Next Article in Journal
Mapping and Attributing Normalized Difference Vegetation Index Trends for Nepal
Next Article in Special Issue
Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest
Previous Article in Journal
Wuhan Surface Subsidence Analysis in 2015–2016 Based on Sentinel-1A Data by SBAS-InSAR
Previous Article in Special Issue
Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(10), 984; https://doi.org/10.3390/rs9100984

Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data

1
Department of National Forest Inventory, Norwegian Institute for Bioeconomy Research (NIBIO), Ås 1430, Norway
2
Faculty of Environmental Sciences and Resource Management, Norwegian University of Life Sciences (NMBU), Ås 1430, Norway
3
Department of Forest Mensuration and Management, Sokoine University of Agriculture (SUA), Morogoro 3000, Tanzania
4
Department of Forest Engineering and Wood Sciences, Sokoine University of Agriculture (SUA), Morogoro 3000, Tanzania
*
Author to whom correspondence should be addressed.
Received: 11 August 2017 / Revised: 13 September 2017 / Accepted: 19 September 2017 / Published: 22 September 2017
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
Full-Text   |   PDF [4165 KB, uploaded 22 September 2017]   |  

Abstract

The use of Interferometric Synthetic Aperture Radar (InSAR) data has great potential for monitoring large scale forest above ground biomass (AGB) in the tropics due to the increased ability to retrieve 3D information even under cloud cover. To date; results in tropical forests have been inconsistent and further knowledge on the accuracy of models linking AGB and InSAR height data is crucial for the development of large scale forest monitoring programs. This study provides an example of the use of TanDEM-X WorldDEM data to model AGB in Tanzanian woodlands. The primary objective was to assess the accuracy of a model linking AGB with InSAR height from WorldDEM after the subtraction of ground heights. The secondary objective was to assess the possibility of obtaining InSAR height for field plots when the terrain heights were derived from global navigation satellite systems (GNSS); i.e., as an alternative to using airborne laser scanning (ALS). The results revealed that the AGB model using InSAR height had a predictive accuracy of R M S E = 24.1 t·ha−1; or 38.8% of the mean AGB when terrain heights were derived from ALS. The results were similar when using terrain heights from GNSS. The accuracy of the predicted AGB was improved when compared to a previous study using TanDEM-X for a sub-area of the area of interest and was of similar magnitude to what was achieved in the same sub-area using ALS data. Overall; this study sheds new light on the opportunities that arise from the use of InSAR data for large scale AGB modelling in tropical woodlands. View Full-Text
Keywords: InSAR; TanDEM-X; above ground biomass; tropical woodlands; LiDAR; forest monitoring; REDD+ InSAR; TanDEM-X; above ground biomass; tropical woodlands; LiDAR; forest monitoring; REDD+
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Puliti, S.; Solberg, S.; Næsset, E.; Gobakken, T.; Zahabu, E.; Mauya, E.; Malimbwi, R.E. Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data. Remote Sens. 2017, 9, 984.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top