Our research includes the methodological and applied aspects of remote sensing and geographical information science. In recent years, research in our lab includes three main areas: 1) Lidar technology and its applications to forestry, we are working on the Lidar system integration (e.g. mobile Lidar platform) and novel algorithms for extracting forestry parameters; 2) theory and applications of geographical one-class data, where only occurrence data of geographical events are available and there are lack of absence data such as species observation data, landslides, wildfires, public health data and so on; 3) development of global climate data and vegetation map, we are developing new and more accurate high-resolution global climate layers with uncertainty and mapping global vegetation parameters by fusing satellite data and observation measurements. We aim to understand the interaction between changing climate and vegetation dynamics at the continental and global scale with emphasis on spatial data uncertainty.

Current Research Projects:

1) Spatial Uncertainty

It has been estimated that there are more than 2500 million specimens in natural history collections. With the increasing interest in understanding changes in environmental, biological, and cultural resources due to human disturbance and climate change, specimen collections have become ever more important, since they can provide baseline information on the environment and the factors driving change.  Before the advent of geographical information systems (GISs) and global positioning systems (GPSs), occurrence information for most specimens was stored as textual descriptions without explicit geographic coordinates. This is a major obstacle for managing and analyzing specimen data in a GIS. Meanwhile, assessing and recording these uncertainties during the georeferencing process is arguably as important as determining coordinates for the locality, because only with the uncertainty can one determine if the location information is suitable for a particular analysis. In this project, we are interested in developing methods to georeference textual descriptions as well as estimate locality uncertainties. In addition, we are also interested in studying the impact of locality uncertainties and other spatial data uncertainties on environmental modeling and the process of decision making.

2) Object based remote sensing classification methods

Object based image analysis (frequently called OBIA) is an approach to classifying high resolution remotely sensed imagery that is currently seeing increased use due to the proliferation of imagery with small pixel sizes, such as those provided by digitized aerial photographs and the IKONOS and QuickBird satellites. Conventional pixel-based methods, while useful in classifying coarse-scale remotely sensed imagery, are less suitable for classifying high resolution images. Generally, an object based image classifier includes two major steps: first an image is segmented into similar image objects (or segments) and then the objects are classified based on attributes of and interrelations between segmented objects. We are interested in developing methods and theory that will help us better apply OBIA method in classifying high resolution images.

3) Adaptive management of Sierra Nevada forests

The goal of the research proposed here is to learn how to use an adaptive management and monitoring system to understand ecosystem behavior, incorporate stakeholder participation, and inform the implementation of adaptive management for Forest Service lands in the Sierra Nevada of California. This project is a multi-campus collaborated project. My lab focuses on extracting the topography and vegetation information from numerous available existing and remotely sensed geodatasets augmented with field measures acquired using GPS, leaf area meters, and laser rangefinders. We will acquire higher resolution remote sensing images (e.g. IKONOS) and LiDAR data to map in detail the vegetation and topography of the sites, the change of the crown closure, leaf area index, and other vegetation parameters before and after the treatments. These data will improve our understanding of the spatial relationships between canopy and forest characteristics and changes in habitat, fire, and hydrologic responses (e.g. soil moisture and thus stream response). We also plan on scaling up these relationships from the catchment to the forest level using coarser resolution remotely sensed imagery and topography. For more information, please visit: http://snamp.cnr.berkeley.edu.

4) California real-time solar irradiance mapping

The goal of this project is to determine the feasibility of mapping global horizontal (GHI) and direct normal (DNI) irradiances for the entire state of California using a combination of satellite image processing and high-quality, redundant ground solar monitoring experiments. We will use GOES-WEST satellite data, ozone, water vapor, and aerosol optical thickness GIS layers to build the solar model that be used to generate near real time global and directed normal irradiance for the entire state of California. The final products will be distributed on the internet using webGIS technology to ensure timely data delivery and to facilitate the use of solar energy in California.

5) Environmental niche modelling

With the increasing availability of digital ecological data, environmental niche modeling has gained much attention for a range of ecological applications. However, one of the major challenges for the use of niche models is the lack of absence data in ecological observation datasets (e.g. those collected by museums, or on wildlife surveys). In this research, we are interesting developing methods in solving the presence-only data problem in ecological modeling. In addition, we are also developing a freely available and user-friendly software ModEco. that enables researchers and students to fully and easily explore the rapidly increasing wealth of species distribution data. ModEco will consist of four major components: data management and visualization, feature analysis, model training and prediction, and accuracy assessment.

6) Species distribution, climate change, and land use / land cover change

We are interesting in understand the relationship between species distribution, climate change and land use/land cover change at the landscape and continental scales. Currently, my lab is working on two related projects: 1) studying the change of vegetation in California over the past 100 year and its relationship with climate change, and land use and land cover change; and 2) statistically downscaling multiple GCM models output to a finer spatial resolution, which will then be used to study the impact of climate change on species distribution.

