Abstract

Tactical decisions on natural resource management require accurate and up to date spatial information for sustainable forest management. Remote sensing devices by the use of multispectral data obtained from satellites or airborne sensors, allow substantial data acquisition that reduce cost of data collection and satisfy demands for continuous precise data. Forest height and Diameter at Breast Height (DBH) are crucial variables to predict volume and biomass. Traditional methods for estimation of tree heights and biomass are time consuming and labour intensive making it difficult for countries to carry out periodic National forest inventories to support forest management and REDD+ activities. This study assessed the applicability of LiDAR data in estimating tree height and biomass in a variety of forest conditions in Londiani Forest Block. The target forests were natural forest, plantation forests and other scattered forests analysed in a variety of topographic conditions. LiDAR data were collected by an aircraft flying at an elevation of 1550 m. The LIDAR pulses hitting the forest were used to estimate the forest height and the density of the vegetation, which implied biomass. LiDAR data were collected in 78 sampling plots of 15 m radius. The LiDAR data were ground truthed to compare its accuracy for above ground biomass (AGB) and height estimation. The correlation coefficients for heights between LiDAR and field data were 0.92 for the pooled data, 0.79 in natural forest, 0.95 in plantation forest and 0.92 in other scattered forest. AGB estimated from LiDAR and ground truthed data had a correlation coefficient of 0.86 for the pooled data, 0.78 in natural forest, 0.84 in plantation forest and 0.51 in other scattered forests. This implied 62%, 84% and 89% accuracy of AGB estimation in natural forests, other scattered forests and plantation forests respectively. The even aged conditions of plantation forests might have resulted to better estimates of height and AGB as compared to uneven aged natural forests and scattered forests. The results imply the reliability of using Airborne LIDAR scanning in forest biomass estimates in Kenya and are an option for supporting a National Forest Monitoring System for REDD+.

Highlights

  • Forest operations require reliable information on forest status and its evolution (Ruiz et al, 2014)

  • The Light detection and Ranging (LiDAR) data were ground truthed to compare its accuracy for above ground biomass (AGB) and height estimation

  • LiDAR technology offers a great capability and ability in estimating vegetation parameters that aid in estimating above ground biomass in instances where ground measurements cannot be actualized

Read more

Summary

Introduction

Forest operations require reliable information on forest status and its evolution (Ruiz et al, 2014). Forest inventory estimation begun in the middle ages (McRoberts et al, 2010). The manual methods of forest inventories involved consumed time and cost, as an aid to this the Remote Sensing technology has been used. This technology has been used in various applications around the world: natural resource management (forest ecosystem management) for many years (Chen et al, 2005), mapping, modeling and understanding of the ecosystem (Lefsky et al, 2002) among others. Conventional applications of remote sensing used images from passive sensors (Goward & Williams 1997) though topographical covers and weather condition influence them. RADARSAT, obtained from active radar sensors, has been used (Waring et al, 1995)

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call