Comparing Tree Heights among Montane Forest Blocks of Kenya Using LiDAR Data from GLAS

Abstract

This study was designed to use LiDAR data to research tree heights in montane forest blocks of Kenya. It uses a completely randomised block design to asses if differences exist in forest heights: 1) among montane forest blocks, 2) among Agro ecological zones (AEZ) within each forest block and 3) between similar AEZ in different forest blocks. Forest height data from the Geoscience Laser Altimeter System (GLAS) on the Ice Cloud and Land Elevation Satellite (ICE-SAT) for the period 2003-2009 was used for 2146 circular plots, of 0.2-0.25 ha in size. Results indicate that, tree height is largely influenced by Agro ecological conditions and the wetter zones have taller trees in the upper, middle and lower highlands. In the upper highland zones of limited human activity, tree heights did not vary among forest blocks. Variations in height among forest blocks and within forest blocks were exaggerated in regions of active human intervention.

Estimation of Tree Height and Forest Biomass Using Airborne LiDAR Data: A Case Study of Londiani Forest Block in the Mau Complex, Kenya

“…The difference is attributed to the accuracy of height estimation which has been described as inaccurate in many forestry inventories (Kinyanjui et al, 2015). In addition to this error was propagated into the AGB calculation for LiDAR data because the model used to estimate AGB using ALS was calibrated using ground data.…”

“…However, this should be assessed against the ability to capture more accurate information which might be realized on a bigger plot. Finally, the availability of the GLAS data for Kenya which covers many forest blocks of Kenya and has many data points (Kinyanjui et al, 2015) should be correlated to this work to find the possibility of estimating biomass on these forests covered by the GLAS data and this would reduce the cost of carrying out a national forest inventory.…”

“…This has led to a successful creation of forest inventories at reduced costs (Ruiz et al, 2014). In Kenya, Kinyanjui et al (2015) demonstrated that LiDAR data can be used to classify the montane forests to show height variations among agro climatic zones and human use zones. However, their work was not ground truthed and gave an estimate of tree heights in the different forests.…”