7) Critical Zone Observatorie Lidar project

The critical zone is defined as the external terrestrial layer extending from the top of the atmospheric boundary layer down through groundwater. The Critical Zone Observatories (CZOs) provide important platforms for studying the processes occurring in the Critical Zone. More information about the CZO can be found at http://criticalzone.org/. LiDAR (Light Detection and Ranging) is emerging as an active remote sensing technology which has application in ecology, geography, geology, geomorphology, seismology, remote sensing and atmospheric physics. Due to its ability to generate 3-dimensional data with high spatial resolution and accuracy, airborne LiDAR data would significantly advance our research, both within and across the CZOs. In this project, two LiDAR flights per site (totally 6 sites across US) are proposed (snow on/off or leaf on/off). Meanwhile, the ground truth data from each site will also be collected during the flights. High resolution DEM, tree heights, DBH, leaf area index, crown cover, and snow depth will be developed based on both the Lidar and ground truth data. These data will help build a network to advance interdisciplinary studies of Earth surface processes as well as foster collaboration among scientists and engineers from different disciplines. More information about the Lidar related research in our lab (Lidar one Pager).

Research Funding:

Using LIDAR and DOQQs to Map Forest Vegetation for Assessing Wildlife Habitat. PI: Qinghua Guo. 2014-2015. USFS.

Forest3D - an open source platform for Lidar applications in forestry. PI, Qinghua Guo 2014-2016. NSF.

WSC Category 3: Propogating Climate-Driven Changes in Hydrologic Processes and Ecosystem Functions across Extreme Biophysical and Anthropogenic Gradients. co-PI. 2012 - 2015. NSF.

Vulnerability of Giant Sequoia to Moisture Stress in a Changing Climate. PI. 2011 - 2013. National Park Service.

Pwning Asthma Triggers: Health Games as Technologies of Social Engagement. co-PI. 2011-2012. CITRIS

Doctoral Dissertation Research: The Development and Integration of Spatial Analyses for Search and Rescue Operations in Yosemite National Park. PI. 2010 2012. NSF.

Sierra Nevada Research Institute Informatics and Data Visualization Center in Yosemite National Park. Co-PI. 2010 2012. NSF.

Sierra Nevada Adaptive Management and Monitoring. PI of UCM spatial team. 2009 2014. USFS/DWR.

Can mammals mediate climatically-induced vegetation transitions in alpine ecosystems of the western United States. UCM PI. 2010 2013. USGS.

MRI: Development of ASSIST: Affordable System for Solar Irrdiance and Tracking. Co-PI. 2009 2012. NSF.

Acquiring airborne LiDAR data to study hydrologic, geomorphologic, and geochemical processes at three Critical Zone Observatories (CZOs). PI 2009 2013. NSF.

WATERS Network: Observing and Predicting Freshwater Eutrophication-Algal Bloom Dynamics Using Local Hyperspectral Imaging. Co-PI. 2009-2012. NSF.

Learning from Presence-Only Data, with Application in Cyber Security and Ecology. Co-PI. 2009-2011. The UC Lab Research Program.

ModelEco: an Integrated Software for Species Data Analysis and Modeling. PI. 2008 2010. NSF.

Mapping California Solar Irradiance and its Implications for Power Sector, California Energy Institute. PI. 2008 2009. California Energy Insitute.

The Solar Irradiance Mapping Initiative (SIMI), 2007-2008. Co-PI, The Center for Information Technology Research (CITRIS).

Sierra Nevada Adaptive Management and Monitoring, PI of UCM spatial team. 2007 2008. USFS/DWR.

Development of Historical Ecological Web Databases. PI. 2007 - 2008. USDA.

Statistically Downscaling General Circulation Model Products. PI. 2007 2008. California Academy of Science.

Sierra Nevada Adaptive Management and Monitoring. Co-PI. 2006 2007. USFS

Modelling of a New Invasive Forest Disease: Refining Models Using Remote Sensing Products and Nested Models. PI. 2006 - 2007. Graduate and Research Council (GRC) Faculty Research Grants, University of California at Merced.

Faculty Development Awards. PI. 2006-2007. University of California at Merced.

Biogeomancer: Enhancing Diversity Science Through Efficient and Effective Georeferencing. UCM PI. 2005 2007. The Gordon & Betty Moore Foundation.

Sierra Nevada Adaptive Management. Co-PI. 2005 2006. USFS/USDA.

Integrated Measurement and Modeling of Sierra Nevada Water Budgets. Co-PI. 2006 2008. Lawrence Livermore National Laboratory.

Development of a Water-Balance Instrument Cluster for Mountain Hydrology, Biochemistry and Ecosystem Science. Co-PI. 2006 2009. NSF.