Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie

Beeri, Ofer; Phillips, R.; Hendrickson, John; Frank, Arlen W.; Kronberg, Scott L.

doi:10.1016/j.rse.2007.02.027

Cited by 109 publications

(103 citation statements)

References 31 publications

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“…As shown in many studies, accurate estimation of green aboveground biomass using remotely sensed data remains a challenge in arid and semiarid grassland due to sparse green vegetation cover and litter [Svoray and Shoshany, 2003;He et al, 2006;Wessels et al, 2006;Beeri et al, 2007]. Based on our field observations, green vegetation cover in our study site was less than 30%, and mean value of litter mass in all sampling plots was 75.8 g m -2 , while mean value of green aboveground biomass was 59. in a large-scale field campaign in the desert steppe of Sonid Zuoqi and Sonid Youqi.…”

Section: Discussionmentioning

confidence: 72%

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Ren

Zhou

2014

European Journal of Remote Sensing

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Accurate estimation of green aboveground biomass in arid and semiarid grassland is essential for a variety of studies, such as sustainable grassland management, fire risk assessment, climate change, and environmental degradation. A great need exists for the establishment of robust method for estimating green aboveground biomass in arid and semiarid grassland due to the influences of soil background and litter. In the study, a new index (litter-soil-adjusted vegetation index, L-SAVI) was proposed to estimate green aboveground biomass in arid and semiarid grassland. The L-SAVI was also evaluated based on biomass and spectra in situ measurements in the desert steppe of Inner Mongolia. Results showed that, the performance of the new index was better than that of NDVI (normalized difference vegetation index), SAVI (soil-adjusted vegetation index), MSAVI (modified soil-adjusted vegetation index), OSAVI (optimised soil-adjusted vegetation index), TSAVI (transformed soil-adjusted vegetation index), ATSAVI (adjusted transformed soil-adjusted vegetation index), PVI (perpendicular vegetation index), GSAVI (green-adjusted vegetation index), and L-ATSAVI (litter-corrected ATSAVI) in our study site. The logic behind the L-SAVI was to enable the SAVI to be less sensitive to litter by incorporating the CAI (cellulose absorption index) in the SAVI. In conclusion, the L-SAVI is a suitable predictor for complementing existing vegetation indices on green aboveground biomass estimation in arid and semiarid grassland.

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Section: Discussionmentioning

confidence: 72%

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Ren

Zhou

2014

European Journal of Remote Sensing

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“…However, in environments where sparse woody vegetation is predominant, remote sensing techniques face specific challenges, and additional methodological research is needed [11][12][13][14][15]. Various studies have shown that the performance is limited when using common broadband Vis, such as NDVI (Normalized Differenced VI) to estimate biomass or wood volume in arid to semi-arid regions using optical data with low resolution (>1 km), e.g., [15][16][17][18][19][20][21], and medium resolution (>5 m-<1 km), e.g., [9,13,[22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

et al. 2016

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Remote sensing-based woody biomass quantification in sparsely-vegetated areas is often limited when using only common broadband vegetation indices as input data for correlation with ground-based measured biomass information. Red edge indices and texture attributes are often suggested as a means to overcome this issue. However, clear recommendations on the suitability of specific proxies to provide accurate biomass information in semi-arid to arid environments are still lacking. This study contributes to the understanding of using multispectral high-resolution satellite data (RapidEye), specifically red edge and texture attributes, to estimate wood volume in semi-arid ecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and Selection Operator) and random forest were used as predictive models relating in situ-measured aboveground standing wood volume to satellite data. Model performance was evaluated based on cross-validation bias, standard deviation and Root Mean Square Error (RMSE) at the logarithmic and non-logarithmic scales. Both models achieved rather limited performances in wood volume prediction. Nonetheless, model performance increased with red edge indices and texture attributes, which shows that they play an important role in semi-arid regions with sparse vegetation.

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“…For instance, Dancy et al (1986) reported an R 2 of 0.72 when deriving ground cover in Botswana rangelands and Beeri et al (2007) reported a prediction error of 18% for total biomass in moderately grazed pastures due to overestimation by NDVI. However, Beck et al (1990) found no correlation between green or total biomass and ground derived NDVI.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

Mundava

Helmholz

Schut

et al. 2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The objective of this paper is to test the relationships between Above Ground Biomass (AGB) and remotely sensed vegetation indices for AGB assessments in the Kimberley area in Western Australia. For 19 different sites, vegetation indices were derived from eight Landsat ETM+ scenes over a period of two years (2011)(2012)(2013). The sites were divided into three groups (Open plains, Bunch grasses and Spinifex) based on similarities in dominant vegetation types. Dry and green biomass fractions were measured at these sites. Single and multiple regression relationships between vegetation indices and green and total AGB were calibrated and validated using a "leave site out" cross validation. Four tests were compared: 1) relationships between AGB and vegetation indices combining all sites; 2) separate relationships per site group; 3) multiple regressions including selected vegetation indices per site group; and 4) as in 3 but including rainfall and elevation data.

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Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie

Cited by 109 publications

References 31 publications

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

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