Next Article in Journal
Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV)
Next Article in Special Issue
Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data
Previous Article in Journal
An Adaptive Offset Tracking Method with SAR Images for Landslide Displacement Monitoring
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessFeature PaperArticle
Remote Sens. 2017, 9(8), 827; doi:10.3390/rs9080827

Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya

1
Department of Geosciences and Geography, University of Helsinki, P.O. Box 68, FI-00014 Helsinki, Finland
2
Fisheries and Environmental Management Group, Department of Environmental Sciences, University of Helsinki, P.O. Box 65, FI-00014 Helsinki, Finland
*
Author to whom correspondence should be addressed.
Received: 21 June 2017 / Revised: 4 August 2017 / Accepted: 7 August 2017 / Published: 11 August 2017
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
View Full-Text   |   Download PDF [13211 KB, uploaded 11 August 2017]   |  

Abstract

Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were (1) to generate a map of AGB for the Taita Hills, in Kenya, based on field measurements and airborne laser scanning (ALS), and (2) to examine determinants of AGB using geospatial data and statistical modelling. The study area is located in the northernmost part of the Eastern Arc Mountains, with an elevation range of approximately 600–2200 m. The field measurements were carried out in 215 plots in 2013–2015 and ALS flights conducted in 2014–2015. Multiple linear regression was used for predicting AGB at a 30 m × 30 m resolution based on canopy cover and the 25th percentile height derived from ALS returns (R2 = 0.88, RMSE = 52.9 Mg ha−1). Boosted regression trees (BRT) were used for examining the relationship between AGB and explanatory variables at a 250 m × 250 m resolution. According to the results, AGB patterns were controlled mainly by mean annual precipitation (MAP), the distribution of croplands and slope, which explained together 69.8% of the AGB variation. The highest AGB densities have been retained in the semi-natural vegetation in the higher elevations receiving more rainfall and in the steep slope, which is less suitable for agriculture. AGB was also relatively high in the eastern slopes as indicated by the strong interaction between slope and aspect. Furthermore, plantation forests, topographic position and the density of buildings had a minor influence on AGB. The findings demonstrate the utility of ALS-based AGB maps and BRT for describing AGB distributions across Afromontane landscapes, which is important for making sustainable land management decisions in the region. View Full-Text
Keywords: airborne laser scanning; boosted regression trees; carbon; LiDAR; REDD+ airborne laser scanning; boosted regression trees; carbon; LiDAR; REDD+
Figures

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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Adhikari, H.; Heiskanen, J.; Siljander, M.; Maeda, E.; Heikinheimo, V.; K. E. Pellikka, P. Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya. Remote Sens. 2017, 9, 827.

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