A5003673355- Remote Sensing and LiDAR Applications
- Remote Sensing in Agriculture
- Forest ecology and management
- 3D Surveying and Cultural Heritage
- Remote Sensing and Land Use
- Forest Management and Policy
- Forest Ecology and Biodiversity Studies
- Species Distribution and Climate Change
- Mobile Learning in Education
- Urban Heat Island Mitigation
- Urban Green Space and Health
- Land Use and Ecosystem Services
- Landslides and related hazards
- Image Processing and 3D Reconstruction
- Remote-Sensing Image Classification
- Forest, Soil, and Plant Ecology in China
- Leaf Properties and Growth Measurement
- Water Quality Monitoring Technologies
- Microplastics and Plastic Pollution
- Geological Modeling and Analysis
- Soil Moisture and Remote Sensing
- Global Trade and Competitiveness
- Superconducting Materials and Applications
- Ecology and Vegetation Dynamics Studies
- Advanced Decision-Making Techniques
University of Hawaiʻi at Mānoa
2014-2024
Wenzhou University
2024
Zhejiang A & F University
2015-2023
Beijing Forestry University
2020
University of Hawaii System
2015-2019
Nanjing Forestry University
2010-2014
Industrial and Commercial Bank of China
2013
State Key Laboratory of Building Safety and Built Environment
2012
Hunan Women'S University
2011
Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted the past three decades. This paper provides a survey current using remote sensing data discusses four critical issues – collection field-based reference data, extraction selection suitable variables from identification proper algorithms to develop models, uncertainty analysis refine procedure. Additionally, we discuss impacts...
CORRESPONDING AUTHOR: Thomas W. Giambelluca, Department of Geography, University Hawai‘i at Manoa, 2424 Maile Way, Honolulu, HI 96822, E-mail: thomas@hawaii.edu
Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR offered an important new structural stream for forest biomass estimations. On other hand, uncertainty analysis research only obtained sufficient attention due to difficulty in collecting reference data. This paper provides a brief overview of current estimation methods using both TM A case study is then presented that demonstrates analysis. Results indicate can provide adequate estimates secondary...
Remote sensing–based forest aboveground biomass (AGB) estimation has been extensively explored in the past three decades, but how to effectively combine different sensor data and modeling algorithms is still poorly understood. This research conducted a comparative analysis of datasets (e.g., Landsat Thematic Mapper (TM), ALOS PALSAR L-band data, their combinations) artificial neural network (ANN), support vector regression (SVR), Random Forest (RF), k-nearest neighbor (kNN), linear (LR)) for...
This study proposes a new metric called canopy geometric volume G, which is derived from small-footprint lidar data, for estimating individual-tree basal area and stem volume. Based on the plant allometry relationship, we found that B exponentially related to G (B �� 1G 3⁄4 , where � 1 constant) V proportional (V 2G, 2 constant). The models based these relationships were compared with number of tree height and/or crown diameter. tested over individual trees in deciduous oak woodland...
Urban planning and management need accurate three-dimensional (3D) data such as light detection ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up to millimeter-level accuracy density of a few thousand points/m2, have gained increasing attention in urban applications. Substantial research has been conducted the past decade. This paper comprehensive survey applications key techniques based on MLS We first introduce characteristics systems corresponding clouds, present...
Leaf area index (LAI) is a significant biophysical variable in the models of hydrology, climatology and crop growth. Rapid monitoring LAI critical modern precision agriculture. Remote sensing (RS) on satellite, aerial unmanned vehicles (UAVs) has become popular technique LAI. Among them, UAVs are highly attractive to researchers agriculturists. However, some vegetation (VI)—derived have relatively low accuracy because limited number multispectral bands, especially as they tend saturate at...
Tropical forests are major repositories of biodiversity, but fast disappearing as land is converted to agriculture. Decision-makers need know which the remaining prioritize for conservation, only spatial information on forest biodiversity has, until recently, come from a sparse network ground-based plots. Here we explore whether airborne hyperspectral imagery can be used predict alpha diversity upper canopy trees in West African forest. The abundance tree species were collected 64 plots...
Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one the most important topics for electric management. However, this is very challenging over complex urban areas, where are in close proximity to buildings trees. In paper, we presented a new, semi-automated versatile framework that consists four steps: (i) line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, (iv) SVM classification....
Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered premier instrument estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial temporal coverage. The previous JERS ALOS SAR satellites were extensively employed model forest literature suggesting signal saturation at low-moderate biomass values, an influence plot size on estimates accuracy. ALOS2 continuity mission since May...
Previous research has explored the potential to integrate lidar and optical data in aboveground biomass (AGB) estimation, but how different sources, vegetation types, modeling algorithms influence AGB estimation is poorly understood. This conducts a comparative analysis of sources approaches improving estimation. RapidEye-based spectral responses textures, lidar-derived metrics, their combination were used develop models. The results indicated that (1) overall, RapidEye are not suitable for...
Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology mapping aboveground biomass (AGB) over landscape levels. However, little research in past been done to study AGB of agroforestry systems using airborne data. Focusing on an system Brazilian Amazon, this first predicted plot-level fixed-effects regression models that assumed...
Model-based inference is an alternative to probability-based for small areas or remote which probability sampling difficult. mean square error estimators incorporate three components: prediction covariance, residual variance, and covariance. The latter two components are often considered negligible, particularly large areas, but no thresholds that justify ignoring them have been reported. objectives of the study were threefold: (i) compare analytical bootstrap model parameter covariances as...