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2019 | OriginalPaper | Buchkapitel

13. Remote Sensing-Based Forest Biomass Assessment in Northwest Himalayan Landscape

verfasst von : Subrata Nandy, Surajit Ghosh, S. P. S. Kushwaha, A. Senthil Kumar

Erschienen in: Remote Sensing of Northwest Himalayan Ecosystems

Verlag: Springer Singapore

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Abstract

Forests cover around one-third of the global land cover (4.03 billion hectares) (FAO 2010; Pan et al. 2013) and are among the richest ecosystems in terms of biological and genetic diversity (Köhl et al. 2015). Forests are considered as reservoirs of carbon, and it is stored as biomass (phytomass). The total amount of above- and belowground organic matter of both living and dead plant parts is called biomass (FAO 2005). Net primary productivity (NPP) is majorly accumulated as biomass. Around two-thirds (262.1 PgC) of the global terrestrial biomass is stored by the tropical forests (Pan et al. 2013; Negrón-Juárez et al. 2015). Therefore, forests act as one of the keystones of the global carbon cycle and play a vital role in designing the mitigating strategies for climate change and reducing the emission of greenhouse gases. Hence, forest biomass estimation is useful in quantifying the carbon stock, carbon emissions due to forest degradation and disturbances, carbon budget, productivity, forest planning and management and policy-making (Caputo 2009). Biomass monitoring in regular interval is utmost necessary for understanding the nature (source/sink) of the forest (Kushwaha et al. 2014). In addition, forests are vital sources of livelihood and economic development of any country (Köhl et al. 2015). Forest ecosystems offer numerous goods (timber, fodder, food, etc.) and ecological services (MEA 2005).

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Literatur
Zurück zum Zitat Ahongshangbam J, Patel NR, Kushwaha SPS, Watham T, Dadhwal VK (2016) Estimating Gross Primary Production of a Forest Plantation Area Using Eddy Covariance Data and Satellite Imagery. J Ind Soc Remote Sens 44(6): 895–904.CrossRef Ahongshangbam J, Patel NR, Kushwaha SPS, Watham T, Dadhwal VK (2016) Estimating Gross Primary Production of a Forest Plantation Area Using Eddy Covariance Data and Satellite Imagery. J Ind Soc Remote Sens 44(6): 895–904.CrossRef
Zurück zum Zitat Anaya JA, Chuvieco E, Palacios-Orueta A (2009) Aboveground biomass assessment in Colombia: A remote sensing approach. For Ecol Manag 257:1237–1246CrossRef Anaya JA, Chuvieco E, Palacios-Orueta A (2009) Aboveground biomass assessment in Colombia: A remote sensing approach. For Ecol Manag 257:1237–1246CrossRef
Zurück zum Zitat Attarchi S, Gloaguen R (2014) Improving the estimation of above ground biomass using dual polarimetric PALSAR and ETM+ data in the Hyrcanian mountain forest (Iran). Rem Sens 6(5):3693–3715CrossRef Attarchi S, Gloaguen R (2014) Improving the estimation of above ground biomass using dual polarimetric PALSAR and ETM+ data in the Hyrcanian mountain forest (Iran). Rem Sens 6(5):3693–3715CrossRef
Zurück zum Zitat Awasthi A, Uniyal SK, Rawat GS, Rajvanshi A (2003) Forest resource availability and its use by the migratory villages of Uttarkashi, Garhwal Himalaya (India). For Ecol Manag 174: 13–24CrossRef Awasthi A, Uniyal SK, Rawat GS, Rajvanshi A (2003) Forest resource availability and its use by the migratory villages of Uttarkashi, Garhwal Himalaya (India). For Ecol Manag 174: 13–24CrossRef
Zurück zum Zitat Baral S (2011) Mapping Carbon Stock using High Resolution Satellite Images in Sub-tropical Forest of Nepal, Dessertation, Faculty of Geo-Information and Earth Observation (ITC), University of Twente, Enschede, The Netherlands Baral S (2011) Mapping Carbon Stock using High Resolution Satellite Images in Sub-tropical Forest of Nepal, Dessertation, Faculty of Geo-Information and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
Zurück zum Zitat Baret F, Guyot G (1991) Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens Environ 35(2–3):161–173CrossRef Baret F, Guyot G (1991) Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens Environ 35(2–3):161–173CrossRef
Zurück zum Zitat Birth GS, McVey GR (1968) Measuring the color of growing turf with a reflectance spectrophotometer. Agron J 60(6):640–643CrossRef Birth GS, McVey GR (1968) Measuring the color of growing turf with a reflectance spectrophotometer. Agron J 60(6):640–643CrossRef
Zurück zum Zitat Bonan GB, Levis S, Sitch S, Vertenstein M, Oleson KW (2003) A dynamic global vegetation model for use with climate models: concepts and description of simulated vegetation dynamics. Glob Change Biol 9(11):1543–1566CrossRef Bonan GB, Levis S, Sitch S, Vertenstein M, Oleson KW (2003) A dynamic global vegetation model for use with climate models: concepts and description of simulated vegetation dynamics. Glob Change Biol 9(11):1543–1566CrossRef
Zurück zum Zitat Boschetti M, Bocchi S, Brivio PA (2007) Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information. Agric Ecosyst Environ 118:267–272CrossRef Boschetti M, Bocchi S, Brivio PA (2007) Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information. Agric Ecosyst Environ 118:267–272CrossRef
Zurück zum Zitat Broge NH, Leblanc E (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ 76(2):156–172CrossRef Broge NH, Leblanc E (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ 76(2):156–172CrossRef
Zurück zum Zitat Caputo J (2009) Sustainable forest biomass: promoting renewable energy and forest stewardship. Policy paper, Environmental and Energy Study Institute Caputo J (2009) Sustainable forest biomass: promoting renewable energy and forest stewardship. Policy paper, Environmental and Energy Study Institute
