Abstract: Forest inventories are essential to assess the composition, structure and distribution of forest vegetation that can be used as baseline information for management decisions. Classical forest inventory is labor intensive and time-consuming and sometimes even dangerous. The use of Light Detection and Ranging (LiDAR) in forest inventory would improve and overcome these restrictions. This study was conducted to determine the possibility of using LiDAR derived data in extracting high accuracy forest biophysical parameters and as a non-destructive method for forest status analysis of San Manual, Pangasinan. Forest resources extraction was carried out using LAS tools, GIS, Envi and .bat scripts with the available LiDAR data. The process includes the generation of derivatives such as Digital Terrain Model (DTM), Canopy Height Model (CHM) and Canopy Cover Model (CCM) in .bat scripts followed by the generation of 17 composite bands to be used in the extraction of forest classification covers using ENVI 4.8 and GIS software. The Diameter in Breast Height (DBH), Above Ground Biomass (AGB) and Carbon Stock (CS) were estimated for each classified forest cover and Tree Count Extraction was carried out using GIS. Subsequently, field validation was conducted for accuracy assessment. Results showed that the forest of San Manuel has 73% Forest Cover, which is relatively much higher as compared to the 10% canopy cover requirement. On the extracted canopy height, 80% of the tree’s height ranges from 12 m to 17 m. CS of the three forest covers based on the AGB were: 20819.59 kg/20x20 m for closed broadleaf, 8609.82 kg/20x20 m for broadleaf plantation and 15545.57 kg/20x20m for open broadleaf. Average tree counts for the tree forest plantation was 413 trees/ha. As such, the forest of San Manuel has high percent forest cover and high CS.
Abstract: The development of allometric models is crucial to
accurate forest biomass/carbon stock assessment. The aim of this
study was to develop a set of biomass prediction models that will
enable the determination of total tree aboveground biomass for
savannah woodland area in Niger State, Nigeria. Based on the data
collected through biometric measurements of 1816 trees and
destructive sampling of 36 trees, five species specific and one site
specific models were developed. The sample size was distributed
equally between the five most dominant species in the study site
(Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa,
Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the
equations were developed for five individual species. Secondly these
five species were mixed and were used to develop an allometric
equation of mixed species. Overall, there was a strong positive
relationship between total tree biomass and the stem diameter. The
coefficient of determination (R2 values) ranging from 0.93 to 0.99 P
< 0.001 were realised for the models; with considerable low standard
error of the estimates (SEE) which confirms that the total tree above
ground biomass has a significant relationship with the dbh. F-test
values for the biomass prediction models were also significant at p
Abstract: Open burning of sugarcane fields is recognized to have a negative impact on soil by degrading its properties, especially soil organic carbon (SOC) content. Better understating the effect of open burning on soil carbon dynamics is crucial for documenting the carbon sequestration capacity of agricultural soils. In this study, experiments to investigate soil carbon stocks under burned and unburned sugarcane plantation systems in Thailand were conducted. The results showed that cultivation fields without open burning during 5 consecutive years enabled to increase the SOC content at a rate of 1.37 Mg ha-1y-1. Also it was found that sugarcane fields burning led to about 15% reduction of the total carbon stock in the 0-30 cm soil layer. The overall increase in SOC under unburned practice is mainly due to the large input of organic material through the use of sugarcane residues.