Geography

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    Evaluating the Impact of the 2020 Iowa Derecho on Corn and Soybean Fields Using Synthetic Aperture Radar
    (MDPI, 2020-11-26) Hosseini, Mehdi; Kerner, Hannah R.; Sahajpal, Ritvik; Puricelli, Estefania; Lu, Yu-Hsiang; Lawal, Afolarin Fahd; Humber, Michael L.; Mitkish, Mary; Meyer, Seth; Becker-Reshef, Inbal
    On 10 August 2020, a series of intense and fast-moving windstorms known as a derecho caused widespread damage across Iowa’s (the top US corn-producing state) agricultural regions. This severe weather event bent and flattened crops over approximately one-third of the state. Immediate evaluation of the disaster’s impact on agricultural lands, including maps of crop damage, was critical to enabling a rapid response by government agencies, insurance companies, and the agricultural supply chain. Given the very large area impacted by the disaster, satellite imagery stands out as the most efficient means of estimating the disaster impact. In this study, we used time-series of Sentinel-1 data to detect the impacted fields. We developed an in-season crop type map using Harmonized Landsat and Sentinel-2 data to assess the impact on important commodity crops. We intersected a SAR-based damage map with an in-season crop type map to create damaged area maps for corn and soybean fields. In total, we identified 2.59 million acres as damaged by the derecho, consisting of 1.99 million acres of corn and 0.6 million acres of soybean fields. Also, we categorized the impacted fields to three classes of mild impacts, medium impacts and high impacts. In total, 1.087 million acres of corn and 0.206 million acres of soybean were categorized as high impacted fields.
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    ASSESSING FOREST BIOMASS AND MONITORING CHANGES FROM DISTURBANCE AND RECOVERY WITH LIDAR AND SAR
    (2015) Huang, Wenli; Dubayah, Ralph; Sun, Guoqing; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This dissertation research investigated LiDAR and SAR remote sensing for assessing aboveground biomass and monitoring changes from anthropogenic forest disturbance and post-disturbance recovery. First, waveform LiDAR data were applied to map forest biomass and its changes at different key map scales for the two study sites: Howland Forest and Penobscot Experimental Forest. Results indicated that the prediction model at the scale of individual LVIS footprints is reliable when the geolocation errors are minimized. The evaluation showed that the predictions were improved markedly (20% R2 and 10% RMSE) with the increase of plot sizes from 0.25 ha to 1.0 ha. The effect of disturbance on the prediction model was strong at the footprint level but weak at the 1.0 ha plot-level. Errors reached minimum when footprint coverage approached about 50% of the area of 1.0 ha plots (16 footprints) with no improvement beyond that. Then, a sensitivity analysis was conducted for multi-source L-band SAR signatures, to change in forest biomass and related factors such as incidence angle, soil moisture, and disturbance type. The effect of incidence angle on SAR backscatter was reduced by an empirical model. A cross-image normalization was used to reduce the radiometric distortions due to changes in acquisition conditions such as soil moisture. Results demonstrated that the normalization ensured that the derived biomass of regrowth forests was cross-calibrated, and thus made the detection of biomass change possible. Further, the forest biomass was mapped for 1989, 1994 and 2009 using multi-source SAR data, and changes in biomass were derived for a 15- and a 20-year period. Results improved our understanding of issues concerning the mapping of biomass dynamic using L-ban SAR data. With the increase of plot sizes, the speckle noise and geolocations errors were reduced. Multivariable models were found to outperform the single-term models developed for biomass estimation. The main contribution of this research was an improved knowledge concerning waveform LiDAR and L-band SAR’s ability in monitoring the changes in biomass in a temperate forest. Results from this study provide calibration and validation methods as a foundation for improving the performance of current and future spaceborne systems.
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    RADAR MONITORING OF HYDROLOGY IN MARYLAND'S FORESTED COASTAL PLAIN WETLANDS: IMPLICATIONS FOR PREDICTED CLIMATE CHANGE AND IMPROVED MAPPING
    (2005-08-05) Weiner Lang, Megan; Kasischke, Eric; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Wetlands provide important services to society but Mid-Atlantic wetlands are at high risk for loss, with forested wetlands being especially vulnerable. Hydrology (flooding and soil moisture) controls wetland function and extent but it may be altered due to changes in climate and anthropogenic influence. Wetland hydrology must better understood in order to predict and mitigate the impact of these changes. Broad-scale forested wetland hydrology is difficult to monitor using ground-based and traditional remote sensing methods. C-band synthetic aperture radar (SAR) data could improve the capability to monitor forested wetland hydrology but the abilities and limitations of these data need further investigation. This study examined: 1) the link between climate and wetland hydrology; 2) the ability of ENVISAT SAR (C-HH and C-VV) data to monitor inundation and soil moisture in forested wetlands; 3) limitations inherent to C-band data (incidence angle, polarization, and phenology) when monitoring forested wetland hydrology; and 4) the accuracy of forested wetland maps produced using SAR data. The study was primarily conducted near the Patuxent River in Maryland but the influence of incidence angle was considered along the Roanoke River in North Carolina. This study showed: 1) climate was highly correlated with wetland inundation; 2) significant differences in C-VV and C-HH backscatter existed between forested areas of varying hydrology (uplands and wetlands) throughout the year; 3) C-HH backscatter was better correlated to hydrology than C-VV backscatter; 4) correlations were stronger during the leaf-off season; 5) the difference in backscatter between flooded and non-flooded areas did not sharply decline with incidence angle, as predicted; and 6) maps produced using SAR data had relatively high accuracy levels. Based on these findings, I concluded that hydrology is influenced by climate at the study site, and C-HH data should be able to monitor changes in hydrology throughout the year. Larger incidence angles should be explored when using C-HH data to monitor forested wetland hydrology, and C-band SAR has the potential to increase the ability to map forested wetlands throughout the year. The methods developed have the potential to fill the need of managers for increased hydrologic information and improved forested wetland maps.