Zurück zum Zitat Carr JR, Myers DE, Glass CE (1985) Cokriging—a computer program. Comput Geosci 11(2): 111–127CrossRef Carr JR, Myers DE, Glass CE (1985) Cokriging—a computer program. Comput Geosci 11(2): 111–127CrossRef
Zurück zum Zitat Casady G, van Leeuwen W, Reed B (2013) Estimating winter annual biomass in the Sonoran and Mojave deserts with satellite- and ground-based observations. Remote Sens 5:909–926CrossRef Casady G, van Leeuwen W, Reed B (2013) Estimating winter annual biomass in the Sonoran and Mojave deserts with satellite- and ground-based observations. Remote Sens 5:909–926CrossRef
Zurück zum Zitat Ceccato P, Flasse S, Gregoire JM (2002) Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications. Remote Sens Environ 82(2):198–207CrossRef Ceccato P, Flasse S, Gregoire JM (2002) Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications. Remote Sens Environ 82(2):198–207CrossRef
Zurück zum Zitat Chacko VJ (1965) A manual on sampling techniques for forest surveys. New Delhi: Manager of Publications, Government of India Chacko VJ (1965) A manual on sampling techniques for forest surveys. New Delhi: Manager of Publications, Government of India
Zurück zum Zitat Chave J, Condit R, Aguilar S, Hernandez A, Lao S, Perez R. 2004. Error Propagation and Scaling for Tropical Forest Biomass Estimates. Philos Trans Royal Soc B: Biol Sci 359: 409–420CrossRef Chave J, Condit R, Aguilar S, Hernandez A, Lao S, Perez R. 2004. Error Propagation and Scaling for Tropical Forest Biomass Estimates. Philos Trans Royal Soc B: Biol Sci 359: 409–420CrossRef
Zurück zum Zitat Chirici G, Barbati A, Corona P, Marchetti M, Travaglini D, Maselli F, Bertini, R. 2008. Non-parametric and parametric methods using satellite images for estimating growing stock volume in Alpine and Mediterranean forest ecosystems. Remote Sens Environ 112(5):2686–2700CrossRef Chirici G, Barbati A, Corona P, Marchetti M, Travaglini D, Maselli F, Bertini, R. 2008. Non-parametric and parametric methods using satellite images for estimating growing stock volume in Alpine and Mediterranean forest ecosystems. Remote Sens Environ 112(5):2686–2700CrossRef
Zurück zum Zitat Chirici G, Corona P, Marchetti M, Mastronardi A, Maselli F, Bottai L, Travaglini D (2012) K-NN FOREST: a software for the non-parametric prediction and mapping of environmental variables by the k nearest neighbors algorithm. Eur J Remote Sens 45:433–442CrossRef Chirici G, Corona P, Marchetti M, Mastronardi A, Maselli F, Bottai L, Travaglini D (2012) K-NN FOREST: a software for the non-parametric prediction and mapping of environmental variables by the k nearest neighbors algorithm. Eur J Remote Sens 45:433–442CrossRef
Zurück zum Zitat Cochran, W. G. 1963. Sampling techniques. John Wiley and Sons Inc, New York Cochran, W. G. 1963. Sampling techniques. John Wiley and Sons Inc, New York
Zurück zum Zitat Cramer W, Bondeau A, Woodward FI, Prentice IC, Betts RA, Brovkin V, Cox PM, Fisher V, Foley JA, Friend AD, Kucharik C (2001) Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biol 7(4):357–373CrossRef Cramer W, Bondeau A, Woodward FI, Prentice IC, Betts RA, Brovkin V, Cox PM, Fisher V, Foley JA, Friend AD, Kucharik C (2001) Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biol 7(4):357–373CrossRef
Zurück zum Zitat Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signal Syst 2(4): 303–314CrossRef Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signal Syst 2(4): 303–314CrossRef
Zurück zum Zitat Datt B (1999) Remote sensing of water content in Eucalyptus leaves. Aust J Bot 47(6): 909–923CrossRef Datt B (1999) Remote sensing of water content in Eucalyptus leaves. Aust J Bot 47(6): 909–923CrossRef
Zurück zum Zitat Daughtry CST, Walthall CL, Kim MS, De Colstoun EB, McMurtrey JE (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ 74(2): 229–239CrossRef Daughtry CST, Walthall CL, Kim MS, De Colstoun EB, McMurtrey JE (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ 74(2): 229–239CrossRef
Zurück zum Zitat Deng Y, Chen X, Chuvieco E, Warner T, Wilson JP (2007) Multi-scale linkages between topographic attributes and vegetation indices in a mountainous landscape. Remote Sens Environ 111:122–134CrossRef Deng Y, Chen X, Chuvieco E, Warner T, Wilson JP (2007) Multi-scale linkages between topographic attributes and vegetation indices in a mountainous landscape. Remote Sens Environ 111:122–134CrossRef
Zurück zum Zitat Dhanda P, Nandy S, Kushwaha SPS., Ghosh S, Murthy YVNK, Dadhwal VK (2017) Optimizing spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms. Prog Phys Geog 41(3): 247–267CrossRef Dhanda P, Nandy S, Kushwaha SPS., Ghosh S, Murthy YVNK, Dadhwal VK (2017) Optimizing spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms. Prog Phys Geog 41(3): 247–267CrossRef
Zurück zum Zitat Dobson MC, Ulaby FT, LeToan T, Beaudoin A, Kasischke ES, Christensen N (1992) Dependence of radar backscatter on coniferous forest biomass. IEEE Trans Geosci Remote Sens 30(2), 412–415CrossRef Dobson MC, Ulaby FT, LeToan T, Beaudoin A, Kasischke ES, Christensen N (1992) Dependence of radar backscatter on coniferous forest biomass. IEEE Trans Geosci Remote Sens 30(2), 412–415CrossRef
Zurück zum Zitat Dubayah R, Rich PM (1995) Topographic solar radiation models for GIS. Int J Geogr Inf Syst 9(4):405–419CrossRef Dubayah R, Rich PM (1995) Topographic solar radiation models for GIS. Int J Geogr Inf Syst 9(4):405–419CrossRef
Zurück zum Zitat Dwyer PC (2011) A spatial estimation of herbaceous biomass using remote sensing in southern African savannas. M. Sc. thesis, University of the Witwatersrand, Johannesburg Dwyer PC (2011) A spatial estimation of herbaceous biomass using remote sensing in southern African savannas. M. Sc. thesis, University of the Witwatersrand, Johannesburg
