Improved forest biomass estimates using ALOS AVNIR-2 texture indices

• Published in: Remote sensing of environment Vol. 115; no. 4; pp. 968 - 977
• Main Authors: Sarker, Latifur Rahman, Nichol, Janet E.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15-04-2011; Elsevier
• Subjects: ALOS (satellite); Animal and plant ecology; Animal, plant and microbial ecology; Applied geophysics; AVNIR-2; Biological and medical sciences; Biomass; Biomass estimation; Earth sciences; Earth, ocean, space; Estimates; Exact sciences and technology; Forests; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Image processing techniques; Internal geophysics; Mathematical models; Surface layer; Synecology; Teledetection and vegetation maps; Terrestrial ecosystems; Texture; Texture measurement; Vegetation; Image processing techniques; AVNIR-2; Texture measurement; Biomass estimation; image processing; biomass; mountains; Spectral band; Vegetation index; textures; fine-grained materials; remote sensing; measurement sensor; subtropical zone; image analysis; forests; Multiple regression; Spectral data; Regression model; Reflectance; high resolution
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2010.11.010

Optical remote sensing is still one of the most attractive choices for obtaining biomass information, as new sensors are available with fine spatial and spectral resolutions. Better biomass estimates may be possible if suitable processing techniques for these sensors can be demonstrated. This research investigates the potential of high resolution optical data from the ALOS AVNIR-2 sensor for biomass estimation in a mountainous, subtropical forested region using four different types of image processing techniques including i) spectral reflectance and simple spectral band ratio, ii) commonly used vegetation indices, iii) texture parameters and iv) ratio of texture parameters. Simple linear and stepwise multiple regression models were developed between biomass data from 50 field plots, and image parameters derived from these techniques.
Results indicate that spectral reflectance, the simple band ratio, and commonly used vegetation indices have relatively low potential for biomass estimation, as only about 58% of the variability in the field data was explained by the model (adjusted r
2
=
0.58 and RMSE
=
64
t/ha). However, the texture parameters of spectral bands were found to be effective for biomass estimation with an explained variability of ca. 76% (adjusted r
2
=
0.76 and RMSE
=
46
t/ha). The result was further improved to adjusted r
2
=
0.88 (RMSE
=
32
t/ha) using the simple ratio of texture parameters. The results suggest that the performance of biomass estimation can be improved significantly using the texture parameters of high resolution optical data, and further improvement can be obtained using the ratio of texture parameters, as this combines the advantages of both texture and ratio.
► We estimate forest biomass using optical data from AVNIR-2. ► We examine vegetation indices, texture measurement and texture indices. ► Vegetation indices are unable to predict forest biomass in high biomass conditions. ► Texture measurements of optical data show better performance in biomass estimation. ► Texture ratios outperform all other processes and minimize the saturation problem.