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    Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data
    (MDPI, 2018-06-14) Liu, Yanan; Gong, Weishu; Hu, Xiangyun; Gong, Jianya
    Carbon sink estimation and ecological assessment of forests require accurate forest type mapping. The traditional survey method is time consuming and labor intensive, and the remote sensing method with high-resolution, multi-spectral commercial satellite images has high cost and low availability. In this study, we explore and evaluate the potential of freely-available multi-source imagery to identify forest types with an object-based random forest algorithm. These datasets included Sentinel-2A (S2), Sentinel-1A (S1) in dual polarization, one-arc-second Shuttle Radar Topographic Mission Digital Elevation (DEM) and multi-temporal Landsat-8 images (L8). We tested seven different sets of explanatory variables for classifying eight forest types in Wuhan, China. The results indicate that single-sensor (S2) or single-day data (L8) cannot obtain satisfactory results; the overall accuracy was 54.31% and 50.00%, respectively. Compared with the classification using only Sentinel-2 data, the overall accuracy increased by approximately 15.23% and 22.51%, respectively, by adding DEM and multi-temporal Landsat-8 imagery. The highest accuracy (82.78%) was achieved with fused imagery, the terrain and multi-temporal data contributing the most to forest type identification. These encouraging results demonstrate that freely-accessible multi-source remotely-sensed data have tremendous potential in forest type identification, which can effectively support monitoring and management of forest ecological resources at regional or global scales.
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    CHARACTERIZING RICE RESIDUE BURNING AND ASSOCIATED EMISSIONS IN VIETNAM USING A REMOTE SENSING AND FIELD-BASED APPROACH
    (2018) Lasko, Kristofer; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Agricultural residue burning, practiced in croplands throughout the world, adversely impacts public health and regional air quality. Monitoring and quantifying agricultural residue burning with remote sensing alone is difficult due to lack of field data, hazy conditions obstructing satellite remote sensing imagery, small field sizes, and active field management. This dissertation highlights the uncertainties, discrepancies, and underestimation of agricultural residue burning emissions in a small-holder agriculturalist region, while also developing methods for improved bottom-up quantification of residue burning and associated emissions impacts, by employing a field and remote sensing-based approach. The underestimation in biomass burning emissions from rice residue, the fibrous plant material left in the field after harvest and subjected to burning, represents the starting point for this research, which is conducted in a small-holder agricultural landscape of Vietnam. This dissertation quantifies improved bottom-up air pollution emissions estimates through refinements to each component of the fine-particulate matter emissions equation, including the use of synthetic aperture radar timeseries to explore rice land area variation between different datasets and for date of burn estimates, development of a new field method to estimate both rice straw and stubble biomass, and also improvements to emissions quantification through the use of burning practice specific emission factors and combustion factors. Moreover, the relative contribution of residue burning emissions to combustion sources was quantified, demonstrating emissions are higher than previously estimated, increasing the importance for mitigation. The dissertation further explored air pollution impacts from rice residue burning in Hanoi, Vietnam through trajectory modelling and synoptic meteorology patterns, as well as timeseries of satellite air pollution and reanalysis datasets. The results highlight the inherent difficulty to capture air pollution impacts in the region, especially attributed to cloud cover obstructing optical satellite observations of episodic biomass burning. Overall, this dissertation found that a prominent satellite-based emissions dataset vastly underestimates emissions from rice residue burning. Recommendations for future work highlight the importance for these datasets to account for crop and burning practice specific emission factors for improved emissions estimates, which are useful to more accurately highlight the importance of reducing emissions from residue burning to alleviate air quality issues.