Zurück zum Zitat Eldeiry A, Garcia LA (2009) Comparison of regression kriging and cokriging techniques to estimate soil salinity using Landsat images. Civil and Environmental Engineering Department, Colorado State University, Fort Collins, CO 80523–1372, Hydrology Day, pp. 27–38 Eldeiry A, Garcia LA (2009) Comparison of regression kriging and cokriging techniques to estimate soil salinity using Landsat images. Civil and Environmental Engineering Department, Colorado State University, Fort Collins, CO 80523–1372, Hydrology Day, pp. 27–38
Zurück zum Zitat FAO (2005) Global Forest Resources Assessment Update 2005, Terms and Definitions (Final Version) (p. 33). Rome: Forest Resources Assessment Program, Working Paper 83, Forest Resources Development Service, Forest Resources Division, FAO FAO (2005) Global Forest Resources Assessment Update 2005, Terms and Definitions (Final Version) (p. 33). Rome: Forest Resources Assessment Program, Working Paper 83, Forest Resources Development Service, Forest Resources Division, FAO
Zurück zum Zitat FAO (2010) Global forest resources assessment 2010. Rome, Italy FAO (2010) Global forest resources assessment 2010. Rome, Italy
Zurück zum Zitat Folkesson K, Smith-Jonforsen G, Ulander LM (2009) Model-based compensation of topographic effects for improved stem-volume retrieval from CARABAS-II VHF-band SAR images. IEEE Trans Geosci Rem Sens 47:1045–1055CrossRef Folkesson K, Smith-Jonforsen G, Ulander LM (2009) Model-based compensation of topographic effects for improved stem-volume retrieval from CARABAS-II VHF-band SAR images. IEEE Trans Geosci Rem Sens 47:1045–1055CrossRef
Zurück zum Zitat Foody GM, Cutler ME, Mcmorrow J, Pelz D, Tangki H, Boyd DS, Douglas I (2001) Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecol Biogeogr 10(4):379–386CrossRef Foody GM, Cutler ME, Mcmorrow J, Pelz D, Tangki H, Boyd DS, Douglas I (2001) Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecol Biogeogr 10(4):379–386CrossRef
Zurück zum Zitat Franco-Lopez H, Ek AR, Bauer ME (2001) Estimating and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sens Environ 77:251–274CrossRef Franco-Lopez H, Ek AR, Bauer ME (2001) Estimating and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sens Environ 77:251–274CrossRef
Zurück zum Zitat FRI (2002) Indian woods: their identification, properties and uses, (Revised edition). Dehradun: Forest Research Institute, Indian Council of Forestry Research and Education, Ministry of Environment and Forests, Government of India, I-VI FRI (2002) Indian woods: their identification, properties and uses, (Revised edition). Dehradun: Forest Research Institute, Indian Council of Forestry Research and Education, Ministry of Environment and Forests, Government of India, I-VI
Zurück zum Zitat FSI (1996) Volume equations for forests of India, Nepal and Bhutan. Dehradun: Forest Survey of India, Ministry of Environment and Forests, Government of India FSI (1996) Volume equations for forests of India, Nepal and Bhutan. Dehradun: Forest Survey of India, Ministry of Environment and Forests, Government of India
Zurück zum Zitat FSI (2015) India State of Forest Report: Forest Survey of India, Ministry of Environment, Forest and Climate Change, Government of India FSI (2015) India State of Forest Report: Forest Survey of India, Ministry of Environment, Forest and Climate Change, Government of India
Zurück zum Zitat Gasparri NI, Parmuchi MG, Bono J, Karszenbaum H, Montenegro CL (2010) Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina. J Arid Environ 74:1262–1270CrossRef Gasparri NI, Parmuchi MG, Bono J, Karszenbaum H, Montenegro CL (2010) Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina. J Arid Environ 74:1262–1270CrossRef
Zurück zum Zitat Gitelson A, Merzlyak MN (1994) Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J Plant Physiol 143(3):286–292CrossRef Gitelson A, Merzlyak MN (1994) Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J Plant Physiol 143(3):286–292CrossRef
Zurück zum Zitat Gitelson AA (2004) Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J Plant Physiol 161(2):165–173CrossRef Gitelson AA (2004) Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J Plant Physiol 161(2):165–173CrossRef
Zurück zum Zitat Gitelson AA, Kaufman YJ, Stark R, Rundquist D (2002) Novel algorithms for remote estimation of vegetation fraction. Remote Sens Environ 80(1):76–87CrossRef Gitelson AA, Kaufman YJ, Stark R, Rundquist D (2002) Novel algorithms for remote estimation of vegetation fraction. Remote Sens Environ 80(1):76–87CrossRef
Zurück zum Zitat Guo Z, Chi H, Sun G (2010) Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data. Science China Earth Sci 53(1):16–25CrossRef Guo Z, Chi H, Sun G (2010) Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data. Science China Earth Sci 53(1):16–25CrossRef
Zurück zum Zitat Haripriya GS (2000) Estimates of biomass in Indian forests. Biomass Bioenerg 19(4):245–258CrossRef Haripriya GS (2000) Estimates of biomass in Indian forests. Biomass Bioenerg 19(4):245–258CrossRef
Zurück zum Zitat Haykin S (1994) Neural Networks: A Comprehensive Foundation. Prentice Hall PTR Upper Saddle River, New Jersey, USA Haykin S (1994) Neural Networks: A Comprehensive Foundation. Prentice Hall PTR Upper Saddle River, New Jersey, USA
Zurück zum Zitat Heyojoo BP, Nandy S (2014) Estimation of above-ground phytomass and carbon in tree resources outside the forest (TROF): A geo-spatial approach. Banko Janakari 24(1):34–40CrossRef Heyojoo BP, Nandy S (2014) Estimation of above-ground phytomass and carbon in tree resources outside the forest (TROF): A geo-spatial approach. Banko Janakari 24(1):34–40CrossRef
