On Friday, January 15, Dr. Chen was invited by the Department of Geography at the University of South Carolina to give a talk, entitled “Multisensor remote sensing for sustainable forest management”. The talk is also part of the Geography Colloquium at USC. Please see details here: http://artsandsciences.sc.edu/geog/geography-colloquium-dr-gang-chen.
Dr. Chen's Research Featured in Media - Mongabay
Dr. Chen’s research of the impact of building and operating the Tucuruí dam on deforestation in Amazon was featured in Mongabay, an environmental science and conservation news and information site. Here is the link to the report, titled “Forest loss increased annually for 25 years at oldest Amazon mega-dam”:
http://news.mongabay.com/2016/01/forest-loss-increased-annually-for-25-years-at-oldest-amazon-mega-dam/
Dr. Chen elected as Secretary/Treasurer of the ASPRS Potomac Region
Dr. Chen was recently elected as the Secretary/Treasurer of the ASPRS (American Society of Photogrammetry and Remote Sensing) Potomac Region. The Secretary/Treasurer serves for four years, progressing to Vice-President, President and Past-President.
See link for more details: http://asprspotomac.org/region-election-results-brown-region-director-chen-secretarytreasurer.
Dr. Ping Wang joins LRSEC
Dr. Ping Wang is visiting the LRSEC for a period of six months starting from December, 2015. Ping is currently a full professor at Shandong University of Science and Technology in China. Her main research interest is the application of remote sensing to monitor the change of our landscape, with an emphasis in the regions severely affected by mining.
Welcome aboard, Ping!
Ozelkan’s paper accepted by International Journal of Digital Earth
A recent submission by Drs. Emre Ozelkan and Gang Chen has been accepted by International Journal of Digital Earth. Congratulations, Emre!
Title: Spatial Estimation of Wind Speed: A New Integrative Model Using Inverse Distance Weighting and Power Law
Abstract:
Spatial interpolation (SI) is currently one of the most common ways to estimate wind speed (Ws). However, classic SI models either ignore the complex geography (e.g., inverse distance weighting (IDW)), or demand high computational resources (e.g., cokriging). This study aimed to develop a simple yet effective SI model for estimating Ws in Eastern Thrace of Turkey. This new method, named MIDW(Ws), is a modified IDW through the integration of IDW with wind profile model, power law (PL), representing the influence of land cover and topography on Ws. Terrain features and elevation data of PL were obtained using normalized difference vegetation index (NDVI) and digital elevation model (DEM), respectively. Results showed superior and comparable performance of MIDW(Ws) to standard IDW and ordinary kriging (OK) across all months of the tested year. Compared to ordinary cokriging (OCK) using DEM as covariate, MIDW(Ws) generated better results in the arid–semiarid seasons (around summer). Local complex atmospheric conditions during rainy seasons (around winter) may have affected the performance of incorporating PL with MIDW(Ws). Generally, the proposed MIDW(Ws) is simpler and easier to implement compared to OCK. For landscape–scale projects, its high computational efficiency and relatively robust performance show potential to deal with large volumes of data sets.
Ozelkan's paper accepted by JAG
A recent submission by Drs. Emre Ozelkan and Gang Chen has been accepted by the International Journal of Applied Earth Observation and Geoinformation. Congratulations, Emre!
Title: Multiscale object-based drought monitoring and comparison in rainfed and irrigated agriculture from Landsat 8 OLI imagery
Abstract:
Drought is a rapidly rising environmental issue that can cause hardly repaired or unrepaired damages to the nature and socio-economy. This is especially true for a region that features arid/semi-arid climate, including the Turkey’s most important agricultural district – Southeast Anatolia. In this area, we examined the uncertainties of applying Landsat 8 Operational Land Imager (OLI) NDVI data to estimate meteorological drought – Standardized Precipitation Index (SPI) – measured from 31 in-situ agro-meteorological monitoring stations during spring and summer of 2013 and 2014. Our analysis was designed to address two important, yet under-examined questions: (i) how does the co-existence of rainfed and irrigated agriculture affect remote sensing drought monitoring in an arid/semi-arid region? (ii) What is the role of spatial scale in drought monitoring using a GEOBIA (geographic object-based image analysis) framework? Results show that spatial scale exerted a higher impact on drought monitoring especially in the drier year 2013, during which small scales were found to outperform large scales in general. In addition, consideration of irrigated and rainfed areas separately ensured a better performance in drought analysis. Compared to the positive correlations between SPI and NDVI over the rainfed areas, negative correlations were determined over the irrigated agricultural areas. Finally, the time lag effect was evident in the study, i.e., strong correlations between spring SPI and summer NDVI in both 2013 and 2014. This reflects the fact that spring watering is crucial for the growth and yield of the major crops (i.e., winter wheat, barley and lentil) cultivated in the region.
