Publications related to Land Cover - Land Use Change | Geographical Sciences Department

Thieme, A., Yadav, S., Oddo, P. C., Fitz, J. M., McCartney, S., King, L., ... & Hively, W. D. (2020). Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed. Remote Sensing of Environment, 248, 111943. 1-s2.0-S0034425720303138-main.pdf2.55 MB
Giglio, L., and Roy, D. P., 2020, On the outstanding need for a long-term, multi-decadal, validated and quality assessed record of global burned area: Caution in the use of Advanced Very High Resolution Radiometer data. Science of Remote Sensing, 2, 100007. Giglio2020_SRS.pdf2.7 MB
Ma, L., Hurtt, G. C., Chini, L. P., Sahajpal, R., Pongratz, J., Frolking, S., Stehfest, E., Klein Goldewijk, K., O'Leary, D., and Doelman, J. C.: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2Geosci. Model Dev., 13, 3203–3220, https://doi.org/10.5194/gmd-13-3203-2020, 2020. gmd-13-3203-2020_0.pdf6.39 MB
Huang, Y., Z. Tian, Q. Ke, J. Liu, M. Irannezhad, D. Fan, M. Hou, L. Sun. 2020. “Nature-Based Solutions for Urban Pluvial Flood Risk Management.” WIREs Water (2018 IF: 4.436), 7(3), Cover Paper, e1421, pp. 1-17. WIREsWater cover May 2020.pdf17.6 MB
Pickens, A.H., Hansen, M.C., Hancher, M., Stehman, S.V., Tyukavina, A., Potapov, P., Marroquin, B., Sherani, Z., 2020. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sensing of Environment 243, 111792. https://doi.org/10.1016/j.rse.2020.111792 globalWaterPickens.pdf3.84 MB
Stubbs, Q., Yeo, I.-Y., Lang, M., Townshend, J., Sun, L., Prestegaard, K., Jantz, C., 2020. Assessment of wetland change on the Delmarva Peninsula from 1984 to 2010. Journal of Coastal Research (IF = 1.053), vol. 36, no. 3, pp. 575–589.DOI: 10.2112/JCOASTRES-D-16-00038.1.
Tian, Z., H. Xu, L. Sun, D. Fan, G. Fischer, H. Zhong, P. Zhang, E. Pope, C. Kent, W. Wu. 2020. “Using a cross-scale simulation tool to assess future maize production under multiple climate change scenarios: An application to the Northeast Farming Region of China.” Climate Services. Vol. 18, article no. 100150, pp. 1-9. https://doi.org/10.1016/j.cliser.2020.100150. Using a cross-scale simulation tool to assess future maize production under multiple climate change scenarios in Northeast China_ClimateServices2020.pdf1.91 MB
Fan, D., H. Zhong, B. Hu, Z. Tian, L. Sun, G. Fischer, X. Wang, Z. Jiang. 2020. “Agro-Ecological Suitability Assessment of Chinese Medicinal Yam under Future Climate Change.” Environmental Geochemistry and Health (2019 IF: 3.472). 42 (3), 987-1000,Open-access publication on 15 Oct 2019. https://doi.org/10.1007/s10653-019-00437-w. Agro-Ecological Suitability Assessment of Chinese Medicinal Yam under Future Climate Change_EGH2020.pdf1.88 MB
Peredo, J., Wayman, C., Whong, B., Thieme, A., Kline, L. R., Yadav, S., ... & Fitz, J. (2020). Utilizing Landsat and SENTINEL-2 to Remotely Monitor and Evaluate the Performance of Winter Cover Crops Throughout Maryland. The International Archives ofPhotogrammetry, Remote Sensing and Spatial Information Sciences, 42, 125-130. UTILIZING_LANDSAT_AND_SENTINEL-2_TO_REMOTELY_MONIT.pdf886.67 KB
He J, Loboda TV, Jenkins L, Chen D. (2020). ABoVE: Distribution Maps of Wildland Fire Fuel Components across Alaskan Tundra, 2015. ORNL DAAC, Oak Ridge, Tennessee, USA.
