A5015848806- Remote Sensing in Agriculture
- Land Use and Ecosystem Services
- Remote Sensing and LiDAR Applications
- Remote-Sensing Image Classification
- Remote Sensing and Land Use
- Urban Heat Island Mitigation
- Forest ecology and management
- Urban Green Space and Health
- Advanced Image Fusion Techniques
- Impact of Light on Environment and Health
- Geochemistry and Geologic Mapping
- Conservation, Biodiversity, and Resource Management
- Species Distribution and Climate Change
- Soil erosion and sediment transport
- Plant Water Relations and Carbon Dynamics
- Forest Ecology and Biodiversity Studies
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Aeolian processes and effects
- Environmental Changes in China
- Fire effects on ecosystems
- Forest Management and Policy
- Soil Moisture and Remote Sensing
- Hydrology and Watershed Management Studies
- Ecology and Vegetation Dynamics Studies
- African Botany and Ecology Studies
Fujian Normal University
2018-2025
Zhejiang A & F University
2013-2020
Michigan State University
2012-2018
University of Michigan
2016
Indiana University Bloomington
2005-2015
Auburn University
2007-2009
Indiana University
2002
Indiana State University
2002
Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis placed on the summarization major advanced approaches techniques used for improving accuracy. In addition, some important issues affecting performance are discussed. literature review suggests designing suitable image‐processing procedure prerequisite successful remotely sensed data into thematic map. Effective...
Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships interactions between human natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used recent decades. Many techniques have been developed. This paper summarizes reviews these techniques. Previous literature has shown that image differencing, principal component analysis post-classification comparison the most...
Remotely sensed data have become the primary source for biomass estimation. A summary of previous research on remote sensing‐based estimation approaches and a discussion existing issues influencing are valuable further improving performance. The literature review has demonstrated that remains challenging task, especially in those study areas with complex forest stand structures environmental conditions. Either optical sensor or radar more suitable sites relatively simple structure than...
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...
The complicated forest stand structure and associated abundant tree species in the Amazon often induce difficulty estimating aboveground biomass (AGB) using remotely sensed data. This paper explores AGB estimation Landsat Thematic Mapper (TM) data eastern western Brazilian Amazon, discusses impacts of on estimation. Estimating is still a challenging task, especially for sites with biophysical environments. TM spectral responses are more suitable relatively simple than structure. Conversely,...
Abstract This article discusses research in which the authors applied Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to maping of soil erosion risk Brazilian Amazonia. map survey data were used develop erodibility factor ( K ), a digital elevation model image was generate topographic LS ). The cover‐management C ) developed based on vegetation, shade, fraction images derived from spectral mixture analysis Landsat Enhanced Thematic...
This paper examines characteristics of urban land-use and land-cover (LULC) classes using spectral mixture analysis (SMA), develops a conceptual model for characterizing LULC patterns. A Landsat Enhanced Thematic Mapper Plus (ETM+) image Indianapolis City was used in this research minimum noise fraction (MNF) transform employed to convert the ETM+ into principal components. Five endmembers (shade, green vegetation, impervious surface, dry soil, dark soil) were selected, an unconstrained...
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...
The data saturation problem in Landsat imagery is well recognized and regarded as an important factor resulting inaccurate forest aboveground biomass (AGB) estimation. However, no study has examined the values for different vegetation types such coniferous broadleaf forests. objective of this to estimate a subtropical region explore approaches improving AGB Thematic Mapper imagery, digital elevation model data, field measurements Zhejiang province Eastern China were used. Correlation...
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...
The global availability of high spatial resolution images makes mapping tree species distribution possible for better management forest resources. Previous research mainly focused on single species, but information about the all kinds trees, especially plantations, is often required. This aims to identify suitable variables and algorithms classifying land cover, forest, species. Bi-temporal ZiYuan-3 multispectral stereo were used. Spectral responses textures from imagery, canopy height...
Cropland redistribution to marginal land has been reported worldwide; however, the resulting impacts on environmental sustainability have not investigated sufficiently. Here we of cropland in China. As a result urbanization-induced loss high-quality croplands south China (∼8.5 t ha
Atmospheric correction is an important preprocessing step required in many remote sensing applications. The authors are engaged the project 'Human Dimensions of Amazonia: Forest Regeneration and Landscape Structure' NASA/INPE's Large Scale Biosphere-Atmosphere Experiment Amazonia (LBA) programme. This requires use corrected Landsat TM data since research foci integrate ground-based to: (1) measure model biomass; (2) classify multiple stages secondary succession; (3) land cover/land changes;...
High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within same cover, confusion among different covers, and shadow problem often lead to poor classification performance based on traditional per-pixel spectral-based methods. This paper explores approaches improve cover with Quickbird imagery. Traditional supervised incorporation of textural multispectral images, spectral-spatial classifier, segmentation-based are...
This paper compares different image processing routines to identify suitable remote sensing variables for urban classifi- cation in the Marion County, Indiana, USA, using a Landsat 7 Enhanced Thematic Mapper Plus (ETM� ) image. The ETMmultispectral, panchromatic, and thermal images are used. Incorporation of spectral signature, texture, surface temperature is examined, as well data fusion techniques combining higher spatial resolution with lower multispectral images. Results indicate that...