Jaime Lopez Joins the lab
Jaime will be visiting the LRSEC for one year starting from August, 2015. He is an assistant professor at the University of Tolima (Colombia), and is working towards his PhD degree at the National University of Colombia. For more details, please see his personal page on our website.
Welcome aboard, Jaime!
Paper ranked 3rd in citation by International Journal of Remote Sensing
Dr. Chen was informed by Dr. Tim Warner, Editor in Chief of IJRS, that his 2012 “Object-based change detection” paper is the third-most cited paper published in the last 5 years in IJRS (and bear in mind that it has only had 3 years out of that 5 year period to gather citations).
Amazon dam-forest paper published in Applied Geography
To understand the “Spatiotemporal patterns of tropical deforestation and forest
degradation in response to the operation of the Tucuruí hydroelectric dam in the Amazon basin“, Dr. Chen and his collaborators from Yale University, Federal University of Lavras (Brazil), and Wageningen University and Research Centre (The Netherlands) published their results in the journal of Applied Geography.
Abstract:
The planned construction of hundreds of hydroelectric dams in the Amazon basin has the potential to provide invaluable ‘clean’ energy resources for aiding in securing future regional energy needs and continued economic growth. These mega-structures, however, directly and indirectly interfere with natural ecosystem dynamics, and can cause noticeable tree loss. To improve our understanding of how hydroelectric dams affect the surrounding spatiotemporal patterns of forest disturbances, this case study integrated remote sensing spectral mixture analysis, GIS proximity analysis and statistical hypothesis testing to extract and evaluate spatially-explicit patterns of deforestation (clearing of entire forest patch) and forest degradation (reduced tree density) in the 80,000 km2 neighborhoods of the Brazil’s Tucuruí Dam, the first large-scale hydroelectric project in the Amazon region, over a period of 25 years from 1988 to 2013. Results show that the average rates of deforestation were consistent during the first three time periods 1988-1995 (620 km2 per year), 1995-2001 (591 km2 per year), and 2001-2008 (660 km2 per year). However, such rate dramatically fell to half of historical levels after 2008, possibly reflecting the 2008 global economic crisis and enforcement of the Brazilian Law of Environmental Crimes. The rate of forest degradation was relatively stable from 1988 to 2013 and, on average, was 17.8% of the rate of deforestation. Deforestation and forest degradation were found to follow similar spatial patterns across the dam neighborhoods, upstream reaches or downstream reaches at the distances of 5 km to 80 km, suggesting that small and large-scale forest disturbances may have been influencing each other in the vicinity of the dam. We further found that the neighborhoods of the Tucuruí Dam and the upstream region experienced similar degrees of canopy loss. Such loss was mainly attributed to the fast expansion of the Tucuruí town, and the intensive logging activities alongside major roads in the upstream reservoir region. In contrast, a significantly lower level of forest disturbance was discovered in the downstream region.
Dr. Chen gives an invited talk at North Carolina Museum of Natural Sciences
On June 6, 2015, Dr. Gang Chen was invited by North Carolina Museum of Natural Sciences in Raleigh to give a talk titled “Counting Carbon in Trees from Space”. This is part of the LRSEC’s outreach activities, with an aim to help the general public, including K-12 students, understand how the cutting-edge remote sensing technologies can assist in more accurate forest carbon accounting in both urban and natural environments.
Link: http://naturalsciences.org/programs-events/science-saturday-counting-carbon-trees-space