Derivation of biomass information for semi-arid areas using remote-sensing data

Eisfelder, Christina; Kuenzer, Claudia; Dech, Stefan

doi:10.1080/01431161.2011.620034

Cited by 191 publications

(151 citation statements)

References 120 publications

(241 reference statements)

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“…Green aboveground biomass is also a key ecological variable in arid and semiarid grassland [Eisfelder et al, 2012], and influences important environmental processes, such as soil erosion, environmental degradation, and desertification [Verstraete, 1986;Eswaran et al, 2001;Hirata et al, 2001;Moleele et al, 2001;Mulligan, 2009]. Therefore, a great need exists for the establishment of robust and transferable methods for green aboveground biomass estimation in arid and semiarid grassland.…”

Section: Introductionmentioning

confidence: 99%

“…Grassland is one of the most widespread ecosystem types, and accurate estimation of grassland green aboveground biomass is increasingly needed to reduce uncertainty of this terrestrial carbon sink [Scurlock and Hall, 1998;Scurlock et al, 2002], especially in arid and semiarid areas [Cui et al, 2011;Fang et al, 2010;Eisfelder et al, 2012]. Green aboveground biomass is also a key ecological variable in arid and semiarid grassland [Eisfelder et al, 2012], and influences important environmental processes, such as soil erosion, environmental degradation, and desertification [Verstraete, 1986;Eswaran et al, 2001;Hirata et al, 2001;Moleele et al, 2001;Mulligan, 2009].…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…This map, together with the estimated livestock number by administrative region, can be used to calculate a forage balance identifying areas potentially exposed to forage deficit or surplus, leading to potential livestock mortality or fire risk, respectively. Different remote sensing (RS) based approaches to estimate aboveground biomass in semi-arid areas have been developed during the last decades using optical and radar data as well as modelling and combined multi-sensor approaches (for a review see Eisfelder et al, 2012). The majority of studies utilized lowand medium resolution optical or radar data and an empirical relationship between field biomass measurements and a RS indicator (Eisfelder et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Different remote sensing (RS) based approaches to estimate aboveground biomass in semi-arid areas have been developed during the last decades using optical and radar data as well as modelling and combined multi-sensor approaches (for a review see Eisfelder et al, 2012). The majority of studies utilized lowand medium resolution optical or radar data and an empirical relationship between field biomass measurements and a RS indicator (Eisfelder et al, 2012). The first studies of herbaceous biomass estimation in Niger date back to the late 1980's and utilized linear regressions between maximum standing biomass and NDVI-based metrics (maximum and time-integrated NDVI) derived from NOAA AVHRR imagery (Maidagi et al, 1987;Wylie et al, 1988Wylie et al, , 1991Wylie et al, , 1995.…”

Section: Introductionmentioning

confidence: 99%

Biomass estimation to support pasture management in Niger

Schucknecht

Meroni

Francois

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Livestock plays a central economic role in Niger, but it is highly vulnerable due to the high inter-annual variability of rain and hence pasture production. This study aims to develop an approach for mapping pasture biomass production to support activities of the Niger Ministry of Livestock for effective pasture management. Our approach utilises the observed spatiotemporal variability of biomass production to build a predictive model based on ground and remote sensing data for the period 1998-2012. Measured biomass (63 sites) at the end of the growing season was used for the model parameterisation. The seasonal cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR), calculated from 10-day image composites of SPOT-VEGETATION FAPAR, was computed as a phenology-tuned proxy of biomass production. A linear regression model was tested aggregating field data at different levels (global, department, agro-ecological zone, and intersection of agro-ecological and department units) and subjected to a cross validation (cv) by leaving one full year out. An increased complexity (i.e. spatial detail) of the model increased the estimation performances indicating the potential relevance of additional and spatially heterogeneous agro-ecological characteristics for the relationship between herbaceous biomass at the end of the season and CFAPAR. The model using the department aggregation yielded the best trade-off between model complexity and predictive power (R 2 = 0.55, R 2 cv = 0.48). The proposed approach can be used to timely produce maps of estimated biomass at the end of the growing season before ground point measurements are made available.

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“…Vegetation observed in remote-sensing data is usually described in terms of derived variables such as vegetation indices (normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), enhanced vegetation index (EVI), etc.) (Tucker 1979;Tarpley 1991;Jackson and Huete 1991;Baret and Guyot 1991;Gupta 1993;Huete et al 1997), leaf area index (LAI) (Gupta, Prasad, and Vijayan 2000;Fensholt, Sandholt, and Rasmussen 2004;Casa and Jones 2005), tree cover density (Bai et al 2005;Yang, Weisberg, and Bristow 2012;Leinenkugel et al 2014, forthcoming), net primary productivity (NPP) and biomass (gC m −2 ) (Wagner et al 2003;Hese et al 2005;Lu 2006;Eisfelder, Kuenzer, and Dech 2011), canopy moisture (Brakke et al 1981), canopy height, expected crop yield (Birnie, Robertson, and Stove 1982;Hatfield 1983;Horie, Yajima, and Nakagawa 1992), measures of fragmentation and connectivity (Stenhouse 2004;Pueyo and Alados 2007;Briant, Gond, and Laurance 2010), or as detailed classification-derived map products breaking down vegetation distribution to the species level (Foody and Cutler 2006;Kutser and Jupp 2006;Pu and Landry 2012;Engler et al 2013). Vegetation height can also be derived from digital elevation model (DEM) data (Walker et al 2007).…”

Section: Spaceborne Remote Sensing Of Vegetation Biodiversitymentioning

confidence: 99%

Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks

Kuenzer

Ottinger²,

Wegmann³

et al. 2014

International Journal of Remote Sensing

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154

112

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Many biologists, ecologists, and conservationists are interested in the possibilities that remote sensing offers for their daily work and study site analyses as well as for the assessment of biodiversity. However, due to differing technical backgrounds and languages, cross-sectorial communication between this group and remote-sensing scientists is often hampered. Hardly any really comprehensive studies exist that are directed towards the conservation community and provide a solid overview of available Earth observation sensors and their different characteristics. This article presents, categorizes, and discusses what spaceborne remote sensing has contributed to the study of animal and vegetation biodiversity, which different types of variables of value for the biodiversity community can be derived from remote-sensing data, and which types of spaceborne sensor data are available for which time spans, and at which spatial and temporal resolution. We categorize all current and important past sensors with respect to application fields relevant for biologists, ecologists, and conservationists. Furthermore, sensor gaps and current challenges for Earth observation with respect to data access and provision are presented.

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Derivation of biomass information for semi-arid areas using remote-sensing data

Cited by 191 publications

References 120 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

Biomass estimation to support pasture management in Niger

Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks

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