Differentiation of rangeland vegetation and assessment of its status: field investigations and MODIS and SPOT VEGETATION data analyses

Geerken, Roland; Batikha, N.; Celis, D.; De-Pauw, E

doi:10.1080/01431160500213425

Cited by 30 publications

(8 citation statements)

References 21 publications

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“…The bootstrapping (Table S6 and Sensitive band combinations to predict canopy closure were selected from the red-edge, NIR, and SWIR part of the spectrum. These results are in line with previous studies suggesting optimal combinations for estimation of forest structural attributes in the red-edge region [62,67,106] and in the NIR and SWIR spectral regions [55,58,107]. Our canopy closure estimates are comparable or even higher than results reported elsewhere.…”

Section: Volumesupporting

confidence: 93%

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

et al. 2015

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Spatial information of forest structural variables is crucial for sustainable forest management planning, forest monitoring, and the assessment of forest ecosystem productivity. We investigate a complex alpine forest ecosystem located in the Swiss National Park (SNP) and apply empirical models to retrieve the structural variables canopy closure, basal area, and timber volume at plot scale. We used imaging spectrometer (IS) data from the Airborne Prism EXperiment (APEX) in combination with in-situ measurements of forest structural variables to develop empirical models. These models are based on simple and stepwise multiple regressions, while all potential two narrow-band combinations of the Simple Ratio (SR), the Normalized Difference Vegetation Index (NDVI), the perpendicular vegetation index (PVI), the second soil-adjusted vegetation index (SAVI2), and band depth indices were tested. The accuracy of the estimated structural attributes was evaluated using a leave-one-out cross-validation technique. Using stepwise multiple regression models, we obtained a moderate to good accuracy when estimating canopy closure (R 2 = 0.81, rRMSE = 10%), basal area (R 2 = 0.68, rRMSE = 20%), and timber volume (R 2 = 0.73, rRMSE = 22%). We discuss the reliability of empirical approaches for estimates of canopy structural parameters considering the causality of light interaction and surface information.

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Section: Volumesupporting

confidence: 93%

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

et al. 2015

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“…Because the NDVI can be considered as a linear combination of the SR, both NDVI and SR show similar patterns for grassland AGB. Identified wavelength regions are in agreement with previous studies suggesting optimal combinations in the red-edge region [30], [54], [77], the NIR, and the SWIR spectral region [2], [22], [78].…”

Section: B Empirical Model To Derive Grassland Agbsupporting

confidence: 89%

“…Such a band combination has previously been reported to be applicable for estimating photosynthetically active and nonactive vegetation, as well as AGB [88]. Other authors reported similar results when estimating grassland AGB [2], [78], [84] or grassland LAI [22], [59], [89]. Ustin et al [88], e.g., suggest that an accurate estimation of grassland and forest AGB requires the full spectrum including the SWIR.…”

Section: A Biomass Estimation Using Empirical Approaches and Is Datasupporting

confidence: 51%

Mapping Alpine Aboveground Biomass From Imaging Spectrometer Data: A Comparison of Two Approaches

Fatehi

Damm

Schweiger

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Aboveground biomass (AGB) of terrestrial ecosystems is an important constraint of global change and productivity models and used to assess carbon stocks and thus the contribution of vegetated ecosystems to the global carbon cycle. Although an indispensable and important requirement for decision makers, coherent and accurate estimates of grassland and forest AGB especially in complex environments are still lacking. In this study, we aim to assess the capability of two strategies to map grassland and forest AGB in a complex alpine ecosystem, i.e., using a discrete as well as a continuous field (CF) mapping approach based on imaging spectroscopy (IS) data. In situ measurements of grassland and forest AGB were acquired in the Swiss National Park (SNP) to calibrate empirical models and to validate AGB retrievals. The selection of robust empirical models considered all potential two narrow-band combinations of the simple ratio (SR) and the normalized difference vegetation index (NDVI) generated from Airborne Prism Experiment (APEX) IS data and in situ measurements. We found a narrow-band SR including spectral bands from the short-wave infrared (SWIR) (1689 nm) and near infrared (NIR) (851 nm) as the best regression model to estimate grassland AGB. Forest AGB showed highest correlation with an SR generated from two spectral bands in the SWIR (1498, 2112 nm). The applied accuracy assessment revealed good results for estimated grassland AGB using the discrete mapping approach [R 2 of 0.65, mean RMSE (mRMSE) of 0.91 t · ha −1 , and mean relative RMSE (mrRMSE) of 26%]. The CF mapping approach produced a higher R 2 ( R 2 = 0.94 ), and decreased the mRMSE and the mrRMSE to 0.55 t · ha −1 and 15%, respectively. For forest, the discrete approach predicted AGB with an R 2 value of 0.64, an mRMSE of 67.8 t · ha −1 , and an mrRMSE of 25%. The CF mapping approach improved the accuracy of forest AGB estimation with R 2 = 0.85 , mean RMSE = 55.85 t · ha −1 , and mean relative RMSE = 21%. Our results indicate that, in general, both mapping approaches are capable of accurately mapping grassland and forest AGB in complex environments using IS data, whereas the CF-based approach yielded higher accuracies due to its capability to incorporate subpixel information (abundances) of different land cover types.

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“…Studies from other regions of the world, however, successfully examined the relationship between the precipitation and productivity inferred from remote sensing data (e.g. Evans and Geerken, 2004;Geerken et al, 2005;Potter and Brooks, 1998;Prince et al, 2007), and analyses of variability may benefit from such an approach (e.g. Jobbagy et al, 2002).…”

Section: Discussionmentioning

confidence: 98%