Information, such as forest inventory and growth projection, is necessary to effectively manage natural resources. Important decisions about investment, revenue and sustainability require accurate and timely metrics. Traditionally, forest metrics are estimated by physically sampling and measuring individual trees. This approach is expensive, slow and durative.
I research how to extract and model forest information from remotely-sensed data to reduce cost and time, specifically at the individual tree level. I primarily work with high-resolution lidar and multi-spectral imagery from aircraft platforms. Ultimately, my research integrates to individual tree growth and yield modeling. Corollary to my research are applied methods for generalized statistical object recognition and classification otherwise called machine learning.
Automated methods for orthorectification.
High-accuacy fusion of aerial imagery and lidar point data.
Feature spaces for classifying large, complex datasets.
Sparse Bayesian learning for individual tree recognition and classification.
Multivariate modeling of individual tree crowns.
Spatial-temporal modeling of latent forest metrics.
Individual tree growth and yield modeling.
Remote sensing for the estimation of carbon sequestration.
Massively parallel computing for the efficient classification of large datasets.
LASAPI. A .Net library for reading and writing the ASPRS LAS v1.2.
Lidar Tools 1.0 (coming soon). A extension for ESRI ArcGIS 9.3 that enables users to interact with lidar data housed in a SQL database.
C#, C++, Java, VB, PHP, R/S+, SAS, SPSS, Matlab, HTML, CSS, AS, ArcObjects, MS DAO, MS ADO, MS XML, MS Office, Scripting
Discriminant Classification of Lidar Fusion Data
An ASPRS 2009 Abstract on Lidar Fusion
Zhong, L., Hawkins, T., Holland, K., Gong, P. and Biging, G. In press. Determination of Crop Types Using
Multi‐temporal MODIS Images. California Agriculture X(X):X‐X.
Breunig, Gasser, Holland. 2003. Wisconsin’s Forestry Best Management Practices for Water Quality: The 2002 Statewide BMP Monitoring Report. Wisconsin Department of Natural Resources, Madison, WI.
Holland. 2004. The 2003 BMP Monitoring Report, Wisconsin’s Forestry Best Management Practices for Water Quality. Wisconsin Department of Natural Resources, Madison, WI.
University of California, Berkeley
Environmental Science, Policy and Management
137 Mulford Hall
Mail Stop # 3114
Berkeley, CA 94720-3114
(202) 412-1043