Zurück zum Zitat Holmström H, Fransson JES (2003) Combining remotely sensed optical and radar data in kNN estimation of forest variables. For Sci 49(3):409–418 Holmström H, Fransson JES (2003) Combining remotely sensed optical and radar data in kNN estimation of forest variables. For Sci 49(3):409–418
Zurück zum Zitat Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1):195–213CrossRef Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1):195–213CrossRef
Zurück zum Zitat Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309CrossRef Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309CrossRef
Zurück zum Zitat Hunt ER, Rock BN (1989) Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sens Environ 30(1):43–54CrossRef Hunt ER, Rock BN (1989) Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sens Environ 30(1):43–54CrossRef
Zurück zum Zitat Hyde P, Dubayah R, Walker W, Blair JB, Hofton M, Hunsaker C (2006) Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sens Environ 102(1–2):63–73CrossRef Hyde P, Dubayah R, Walker W, Blair JB, Hofton M, Hunsaker C (2006) Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sens Environ 102(1–2):63–73CrossRef
Zurück zum Zitat IPCC (2006) IPCC guidelines for national greenhouse gas inventories, Prepared by the National Greenhouse Gas Inventories Programme, Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (eds) Published: IGES, Japan IPCC (2006) IPCC guidelines for national greenhouse gas inventories, Prepared by the National Greenhouse Gas Inventories Programme, Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (eds) Published: IGES, Japan
Zurück zum Zitat Jiang G, Zhao D, Zhang G (2008) Seismic evidence for a metastable olivine wedge in the subducting Pacific slab under Japan Sea. Earth Planet Sci Lett 270(3):300–307CrossRef Jiang G, Zhao D, Zhang G (2008) Seismic evidence for a metastable olivine wedge in the subducting Pacific slab under Japan Sea. Earth Planet Sci Lett 270(3):300–307CrossRef
Zurück zum Zitat Jing L, Hu B, Noland T, Li J (2012) An individual tree crown delineation method based on multi-scale segmentation of imagery. ISPRS J. Photogramm. Remote Sens 70:88–98CrossRef Jing L, Hu B, Noland T, Li J (2012) An individual tree crown delineation method based on multi-scale segmentation of imagery. ISPRS J. Photogramm. Remote Sens 70:88–98CrossRef
Zurück zum Zitat Joos F, Gerber S, Prentice IC, Otto Bliesner BL, Valdes PJ (2004) Transient simulations of Holocene atmospheric carbon dioxide and terrestrial carbon since the Last Glacial Maximum. Global Biogeochem Cy 18(2)CrossRef Joos F, Gerber S, Prentice IC, Otto Bliesner BL, Valdes PJ (2004) Transient simulations of Holocene atmospheric carbon dioxide and terrestrial carbon since the Last Glacial Maximum. Global Biogeochem Cy 18(2)CrossRef
Zurück zum Zitat Joshi N, Baumann M, Ehammer A, Fensholt R, Grogan K, Hostert P, Jepsen MR, Kuemmerle T, Meyfroidt P, Mitchard ET, Reiche J (2016) A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens 8(1):70CrossRef Joshi N, Baumann M, Ehammer A, Fensholt R, Grogan K, Hostert P, Jepsen MR, Kuemmerle T, Meyfroidt P, Mitchard ET, Reiche J (2016) A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens 8(1):70CrossRef
Zurück zum Zitat Karna YK, Hussin YA, Gilani H, Bronsveld MC, Murthy MSR, Qamer FM, Karky BS, Bhattarai T, Aigong X, Baniya CB (2015) Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed, Nepal. Int J Appl Earth Obs Geoinform 38:280–291CrossRef Karna YK, Hussin YA, Gilani H, Bronsveld MC, Murthy MSR, Qamer FM, Karky BS, Bhattarai T, Aigong X, Baniya CB (2015) Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed, Nepal. Int J Appl Earth Obs Geoinform 38:280–291CrossRef
Zurück zum Zitat Keeling HC, Phillips OL (2007) The global relationship between forest productivity and biomass. Global Ecol Biogeogr 16(5):618–631CrossRef Keeling HC, Phillips OL (2007) The global relationship between forest productivity and biomass. Global Ecol Biogeogr 16(5):618–631CrossRef
Zurück zum Zitat Kellndorfer J, W Walker, L Pierce, C Dobson, JA Fites, C Hunsaker, J Vona, M Clutter (2004) Vegetation Height Estimation from Shuttle Radar Topography Mission and National Elevation Datasets. Remote Sens Environ 93 (3):339–358CrossRef Kellndorfer J, W Walker, L Pierce, C Dobson, JA Fites, C Hunsaker, J Vona, M Clutter (2004) Vegetation Height Estimation from Shuttle Radar Topography Mission and National Elevation Datasets. Remote Sens Environ 93 (3):339–358CrossRef
Zurück zum Zitat Kim Y, van Zyl JJ (2009) A time-series approach to estimate soil moisture using polarimetric radar data. IEEE T Geosci Remote Sens 47(8):2519–2527CrossRef Kim Y, van Zyl JJ (2009) A time-series approach to estimate soil moisture using polarimetric radar data. IEEE T Geosci Remote Sens 47(8):2519–2527CrossRef
Zurück zum Zitat Knapp AK, Smith MD (2001) Variation among biomes in temporal dynamics of aboveground primary production. Sci 291(5503):481–484CrossRef Knapp AK, Smith MD (2001) Variation among biomes in temporal dynamics of aboveground primary production. Sci 291(5503):481–484CrossRef
Zurück zum Zitat Koch B (2010) Status and Future of Laser Scanning, Synthetic Aperture Radar and Hyperspectral Remote Sensing Data for Forest Biomass Assessment. ISPRS J Photogramm Remote Sens 65 (6):581–590CrossRef Koch B (2010) Status and Future of Laser Scanning, Synthetic Aperture Radar and Hyperspectral Remote Sensing Data for Forest Biomass Assessment. ISPRS J Photogramm Remote Sens 65 (6):581–590CrossRef