Peter Potapov, Matthew C Hansen, Indrani Kommareddy, Anil Kommareddy, Svetlana Turubanova, Amy Pickens, Bernard Adusei, Alexandra Tyukavina, Qing Ying, 2020. Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping. Remote Sensing12(3)
Shen W, Mao X, He J, Dong J, Huang C, Li M. (2020) Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China. Remote Sens. 2020, 12, 155.
Liu, X., Pei, S., Wang, S., Wen, Y., Li, X., Wu, J., Chen, J., Feng, K., Liu, J., Hubacek, K., Davis, S., Yu, L., Liu, Z., Wu, C., Cai, Y., Yuan, W., (2019), Global urban expansion offsets climate-driven increases in terrestrial net primary productivity,
Chen, D., Loboda, T.V., & Hall, J.V. (2020). A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems. Isprs Journal of Photogrammetry and Remote Sensing, 159, 63-77 https://doi.org/10.1016/j.isprsjprs.2019.11.011
Skakun S., Justice C., Kussul N., Shelestov A., Lavreniuk M. (2019) Satellite Data Reveal Cropland Losses in South-Eastern Ukraine Under Military Conflict. Frontiers in Earth Science, 7:305.
Li Wang, Wensheng Duan, Bo Yu, Qing Ying, Hanbing Yang, Yahui Lei, 2019. Exploring the Response Mechanism of Remote Sensing Images in Monitoring Fixed Assets Investment Project in Terms of Building Detection. IEEE Access 7:167919 - 167929
Hoffman-Hall, A., Loboda, T. V., Hall, J. V., Carroll, M. L., & Chen, D. (2019). Mapping remote rural settlements at 30 m spatial resolution using geospatial data-fusion. Remote Sensing of Environment, 233, 111386.
Hu, H., Z. Tian, L. Sun, J. Wen, Z. Liang, G. Dong, J. Liu. 2019. “Synthesized trade-off analysis of flood control solutions under future deep uncertainty: An application to the central business district of Shanghai.” Water Research (2018 IF: 7.913; 5 yr IF: 8.424), vol. 166, article no. 115067. https://doi.org/10.1016/j.watres.2019.115067. Synthesized trade-off analysis of flood control solutions under future deep uncertainty_WaterResearch_Sep2019.pdf2.42 MB
Ying, Q., M. C. Hansen, L. Sun, L. Wang, M. Steininger. 2019. “Satellite-detected gain in built-up area as a leading economic indicator.” Environmental Research Letters (2018 IF: 6.192). vol. 14, article no. 114015.https://doi.org/10.1088/1748-9326/ab443e. Satellite-detected gain in built-up area as a leading economic indicator_EnvResearchLetters2019.pdf1.05 MB
Jie Pei, Li Wang, Xiaoyue Wang, Zheng Niu, Maggi Kelly, Xiao-Peng Song, Ni Huang, Jing Geng, Haifeng Tian, Yang Yu, Shiguang Xu, Lei Wang, Qing Ying, Jianhua Cao, 2019. Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016. Remote Sensing 11(17):2044
Skakun, S.; Vermote, E.; Franch, B.; Roger, J.-C.; Kussul, N.; Ju, J.; Masek, J. (2019) Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models.Remote Sensing, 11(15), 1768
Xianghong Che, Min Feng, Joe Sexton, Saurabh Channan, Qing Sun, Qing Ying, Jiping Liu, Yong Wang, 2019. Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000–2015). Remote Sensing 11(11).
Note: Dr.Vadrevu's research was previously published as "V.K.Prasad" and "V.Krishna Prasad". Asterisk indicates student researcher.
Vadrevu,KP,Lasko,K, Giglio,L, Schroeder,W, Biswas,S., and Justice,C. 2019. Trends in Vegetation fires in South and Southeast Asian Countries.NatureScientificReports 9:7422
Franch, B., Vermote, E. F., Skakun, S., Roger, J. C., Becker-Reshef, I., Murphy, E., & Justice, C. (2019). Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine. International Journal of Applied Earth Observation and Geoinformation, 76, 112-127.