Geography Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1641

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Start date: 2020Subject: search.filters.subject.remote sensing

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Now showing 1 - 8 of 8
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    Assessing Terrestrial Ecosystem Resilience using Satellite Leaf Area Index
    (MDPI, 2020-02-11) Wu, Jinhui; Liang, Shunlin
    Quantitative approaches to measuring and assessing terrestrial ecosystem resilience, which expresses the ability of an ecosystem to recover from disturbances without shifting to an alternative state or losing function and services, is critical and essential to forecasting how terrestrial ecosystems will respond to global change. However, global and continuous terrestrial resilience measurement is fraught with difficulty, and the corresponding attribution of resilience dynamics is lacking in the literature. In this study, we assessed global terrestrial ecosystem resilience based on the long time-series GLASS LAI product and GIMMS AVHRR LAI 3g product, and validated the results using drought and fire events as the main disturbance indicators. We also analyzed the spatial and temporal variations of global terrestrial ecosystem resilience and attributed their dynamics to climate change and environmental factors. The results showed that arid and semiarid areas exhibited low resilience. We found that evergreen broadleaf forest exhibited the highest resilience (mean resilience value (from GLASS LAI): 0.6). On a global scale, the increase of mean annual precipitation had a positive impact on terrestrial resilience enhancement, while we found no consistent relationships between mean annual temperature and terrestrial resilience. For terrestrial resilience dynamics, we observed three dramatic raises of disturbance frequency in 1989, 1995, and 2001, respectively, along with three significant drops in resilience correspondingly. Our study mapped continuous spatiotemporal variation and captured interannual variations in terrestrial ecosystem resilience. This study demonstrates that remote sensing data are effective for monitoring terrestrial resilience for global ecosystem assessment.
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    Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery
    (MDPI, 2021-02-26) Skakun, Sergii; Kalecinski, Natacha I.; Brown, Meredith G. L.; Johnson, David M.; Vermote, Eric F.; Roger, Jean-Claude; Franch, Belen
    Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.
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    Metrics to Accelerate Private Sector Investment in Sustainable Development Goal 2—Zero Hunger
    (MDPI, 2021-05-25) Brown, Molly E.
    Substantial investment from both the private and public sectors will be needed to achieve the ambitious Sustainable Development Goal 2 (SDG2), which focuses on ending poverty and achieving zero hunger. To harness the private sector, high quality, transparent metrics are needed to ensure that every dollar spent reaches the most marginalized segments of a community while still helping institutions achieve their goals. Satellite-derived Earth observations will be instrumental in accelerating these investments and targeting them to the regions with the greatest need. This article proposes two quantitative metrics that could be used to evaluate the impact of private sector activities on SDG2: measuring increases in yield over baseline and ensuring input availability and affordability in all markets.
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    A Disease Control-Oriented Land Cover Land Use Map for Myanmar
    (MDPI, 2021-06-13) Chen, Dong; Shevade, Varada; Baer, Allison; He, Jiaying; Hoffman-Hall, Amanda; Ying, Qing; Li, Yao; Loboda, Tatiana V.
    Malaria is a serious infectious disease that leads to massive casualties globally. Myanmar is a key battleground for the global fight against malaria because it is where the emergence of drug-resistant malaria parasites has been documented. Controlling the spread of malaria in Myanmar thus carries global significance, because the failure to do so would lead to devastating consequences in vast areas where malaria is prevalent in tropical/subtropical regions around the world. Thanks to its wide and consistent spatial coverage, remote sensing has become increasingly used in the public health domain. Specifically, remote sensing-based land cover/land use (LCLU) maps present a powerful tool that provides critical information on population distribution and on the potential human-vector interactions interfaces on a large spatial scale. Here, we present a 30-meter LCLU map that was created specifically for the malaria control and eradication efforts in Myanmar. This bottom-up approach can be modified and customized to other vector-borne infectious diseases in Myanmar or other Southeastern Asian countries.
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    Lidar-Imagery Fusion Reveals Rapid Coastal Forest Loss in Delaware Bay Consistent with Marsh Migration
    (MDPI, 2022-09-13) Powell, Elisabeth B.; St. Laurent, Kari A.; Dubayah, Ralph
    Tidal wetland ecosystems and their vegetation communities are broadly controlled by tidal range and inundation frequency. Sea-level rise combined with episodic flooding events are causing shifts in thresholds of vegetation species which reconstructs the plant zonation of the coastal landscape. More frequent inundation events in the upland forest are causing the forest to convert into tidal marshes, and what is left behind are swaths of dead-standing trees along the marsh–forest boundary. Upland forest dieback has been well documented in the mid-Atlantic; however, reliable methods to accurately identify this dieback over large scales are still being developed. Here, we use multitemporal Lidar and imagery from the National Agricultural Imagery Program to classify areas of forest loss in the coastal regions of Delaware. We found that 1197 ± 405 hectares of forest transitioned to non-forest over nine years, and these losses were likely driven by major coastal storms and severe drought during the study period. In addition, we report decreases in Lidar-derived canopy height in forest loss areas, suggesting forest structure changes associated with the conversion from forest to marsh. Our results highlight the potential value of integrating Lidar-derived metrics to determine specific forest characteristics that may help predict future marsh migration pathways.
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    Determining Variability in Arctic Sea Ice Pressure Ridge Topography With ICESat-2
    (Wiley, 2022-09-16) Duncan, K.; Farrell, S. L.
    We investigate the characteristics and distribution of pressure ridges in Arctic sea ice using a novel algorithm applied to ICESat-2 surface heights. We derive the frequency and height of individual pressure ridges and map surface roughness and ridging intensity at the basin scale over three winters between 2019 and 2021. Comparisons with near-coincident airborne lidar data show that not only can we detect individual ridges 5.6 m wide, but also measure sail height more accurately than the existing ICESat-2 sea ice height product. We find large regional variability in ridge morphology not only related to parent ice type but also geographic location. Ridge sails are best represented by log-normal distributions while surface roughness is well fit by an exponential normal function. Our results reveal that high-resolution satellite altimetry is valuable for characterizing sea ice deformation at short length-scales and delivers observations that will advance ridging parameterizations in sea ice models.
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    Peer-Reviewed Offset Protocol for U.S. Forest Projects 1.0
    (2022-04-29) Albee, Madeleine; Hoffman Delett, Camille; Panday, Frances Marie; Lamb, Rachel; Hurtt, George
    The protocol was developed for submission into Second Nature's Peer Review Offset Network by the Campus Forest Carbon Project. As of October 2022, the protocol is still under review. This offset protocol is a modified version of a previously adopted protocol created by the California Environmental Protection Agency Air Resources Board as a Compliance Offset Protocol for U.S. Forest Projects. The Campus Forest Carbon Project modified this protocol using NASA Carbon Monitoring System science to integrate a high-resolution remote sensing and modeling based quantification methodology into the voluntary carbon offset market for forest projects. Accompanying the protocol is a background and development document that outlines the specific changes to the protocol and the context surrounding its development.
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    Campus Forest Carbon Project Technical Guidance Document
    (2022-08-11) Panday, Frances Marie; Howerton, Michael; Kopp, Katelyn; Hurtt, George; Lamb, Rachel
    The technical guidance document was created for the Office of Sustainability to support the inclusion of forest carbon into UMD's Greenhouse Gas Inventory. This document outlines the Campus Forest Carbon's project role within UMD's climate action plan and the approach to calculating forest carbon dynamics on UMD managed and owned properties.