Zurück zum Zitat Köhl M, Lasco R, Cifuentes M, Jonsson Ö, Korhonen KT, Mundhenk P, de Jesus Navar J, Stinson G (2015) Changes in forest production, biomass and carbon: Results from the 2015 UN FAO Global Forest Resource Assessment. For Ecol Manag 352:21–34CrossRef Köhl M, Lasco R, Cifuentes M, Jonsson Ö, Korhonen KT, Mundhenk P, de Jesus Navar J, Stinson G (2015) Changes in forest production, biomass and carbon: Results from the 2015 UN FAO Global Forest Resource Assessment. For Ecol Manag 352:21–34CrossRef
Zurück zum Zitat Kushwaha SPS, Nandy S, Gupta M (2014) Growing stock and woody biomass assessment in Asola-Bhatti Wildlife Sanctuary, Delhi, India. Environ Monitor Assess 186(9):5911–5920CrossRef Kushwaha SPS, Nandy S, Gupta M (2014) Growing stock and woody biomass assessment in Asola-Bhatti Wildlife Sanctuary, Delhi, India. Environ Monitor Assess 186(9):5911–5920CrossRef
Zurück zum Zitat Labrecque S, Fournier RA, Luther JE, Piercey D (2006) A comparison of four methods to map biomass from LandsatTM and inventory data in western Newfoundland. For Ecol Manag 226:129–144CrossRef Labrecque S, Fournier RA, Luther JE, Piercey D (2006) A comparison of four methods to map biomass from LandsatTM and inventory data in western Newfoundland. For Ecol Manag 226:129–144CrossRef
Zurück zum Zitat Le Toan T, Quegan S, Davidson MW, Balzter H, Paillou P, Papathanassiou K, Plummer S, Rocca F, Saatchi S, Shugart H, Ulander L (2011) The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ 115(11): 2850–2860CrossRef Le Toan T, Quegan S, Davidson MW, Balzter H, Paillou P, Papathanassiou K, Plummer S, Rocca F, Saatchi S, Shugart H, Ulander L (2011) The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ 115(11): 2850–2860CrossRef
Zurück zum Zitat Leal RR, Butler P, Lane P, Payne PA (1997) Data fusion and artificial neural networks for biomass estimation. IEE Proceedings-Science, Measurement and Technology 144(2): 69–72CrossRef Leal RR, Butler P, Lane P, Payne PA (1997) Data fusion and artificial neural networks for biomass estimation. IEE Proceedings-Science, Measurement and Technology 144(2): 69–72CrossRef
Zurück zum Zitat Li D (2010) Remotely Sensed Images and GIS Data Fusion for Automatic Change Detection. Int J Image Data Fusion 1(1): 99–108CrossRef Li D (2010) Remotely Sensed Images and GIS Data Fusion for Automatic Change Detection. Int J Image Data Fusion 1(1): 99–108CrossRef
Zurück zum Zitat Li X, Gar-On Yeh A, Wang S, Liu K, Liu X, Qian J, Chen X (2007) Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images. Int J Remote Sens 28(24):5567–5582CrossRef Li X, Gar-On Yeh A, Wang S, Liu K, Liu X, Qian J, Chen X (2007) Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images. Int J Remote Sens 28(24):5567–5582CrossRef
Zurück zum Zitat Liang S, Li X, Wang J (2012) Advanced Remote Sensing: Terrestrial Information Extraction and Applications. Academic Press, Oxford Liang S, Li X, Wang J (2012) Advanced Remote Sensing: Terrestrial Information Extraction and Applications. Academic Press, Oxford
Zurück zum Zitat Liu W, Song C, Schroeder TA, Cohen WB (2008) Predicting forest successional stages using multi-temporal Landsat imagery with forest inventory and analysis data. Int J Remote Sens 29: 3855–3872CrossRef Liu W, Song C, Schroeder TA, Cohen WB (2008) Predicting forest successional stages using multi-temporal Landsat imagery with forest inventory and analysis data. Int J Remote Sens 29: 3855–3872CrossRef
Zurück zum Zitat Lu D (2005) Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. Int J Rem Sens 26:2509–2525CrossRef Lu D (2005) Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. Int J Rem Sens 26:2509–2525CrossRef
Zurück zum Zitat Lu D (2006) The potential and Challenge of Remote Sensing-based Biomass Estimation. Int J Remote Sens 27 (7):1297–1328CrossRef Lu D (2006) The potential and Challenge of Remote Sensing-based Biomass Estimation. Int J Remote Sens 27 (7):1297–1328CrossRef
Zurück zum Zitat Lu D, Chen Q, Wang G, Liu L, Li G, Moran E (2014) A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int J Digit Earth 9(1):63–105CrossRef Lu D, Chen Q, Wang G, Liu L, Li G, Moran E (2014) A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int J Digit Earth 9(1):63–105CrossRef
Zurück zum Zitat Lu D, Mausel P, Brond’ızio E, Moran E (2004) Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. For Ecol Manag 198(1–3):149–167CrossRef Lu D, Mausel P, Brond’ızio E, Moran E (2004) Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. For Ecol Manag 198(1–3):149–167CrossRef
Zurück zum Zitat Lu D, Q Chen, G Wang, E Moran, M Batistella, M Zhang, G VaglioLaurin, D Saah. (2012) Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates. Int J For Res 2012:436537 Lu D, Q Chen, G Wang, E Moran, M Batistella, M Zhang, G VaglioLaurin, D Saah. (2012) Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates. Int J For Res 2012:436537
Zurück zum Zitat Maharjan S (2012) Estimation and mapping above ground woody carbon stocks using lidar data and digital camera imagery in the hilly forests of Gorkha, Nepal. Dessertation, Faculty of Geo-Information and Earth Observation (ITC), University of Twente, Enschede, The Netherlands Maharjan S (2012) Estimation and mapping above ground woody carbon stocks using lidar data and digital camera imagery in the hilly forests of Gorkha, Nepal. Dessertation, Faculty of Geo-Information and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
Zurück zum Zitat Mangla R, Kumar S, Nandy S (2016) Random forest regression modelling for forest aboveground biomass estimation using RISAT-1 PolSAR and terrestrial LiDAR data. In SPIE Asia-Pacific Remote Sensing (pp. 98790Q–98790Q); doi:https://doi.org/10.1117/12.2227380. Mangla R, Kumar S, Nandy S (2016) Random forest regression modelling for forest aboveground biomass estimation using RISAT-1 PolSAR and terrestrial LiDAR data. In SPIE Asia-Pacific Remote Sensing (pp. 98790Q–98790Q); doi:https://​doi.​org/​10.​1117/​12.​2227380.
