Carbon assessment for cocoa cropping systems in Lampung, Indonesia

Cocoa (Theobroma cacao L.) production plays a key role in the economics of Indonesia, the world’s fourth largest cocoa bean producing country. With more than 1.6 million hectares of land planted with cocoa, small improvements in emissions efficiencies or carbon sequestration opportunities can have a relatively large mitigating effect on emissions from agroforestry and land use. The carbon assessment in Lampung, Sumatra was done to evaluate environmental impacts of cocoa as a commodity through estimation of carbon stock and carbon footprint, GHG emissions during the cultivation of cocoa in different cropping systems. Segmentation of cropping systems along density of intercropping, inputs use intensity and residue management practices identify opportunities for climate smart practices tailored to each segment

Individual Tree Species Classification from Airborne Multisensor Imagery Using Robust PCA

Remote sensing of individual tree species has many applications in resource management, biodiversity assessment, and conservation. Airborne remote sensing using light detection and ranging (LiDAR) and hyperspectral sensors has been used extensively to extract biophysical traits of vegetation and to detect species. However, its application for individual tree mapping remains limited due to the technical challenges of precise coalignment of images acquired from different sensors and accurately delineating individual tree crowns (ITCs). In this study, we developed a generic workflow to map tree species at ITC level from hyperspectral imagery and LiDAR data using a combination of well established and recently developed techniques. The workflow uses a nonparametric image registration approach to coalign images, a multiclass normalized graph cut method for ITC delineation, robust principal component analysis for feature extraction, and support vector machine for species classification. This workflow allows us to automatically map tree species at both pixel- and ITC-level. Experimental tests of the technique were conducted using ground data collected from a fully mapped temperate woodland in the UK. The overall accuracy of pixel-level classification was 91%, while that of ITC-level classification was 61%. The test results demonstrate the effectiveness of the approach, and in particular the use of robust principal component analysis to prune the hyperspectral dataset and reveal subtle difference among species.Department for Environment, Food and Rural AffairsThis is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/JSTARS.2016.256940

Southeast- Summer 2019

https://digitalcommons.lsu.edu/horthints/1054/thumbnail.jp

Florida-Friendly Landscaping™ Guidelines for Community Associations: Considerations for Selecting a Landscape Contractor and Writing an Effective Landscaping Contract

This document is intended to provide guidance to entities such as HOA community associations when developing a contract and hiring a landscape maintenance company to perform contracted service

Southwest- Summer 2021

https://digitalcommons.lsu.edu/horthints/1035/thumbnail.jp