Ms. Lingling Kong will visit our lab for five months. Her research interests are power electronic technology in Smart Grid, renewable energy power generation, grid connected technology, and GIScience in power system.
Welcome, Lingling!
Ms. Lingling Kong will visit our lab for five months. Her research interests are power electronic technology in Smart Grid, renewable energy power generation, grid connected technology, and GIScience in power system.
Welcome, Lingling!
We welcome Dr. Fenghua Huang (Yango University) and Mr. Xun Luo (Guangxi Teachers Education University) to join us!
Fenghua will be working on projects related to image analysis using deep learning, while Xun will be completing his Master’s degree in agricultural land use management.
Chase Hobbs recently joined our lab as a Charlotte Research Scholar (CRS). Chase will work with Dr. Chen and Yinan from May to July to complete a research project aiming to estimate disease-caused tree mortality using remote sensing imagery.
Welcome aboard, Chase!
This is the link to his webpage.
Two lab members Caroline Narron and Yinan He successfully presented at the AAG (American Association of Geographers) annual meeting at Boston in early April, 2017. The presentations stimulated strong interest in the audience.
Great work, Caroline and Yinan!
A collaborative study assessing the impacts of land cover and seasonal variation on maximum air temperature estimation was recently accepted by the journal Remote Sensing. This project was completed when Dr. Yulin Cai visited LRSEC.
Abstract:
Daily maximum surface air temperature (Tamax) is a crucial factor for understanding the complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.
Donald Rees recently joined LRSEC as a research assistant, with a research emphasis on mapping disease-caused tree mortality with satellite and airborne remote sensing data. Donald obtained his B.S. degree at the University of Minnesota-Twin Cities. He is currently working toward his second B.S. at UNC Charlotte. For details, please refer to his webpage.
Welcome aboard, Donald!
A new chapter entitled “Remote Sensing of Forest Damage by Diseases and Insects” was recently published in the book “Remote Sensing for Sustainability” (by CRC Press).
Chen, G., and R.K. Meentemeyer (2016). Remote Sensing of Forest Damage by Diseases and Insects. In Q. Weng (Ed.), Remote Sensing for Sustainability (pp. 145-162). Boca Raton, Florida: CRC Press, Taylor & Francis Group.
The chapter is a review communication, which (i) summarizes the recent trends of remotely detecting forest disease and insect outbreaks, (ii) investigates the qualifications of remote sensing for studying the topic, (iii) provides a brief review of remote sensing algorithms, and (iv) discusses several remaining challenges.
For more details about the chapter, please refer to: https://www.crcpress.com/Remote-Sensing-for-Sustainability/Weng/p/book/9781498700719.
New research paper was accepted by the Journal of Environmental Management.
Title: Uncertainties in mapping forest carbon in urban ecosystems
Abstract:
Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density – 5.8, 4.6, 2.3, and 1.2 pts/m2, aerial optical NAIP (National Agricultural Imagery Program) imagery – 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7-11.0% of variation) than changing NAIP image resolution (causing 6.2-8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape.
Fig 1. Forest carbon maps derived from four resolutions (1, 5, 10 and 20 m) of NAIP images, four densities of LiDAR point clouds (100%, 80%, 40% and 20% of the original data).