Zurück zum Zitat Manna S, Nandy S, Chanda A, Akhand A, Hazra S, Dadhwal VK (2014) Estimating aboveground biomass in Avicennia marina plantation in Indian Sundarbans using high-resolution satellite data. J Appl Remote Sens 8(1):083638CrossRef Manna S, Nandy S, Chanda A, Akhand A, Hazra S, Dadhwal VK (2014) Estimating aboveground biomass in Avicennia marina plantation in Indian Sundarbans using high-resolution satellite data. J Appl Remote Sens 8(1):083638CrossRef
Zurück zum Zitat Mather P, Tso B (2009) Classification methods for remotely sensed data. CRC Press, New York Mather P, Tso B (2009) Classification methods for remotely sensed data. CRC Press, New York
Zurück zum Zitat Mather PM (1999) Computer processing of remotely-sensed images. John Wiley & Sons, England Mather PM (1999) Computer processing of remotely-sensed images. John Wiley & Sons, England
Zurück zum Zitat Means JE, Acker SA, Harding DJ, Blair JB, Lefsky MA, Cohen WB, Harmon ME, McKee WA (1999) Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the western cascades of Oregon. Remote Sens Environ 67(3):298–308CrossRef Means JE, Acker SA, Harding DJ, Blair JB, Lefsky MA, Cohen WB, Harmon ME, McKee WA (1999) Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the western cascades of Oregon. Remote Sens Environ 67(3):298–308CrossRef
Zurück zum Zitat Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-Being: biodiversity synthesis. World Resources Institute, Washington, DC Millennium Ecosystem Assessment (2005) Ecosystems and Human Well-Being: biodiversity synthesis. World Resources Institute, Washington, DC
Zurück zum Zitat Mitchard ET, Saatchi SS, White L, Abernethy K, Jeffery KJ, Lewis SL, Collins M, Lefsky MA, Leal ME, Woodhouse IH, Meir P (2012) Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park Gabon: overcoming problems of high biomass and persistent cloud. Biogeosci 9:179–191CrossRef Mitchard ET, Saatchi SS, White L, Abernethy K, Jeffery KJ, Lewis SL, Collins M, Lefsky MA, Leal ME, Woodhouse IH, Meir P (2012) Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park Gabon: overcoming problems of high biomass and persistent cloud. Biogeosci 9:179–191CrossRef
Zurück zum Zitat Montesano PM, BD Cook, G Sun, M Simard, RF Nelson, KJ Ranson, Z Zhang, S Luthcke (2013) Achieving Accuracy Requirements for Forest Biomass Mapping: A Spaceborne Data Fusion Method for Estimating Forest Biomass and LiDAR Sampling Error. Remote Sens Environ 130:153–170CrossRef Montesano PM, BD Cook, G Sun, M Simard, RF Nelson, KJ Ranson, Z Zhang, S Luthcke (2013) Achieving Accuracy Requirements for Forest Biomass Mapping: A Spaceborne Data Fusion Method for Estimating Forest Biomass and LiDAR Sampling Error. Remote Sens Environ 130:153–170CrossRef
Zurück zum Zitat Mutanga O, Skidmore AK (2004) Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions. Int J Appl Earth Obs Geoinf 5:87–96CrossRef Mutanga O, Skidmore AK (2004) Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions. Int J Appl Earth Obs Geoinf 5:87–96CrossRef
Zurück zum Zitat Muukkonen P, Heiskanen J (2007) Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: a possibility to verify carbon inventories. Remote Sens Environ 107(4):617–624CrossRef Muukkonen P, Heiskanen J (2007) Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: a possibility to verify carbon inventories. Remote Sens Environ 107(4):617–624CrossRef
Zurück zum Zitat Nandy S, Kushwaha, SPS, Dadhwal VK (2011) Forest degradation assessment in the upper catchment of the river Tons using remote sensing and GIS. Ecolo Indic 11:509–513CrossRef Nandy S, Kushwaha, SPS, Dadhwal VK (2011) Forest degradation assessment in the upper catchment of the river Tons using remote sensing and GIS. Ecolo Indic 11:509–513CrossRef
Zurück zum Zitat Nandy S, Singh RP, Ghosh S, Watham T, Kushwaha SPS, Senthil Kumar A, Dadhwal VK (2017) Neural Network-based Modelling for Forest Biomass Assessment. Carbon Manag 8(4):305–317CrossRef Nandy S, Singh RP, Ghosh S, Watham T, Kushwaha SPS, Senthil Kumar A, Dadhwal VK (2017) Neural Network-based Modelling for Forest Biomass Assessment. Carbon Manag 8(4):305–317CrossRef
Zurück zum Zitat Negi JDS (1984) Biological productivity and cycling of nutrients in managed and man-made ecosystems; Ph.D. Thesis, Garhwal University, Srinagar, India Negi JDS (1984) Biological productivity and cycling of nutrients in managed and man-made ecosystems; Ph.D. Thesis, Garhwal University, Srinagar, India
Zurück zum Zitat Negi SS (1982) Environmental Problems in the Himalaya. Bishen Singh Mahendra Pal Singh, Dehradun, pp 188 Negi SS (1982) Environmental Problems in the Himalaya. Bishen Singh Mahendra Pal Singh, Dehradun, pp 188
Zurück zum Zitat Negrón-Juárez RI, Koven CD, Riley WJ, Knox RG, Chambers JQ (2015) Observed allocations of productivity and biomass, and turnover times in tropical forests are not accurately represented in CMIP5 Earth system models. Environ Res Lett 10(6):064017CrossRef Negrón-Juárez RI, Koven CD, Riley WJ, Knox RG, Chambers JQ (2015) Observed allocations of productivity and biomass, and turnover times in tropical forests are not accurately represented in CMIP5 Earth system models. Environ Res Lett 10(6):064017CrossRef
Zurück zum Zitat Nelson RF, Kimes DS, Salas WA, Routhier M (2000) Secondary forest age and tropical forest biomass estimation using Thematic Mapper imagery. Biogeosci 50:419–431 Nelson RF, Kimes DS, Salas WA, Routhier M (2000) Secondary forest age and tropical forest biomass estimation using Thematic Mapper imagery. Biogeosci 50:419–431
