types of vegetation. An accurate mapping strategy of rubber trees was successfully formulated.

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Performance evaluation of spectral vegetation indices using a statistical sensitivity function

Cited by 122 publications

References 22 publications

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Comparison of remote sensing change detection techniques for assessing hurricane damage to forests

Mapping rubber trees based on phenological analysis of Landsat time series data-sets

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