Multi-sensor forest vegetation height mapping methods for Tanzania

European Journal of Remote Sensing

Jörg Haarpaintner¹  Øivind Due Trier²  Arnt-Børre Salberg²  Terje Gobakken³  Erik Næsset³  Dagrun Aarsten⁴ 

[1] Norut - Northern Research Institute;Norwegian Computing Center;Norwegian University of Life Sciences;TerraTec AS;

关键词: Specific leaf area index;    cloud masking;    Landsat;    Sentinel-2;    ALOS PALSAR;    airborne laser scanning;   

DOI  :  10.1080/22797254.2018.1461533

来源: DOAJ

【 摘 要 】

This paper proposes a new method for mapping of forest cover in Tanzania, in the form of yearly estimates of average vegetation height from time-series of Landsat and ALOS PALSAR satellite images. By using airborne laser scanning data and Landsat-8 data from 2014, a regression between average vegetation height and the specific leaf area vegetation index is established. By using all available Landsat acquisitions of the same area within 1 year, and producing a yearly estimate of vegetation height, the estimation error variance is reduced. The variance is further reduced by Kalman filtering the sequence of yearly estimates. A multi-sensor version of the method comprises application of the radar backscatter when L-band SAR data is available. To conclude, we have demonstrated that estimation of mean vegetation height is possible from dense time series of optical and SAR satellite data. Change detection was able to detect areas with total loss of biomass.

【 授权许可】

Unknown