Zurück zum Zitat Overman JPM, HJL Witte, JG Saldarriaga (1994) Evaluation of Regression Models for Above-ground Biomass Determination in Amazon Rainforest. J Trop Ecol 10 (02):207–218CrossRef Overman JPM, HJL Witte, JG Saldarriaga (1994) Evaluation of Regression Models for Above-ground Biomass Determination in Amazon Rainforest. J Trop Ecol 10 (02):207–218CrossRef
Zurück zum Zitat Pan Y, Birdsey RA, Phillips OL, Jackson, RB (2013) The structure, distribution, and biomass of the world’s forests. Annu Rev Ecol Evol Syst 44:593–622CrossRef Pan Y, Birdsey RA, Phillips OL, Jackson, RB (2013) The structure, distribution, and biomass of the world’s forests. Annu Rev Ecol Evol Syst 44:593–622CrossRef
Zurück zum Zitat Powell SL, WB Cohen, SP Healey, RE Kennedy, GG Moisen, KB Pierce, JL Ohmann (2010) Quantification of Live Aboveground Forest Biomass Dynamics with Landsat Time-series and Field Inventory Data: A Comparison of Empirical Modeling Approaches. Remote Sens Environ 114 (5):1053–1068CrossRef Powell SL, WB Cohen, SP Healey, RE Kennedy, GG Moisen, KB Pierce, JL Ohmann (2010) Quantification of Live Aboveground Forest Biomass Dynamics with Landsat Time-series and Field Inventory Data: A Comparison of Empirical Modeling Approaches. Remote Sens Environ 114 (5):1053–1068CrossRef
Zurück zum Zitat Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48(2):119–126CrossRef Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48(2):119–126CrossRef
Zurück zum Zitat Ren HR, Zhou GS, Zhang XS (2011) Estimation of green aboveground biomass of desert steppe in Inner Mongolia based on red-edge reflectance curve area method. Biosyst Eng 109:385–395CrossRef Ren HR, Zhou GS, Zhang XS (2011) Estimation of green aboveground biomass of desert steppe in Inner Mongolia based on red-edge reflectance curve area method. Biosyst Eng 109:385–395CrossRef
Zurück zum Zitat Richardson AJ, Wiegand CL (1977) Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens 43(12):1541–1552 Richardson AJ, Wiegand CL (1977) Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens 43(12):1541–1552
Zurück zum Zitat Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55(2):95–107CrossRef Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55(2):95–107CrossRef
Zurück zum Zitat Roujean JL, Breon FM (1995) Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens Environ 51(3):375–384CrossRef Roujean JL, Breon FM (1995) Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens Environ 51(3):375–384CrossRef
Zurück zum Zitat Sales MH, Souza Jr CM, Kyriakidis PC, Roberts DA, Vidal E (2007) Improving spatial distribution estimation of forest biomass with geostatistics: a case study for rondônia, Brazil. Ecol Model 205:221–230CrossRef Sales MH, Souza Jr CM, Kyriakidis PC, Roberts DA, Vidal E (2007) Improving spatial distribution estimation of forest biomass with geostatistics: a case study for rondônia, Brazil. Ecol Model 205:221–230CrossRef
Zurück zum Zitat Santin-Janin H, Garel M, Chapuis JL, Pontier D (2009) Assessing the performance of NDVI as a proxy for plant biomass using non-linear models: a case study on the Kerguelen archipelago. Pol Biol 32(6):861–871CrossRef Santin-Janin H, Garel M, Chapuis JL, Pontier D (2009) Assessing the performance of NDVI as a proxy for plant biomass using non-linear models: a case study on the Kerguelen archipelago. Pol Biol 32(6):861–871CrossRef
Zurück zum Zitat Sarker LR, Nichol JE (2011) Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sens Environ 115: 968–977CrossRef Sarker LR, Nichol JE (2011) Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sens Environ 115: 968–977CrossRef
Zurück zum Zitat Sharma A, Prasad R, Saksena S, Joshi V (1999) Micro-level sustainable biomass system development in central Himalayas: stress computation and biomass planning. Sust Dev 7 (3):132–139CrossRef Sharma A, Prasad R, Saksena S, Joshi V (1999) Micro-level sustainable biomass system development in central Himalayas: stress computation and biomass planning. Sust Dev 7 (3):132–139CrossRef
Zurück zum Zitat Shimano K (1997) Analysis of the relationship between DBH and crown projection area using a new model. J For Res 2(4): 237–242CrossRef Shimano K (1997) Analysis of the relationship between DBH and crown projection area using a new model. J For Res 2(4): 237–242CrossRef
Zurück zum Zitat Shugart HH, Saatchi S, Hall FG (2010) Importance of structure and its measurement in quantifying function of forest ecosystems. J Geophys Res 115 (G2): G00E13CrossRef Shugart HH, Saatchi S, Hall FG (2010) Importance of structure and its measurement in quantifying function of forest ecosystems. J Geophys Res 115 (G2): G00E13CrossRef
Zurück zum Zitat Soenen SA, Peddle DR, Hall RJ, Coburn CA, Hall FG (2010) Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain. Remote Sens Environ 114:1325–1337CrossRef Soenen SA, Peddle DR, Hall RJ, Coburn CA, Hall FG (2010) Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain. Remote Sens Environ 114:1325–1337CrossRef
Zurück zum Zitat Somanathan E (1991) Deforestation, property rights, and incentives in central Himalaya. Econ Pol Wkly 26:37–46 Somanathan E (1991) Deforestation, property rights, and incentives in central Himalaya. Econ Pol Wkly 26:37–46
Zurück zum Zitat Swatantran A, Dubayah R, Roberts D, Hofton M, Blair JB (2011) Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion. Remote Sens Environ 115(11): 2917–2930CrossRef Swatantran A, Dubayah R, Roberts D, Hofton M, Blair JB (2011) Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion. Remote Sens Environ 115(11): 2917–2930CrossRef
Zurück zum Zitat Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150CrossRef Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150CrossRef
