Vegetation classification by satellite image processing in a dry area of north-eastern Syria

Hirata, Masahiro; Kogab, Naoki; Shinjo, Hitoshi; Fujita, Haruhiro; Gintzburger, G.; Miyazaki, Akira

doi:10.1080/01431160050505829

Cited by 24 publications

(17 citation statements)

References 9 publications

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“…Possible applications Example studies Sustainable land-use management -Support drought detection -Give advice to farmers regarding good grazing places -Information on the amount of forage, its quality as well as distribution -Understanding of wildlife feeding patterns -Build up management guidelines on the suitability of specific habitats -Quantifying the effect of grazing intensities -Crop forecasting -Locust forecasting and hatching Rosema (1993), Wylie et al (1995), Du Plessis (1999), Qi and Wallace (2002), Dech et al (2003), Kogan et al (2004), Kawamura et al (2005), Mutanga and Rugege (2006) Fire risk assessment -Fire risk assessment for savannah ecosystems -Selection of sites for controlled burning -Development of early warning systems for fire management -Fuel modelling and fire behaviour simulation Pereira et al (1995), Kraus and Samimi (2002), Sannier et al (2002), Mutanga and Rugege (2006), Verbesselt et al (2006) Climate change and degradation -Understand the exchange of energy and CO 2 between vegetation and the atmosphere -Understand the role these regions play in the biochemical cycles -Examine yield and food production -Assess both degradation and ecosystem recovery -Plan protective measures for areas under high risk of desertification -Assist the decision-making process for the declaration of areas experiencing drought exceptional circumstances Pieper (1988), Kennedy (1989), Rosema (1993), Running et al (1995), Pickup (1996), Imeson and Lavee (1998), McVicar andJupp (1998), Shoshany (2000), Diouf and Lambin (2001), Hirata et al (2001), Moleele et al (2001), Collins et al (2009) …”

Section: Areamentioning

confidence: 99%

“…The vegetation cover in semi-arid and arid regions is of high importance for protection against wind and storms. As the level of soil protection provided by, for example, shrubs is directly proportional to their biomass/cover (Calvão and Palmeirim 2004), the quantification of biomass is essential to identify and monitor areas under high risk of degradation/desertification (Hirata et al 2001, Moleele et al 2001 and to assess the degradation status of semi-arid regions (e.g. Liu et al 2003, Baumann 2009).…”

Section: Semi-arid Areas and The Specific Need For Biomass Researchmentioning

confidence: 99%

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

Eisfelder

Kuenzer

Dech

2011

International Journal of Remote Sensing

191

129

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Section: Areamentioning

confidence: 99%

Section: Semi-arid Areas and The Specific Need For Biomass Researchmentioning

confidence: 99%

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

Eisfelder

Kuenzer

Dech

2011

International Journal of Remote Sensing

191

129

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“…For example, using NDVI data, Running et al (1995) present a decision tree classification based on the permanence of above-ground biomass, longevity of leaves, and leaf type. Critically, the use of a vegetation index can yield a classification that is more accurate than one derived from the data used in its calculation (Anderson et al 1993, Nemani et al 1993, Hirata et al 2001. This feature, together with the ready availability of NDVI data in major archives (e.g.…”

Section: Introductionmentioning

confidence: 99%

Land cover classification using multi‐temporal MERIS vegetation indices

Dash

Mathur

Foody

et al. 2007

International Journal of Remote Sensing

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The spectral, spatial, and temporal resolutions of Envisat's Medium Resolution Imaging Spectrometer (MERIS) data are attractive for regional-to global-scale land cover mapping. Moreover, two novel and operational vegetation indices derived from MERIS data have considerable potential as discriminating variables in land cover classification. Here, the potential of these two vegetation indices (the MERIS global vegetation index (MGVI), MERIS terrestrial chlorophyll index (MTCI)) was evaluated for mapping eleven broad land cover classes in Wisconsin. Data acquired in the high and low chlorophyll seasons were used to increase inter-class separability. The two vegetation indices provided a higher degree of inter-class separability than data acquired in many of the individual MERIS spectral wavebands. The most accurate landcover map (73.2%) was derived from a classification of vegetation index-derived data with a support vector machine (SVM), and was 4.4% more accurate than the corresponding map derived from a classification using the data acquired in the original spectral wavebands.

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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%

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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Vegetation classification by satellite image processing in a dry area of north-eastern Syria

Cited by 24 publications

References 9 publications

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

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

Land cover classification using multi‐temporal MERIS vegetation indices

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

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