Scaling dimensions in spectroscopy of soil and vegetation

Malenovský, Zbyněk; Bartholomeus, Harm; Júnior, Fausto Weimar Acerbi; Schopfer, Jürg; Painter, T. H.; Epema, G.F.; Bregt, A.K.

doi:10.1016/j.jag.2006.08.003

Cited by 42 publications

(26 citation statements)

References 133 publications

(147 reference statements)

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“…This can be explained because the FieldSpec data relate to reflectance measurements at the branch level, whereas reflectances derived from WorldView2 data relate to the canopy level. Clumping effects and internal shadowing inside the tree canopy cause lower NDVI values at the canopy level [50,51]. In spite of this, NDVI was linearly correlated to GCF showing an R 2 of 0.7 and a RMSE of 0.12.…”

Section: Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 97%

Assessing Water Stress of Desert Tamarugo Trees Using in situ Data and Very High Spatial Resolution Remote Sensing

et al. 2013

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Abstract:The hyper-arid Atacama Desert is one of the most extreme environments for life and only few species have evolved to survive its aridness. One such species is the tree Prosopis tamarugo Phil. Because Tamarugo completely depends on groundwater, it is being threatened by the high water demand from the Chilean mining industry and the human consumption. In this paper, we identified the most important biophysical variables to assess the water status of Tamarugo trees and tested the potential of WorldView2 satellite images to retrieve these variables. We propose green canopy fraction (GCF) and green drip line leaf area index (DLLAI green ) as best variables and a value of 0.25 GCF as a critical threshold for Tamarugo survival. Using the WorldView2 spectral bands and an object-based image analysis, we showed that the NDVI and the Red-edge Chlorophyll Index (CI Red-edge ) have good potential to retrieve GCF and DLLAI green . The NDVI performed best for DLLAI green (RMSE = 0.4) while the CI Red-edge was best for GCF (RMSE = 0.1). However, both indices were affected by Tamarugo leaf movements (leaves avoid facing direct solar radiation at the hottest time of the day). Thus, monitoring systems OPEN ACCESSRemote Sens. 2013, 5 5065 based on these indices should consider the time of the day and the season of the year at which the satellite images are acquired.

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Section: Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 97%

Assessing Water Stress of Desert Tamarugo Trees Using in situ Data and Very High Spatial Resolution Remote Sensing

et al. 2013

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“…While Malenovsky et al (2007) discuss the topic in more detail, several approaches to bridge various scaling levels exist. Classical gaps can be identified in photonvegetation interaction, when scaling from leaf to canopy level and coupled (or linked) radiative transfer models (RTM) are used (c.f., Jacquemoud et al, 2009-in this issue).…”

Section: Bridging Scaling Gaps From Molecules To Ecosystems and Biomesmentioning

confidence: 99%

Earth system science related imaging spectroscopy—An assessment

Schaepman

Ustin

Plaza

et al. 2009

Remote Sensing of Environment

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308

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“…As shown in the land cover map (Figure 2), the land covers on the Zoige Plateau are characterized by high spatial heterogeneity, especially in the transition zones between wetlands and grasslands. Although some researches demonstrated that the linearity assumption only leads to minor inaccuracies when NDVI is used instead of reflectance [28,63], because of the influence of adjacent targets [28], the mechanism of NDVI mixing is very complex and the linear unmixing assumption is probably not suitable for areas with a complex underlying surface [64,65]. Moreover, the error of the land cover map (about 10 percent) and image registration between data sources (different spatial resolution match between MODIS NDVI and TM-derived land cover map) can also introduce uncertainty into unmixing and subsequent LUE inversion.…”

Section: Discussionmentioning

confidence: 99%

Estimating the Maximal Light Use Efficiency for Different Vegetation through the CASA Model Combined with Time-Series Remote Sensing Data and Ground Measurements

Bian

Lei

et al. 2012

Remote Sensing

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Maximal light use efficiency (LUE) is an important ecological index of a vegetation essential attribute, and a key parameter of the LUE-based model for estimating large-scale vegetation productivity by remote sensing technology. However, although currently used in different models there still exists extensive controversy. This paper takes the Zoige Plateau in China as a case area to develop a new approach for estimating the maximal LUEs for different vegetation. Based on an existing land cover map and MODIS NDVI product, the linear unmixing method with a moving window was adopted to estimate the time-series NDVI for different end members in a MODIS NDVI pixel; then Particle Swarm Optimizer (PSO) was applied to search for the optimization of LUE retrievals through the CASA (Carnegie-Ames-Stanford Approach) model combined with time-series NDVI and ground measurements. The derived maximal LUEs present significant differences among various vegetation types. These are 0.

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Scaling dimensions in spectroscopy of soil and vegetation

Cited by 42 publications

References 133 publications

Assessing Water Stress of Desert Tamarugo Trees Using in situ Data and Very High Spatial Resolution Remote Sensing

Assessing Water Stress of Desert Tamarugo Trees Using in situ Data and Very High Spatial Resolution Remote Sensing

Earth system science related imaging spectroscopy—An assessment

Estimating the Maximal Light Use Efficiency for Different Vegetation through the CASA Model Combined with Time-Series Remote Sensing Data and Ground Measurements

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