Zurück zum Zitat Valeriano MDM, Sanches IDA, Formaggio AR (2016) Topographic effect on spectral vegetation indices from landsat tm data: is topographic correction necessary? B Cienc Geod 22(1):95–107CrossRef Valeriano MDM, Sanches IDA, Formaggio AR (2016) Topographic effect on spectral vegetation indices from landsat tm data: is topographic correction necessary? B Cienc Geod 22(1):95–107CrossRef
Zurück zum Zitat Vapnik V (2006) Estimation of Dependences Based on Empirical Data. Springer Science & Business Media Vapnik V (2006) Estimation of Dependences Based on Empirical Data. Springer Science & Business Media
Zurück zum Zitat Veraverbeke S, Verstraeten WW, Lhermitte S, Goossens R (2010) Illumination effects on the differenced Normalized Burn Ratio’s optimality for assessing fire severity. Int J Appl Earth Obs 2:60–70CrossRef Veraverbeke S, Verstraeten WW, Lhermitte S, Goossens R (2010) Illumination effects on the differenced Normalized Burn Ratio’s optimality for assessing fire severity. Int J Appl Earth Obs 2:60–70CrossRef
Zurück zum Zitat Viana HJ, Lopes AD, Cohenc WB (2012) Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models. Ecol Model 226:22–35CrossRef Viana HJ, Lopes AD, Cohenc WB (2012) Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models. Ecol Model 226:22–35CrossRef
Zurück zum Zitat Wang Y, Hou X, Wang M, Wang M, Wu L, Ying L, Feng Y (2012) Topographic controls on vegetation index in a hilly landscape: a case study in the Jiaodong Peninsula, eastern China. Environ Earth Sci 70:625–634CrossRef Wang Y, Hou X, Wang M, Wang M, Wu L, Ying L, Feng Y (2012) Topographic controls on vegetation index in a hilly landscape: a case study in the Jiaodong Peninsula, eastern China. Environ Earth Sci 70:625–634CrossRef
Zurück zum Zitat Waring RH, Way J, Hunt ER, Morrissey L, Ranson KJ, Weishampel JF, Oren R, Franklin SE (1995) Imaging radar for ecosystem studies. BioSci 45:715–723CrossRef Waring RH, Way J, Hunt ER, Morrissey L, Ranson KJ, Weishampel JF, Oren R, Franklin SE (1995) Imaging radar for ecosystem studies. BioSci 45:715–723CrossRef
Zurück zum Zitat Watham T, Kushwaha SPS, Nandy S, Patel NR, Ghosh S (2016) Forest carbon stock assessment at Barkot Flux tower Site (BFS) using field inventory, Landsat-8 OLI data and geostatistical techniques. Int J Multidisc Res Dev 3 (5):111–119 Watham T, Kushwaha SPS, Nandy S, Patel NR, Ghosh S (2016) Forest carbon stock assessment at Barkot Flux tower Site (BFS) using field inventory, Landsat-8 OLI data and geostatistical techniques. Int J Multidisc Res Dev 3 (5):111–119
Zurück zum Zitat Watham T, Patel NR, Kushwaha SPS, Dadhwal VK, Kumar AS (2017) Evaluation of remote-sensing-based models of gross primary productivity over Indian sal forest using flux tower and MODIS satellite data. Int J Remote sens 38(18): 5069–5090CrossRef Watham T, Patel NR, Kushwaha SPS, Dadhwal VK, Kumar AS (2017) Evaluation of remote-sensing-based models of gross primary productivity over Indian sal forest using flux tower and MODIS satellite data. Int J Remote sens 38(18): 5069–5090CrossRef
Zurück zum Zitat Webster R, Oliver MA. (2001) Geostatistics for environmental scientists. New York: Wiley. Webster R, Oliver MA. (2001) Geostatistics for environmental scientists. New York: Wiley.
Zurück zum Zitat Xiao X, Boles S, Frolking S, Salas W, Moore Iii B, Li C, He L, Zhao R (2002) Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data. Int J Remote Sens 23(15):3009–3022CrossRef Xiao X, Boles S, Frolking S, Salas W, Moore Iii B, Li C, He L, Zhao R (2002) Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data. Int J Remote Sens 23(15):3009–3022CrossRef
Zurück zum Zitat Xing Y, de Gier A, Zhang J,Wang L (2010) An improved method for estimating forest canopy height using ICESat-GLAS full waveform data over sloping terrain: A case study in Changbai mountains, China. Int J Appl Earth Obs 12(5):385–392CrossRef Xing Y, de Gier A, Zhang J,Wang L (2010) An improved method for estimating forest canopy height using ICESat-GLAS full waveform data over sloping terrain: A case study in Changbai mountains, China. Int J Appl Earth Obs 12(5):385–392CrossRef
Zurück zum Zitat Yadav BKV, Nandy S (2015) Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques. Environ Monitor Assess 187(5):1–12CrossRef Yadav BKV, Nandy S (2015) Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques. Environ Monitor Assess 187(5):1–12CrossRef
Zurück zum Zitat Yan F, Wu B, Wang YJ (2013) Estimating aboveground biomass in Mu Us Sandy Land using Landsat spectral derived vegetation indices over the past 30 years. J Arid Land 5:521–530CrossRef Yan F, Wu B, Wang YJ (2013) Estimating aboveground biomass in Mu Us Sandy Land using Landsat spectral derived vegetation indices over the past 30 years. J Arid Land 5:521–530CrossRef
Zurück zum Zitat Zhang G, Ganguly S, Nemani RR, White MA, Milesi C, Hashimoto H, Wang W, Saatchi S, Yu Y, Myneni RB (2014) Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data. Remote Sens Environ 151:44–56CrossRef Zhang G, Ganguly S, Nemani RR, White MA, Milesi C, Hashimoto H, Wang W, Saatchi S, Yu Y, Myneni RB (2014) Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data. Remote Sens Environ 151:44–56CrossRef
Metadaten
Titel
Remote Sensing-Based Forest Biomass Assessment in Northwest Himalayan Landscape
verfasst von
Subrata Nandy
Surajit Ghosh
S. P. S. Kushwaha
A. Senthil Kumar
Copyright-Jahr
2019
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-13-2128-3_13

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