Abstract
Above-ground biomass estimation of successional and mature forests in moist tropical regions is attracting increasing attention. Because of complex stand structure and abundant vegetation species, rarely has remote-sensing research been successfully conducted in biomass estimation for moist tropical areas. In this paper, two study areas in the Brazilian Amazon basin—Altamira and Bragantina— with different biophysical characteristics were selected. Atmospherically corrected Thematic Mapper (TM) images and field vegetation inventory data were used in the analysis, and different vegetation indices and texture measures were explored. Multiple regression models were developed through integration of image data (including TM bands, different vegetation indices, and texture measures) and vegetation inventory data. These models were used for biomass estimation in both selected study areas. This study concludes that neither TM spectral bands nor vegetation indices alone are sufficient to establish an effective model for biomass estimation, but multiple regression models that consist of spectral and textural signatures improve biomass estimation performance. The models developed are especially suitable for above-ground biomass estimation of dense vegetation areas.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Refrences
Anderson GL, Hanson JD (1992) Evaluating handheld radiometer derived vegetation indices for estimating above ground biomass. Geocarto International 7:71–78
Anderson GL, Hanson JD, Haas RH (1993) Evaluating Landsat Thematic Mapper derived Vegetation indices for estimating above-ground biomass on semiarid rangelands. Remote Sensing of Environment 45:165–175
Bannari A, Morin D, Bonn F, Huete AR (1995) A review of vegetation indices. Remote Sensing Reviews 13:95–120
Eastwood JA, Yates MG, Thomson AG, Fuller RM (1997) The reliability of vegetation indices for monitoring saltmarsh vegetation cover. International Journal of Remote Sensing 18:3901–3907
Franklin J, Hiernaux PYH (1991) Estimating foliage and woody biomass in Sahelian and Sudanian woodlands using a remote sensing model. International Journal of Remote Sensing 12:1387–1404
Franklin SE, Peddle DR (1989) Spectral texture for improved class discrimination in complex terrain. International Journal of Remote Sensing 10:1437–1443
Gillespie AJR, Brown S, Lugo AE (1992) Tropical forest biomass estimation from truncated stand tables. Forest Ecology and Management 48:69–87
Gordon DK, Phillipson WR (1986) A texture enhancement procedure for separating orchard from forest in Thematic Mapper imagery. International Journal of Remote Sensing 8:301–304
Haralick RM (1979) Statistical and structural approaches to texture. Proceedings of the IEEE 67(May):786–804
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics SMC-3:610–620
Jakubauskas ME, Price KP (1997) Empirical relationships between structural and spectral factors of Yellowstone lodgepole pine forest. Photogrammetric Engineering and Remote Sensing 63:1375–1381
Leblon B, Granberg H, Ansseau C, Royer A (1993) A semi-empirical model to estimate the biomass production of forest canopies from spectral variables, Part 1: Relationship between spectral variables and light interception efficiency. Remote Sensing Reviews 7:109–125
Lu DS (2001) Estimation of forest stand parameters and application in classification and change detection of forest cover types in the Brazilian Amazon Basin. Ph.D. diss., Indiana State University, Terre Haute, Indiana
Lu DS, Mausel P, Brondizio ES, Moran E (‘No Date’) Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. International Journal of Remote Sensing (in press)
Marceau DJ, Howarth PJ, Dubois JM, Gratton DJ (1990) Evaluation of the grey-level cooccurrence matrix method for land-cover classification using SPOT imagery. IEEE Transactions on Geoscience and Remote Sensing 28:513–519
Moran E, Brondizio E, McCracken S (‘No Date’) Trajectories of land use: soils, succession, and crop choice. In: Wood C et al. (eds) Patterns and Processes of Land Use and Forest Change in the Amazon. Gainesville: University Press of Florida (in press)
Nelson BW, Mesquita R, Pereira JLG, de Souza SGA, Batista GT, Couto, LB (1999) Allometric regression for improved estimate of secondary forest biomass in the central Amazon. Forest Ecology and Management 117:149–167
Nelson R, Krabill W, Tonelli J (1988) Estimating forest biomass and volume using airborne laser data. Remote Sensing of Environment 24:247–267
Nelson RF, Kimes DS, Salas WA, Routhier M (2000) Secondary forest age and tropical forest biomass estimation using Thematic Mapper imagery. Bioscience 50:419–431
Overman JPM, Witte HJL, Saldarriaga JG (1994) Evaluation of regression models for above-ground biomass determination in Amazon rainforest. Journal of Tropical Ecology 10:207–218
Peddle DR, Franklin SE (1991) Image texture processing and data integration for surface pattern discrimination. Photogrammetric Engineering and Remote Sensing 57:413–420
Roy PS, Ravan SA (1996) Biomass estimation using satellite remote sensing data — an investigation on possible approaches for natural forest. Journal of Bioscience 21:535–561
Steininger MK (2000) Satellite estimation of tropical secondary forest above-ground biomass data from Brazil and Bolivia. International Journal of Remote Sensing 21:1139–1157
Tucker JM, Brondizio ES, Moran EF (1998) Rates of forest regrowth in Eastern Amazonia: a comparison of Altamira and Bragantina regions, Para State, Brazil. Interciencia 23:64–73
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, D., Mausel, P., Brondizio, E., Moran, E. (2002). Above-Ground Biomass Estimation of Successional and Mature Forests Using TM Images in the Amazon Basin. In: Richardson, D.E., van Oosterom, P. (eds) Advances in Spatial Data Handling. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56094-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-56094-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-62859-7
Online ISBN: 978-3-642-56094-1
eBook Packages: Springer Book Archive