27 Sep 2024

Habitat and Landcover Classification and Maritime Forest Restoration Recommendations for Deer Island, Mississippi

Abstract: This report addresses two objectives: (1) an island-wide survey and mapping initiative to document habitat and landcover types present on Deer Island, Mississippi, and (2) an evaluation of forested resources on Deer Island along with recommendations to improve and expand the extent of maritime forests on the island. Diverse habitats were documented, including more than 30 distinct habitat and landcover types ranging from wetland marshes to maritime forests and sand ridges. The habitat and landcover survey (and accompanying maps) support ongoing and future ecosystem restoration activities, provides baseline data to conduct change analysis over time, and informs decision-making related to the management of the island’s natural resources. Additionally, the characterization of Deer Island’s forests documented a range of forest health conditions dictated by elevation gradients, soils, invasive species presence, and other factors. Collectively, the data presented inform ongoing planning efforts related to restoration activities on the island as well as future management opportunities to ensure Deer Island continues to provide ecological functions that benefit the community of Biloxi, Mississippi. The results and recommendations herein are broadly applicable to other barrier islands across the northern Gulf region and promotes additional research into the ecology of these unique coastal features.

26 Sep 2024

A Geospatial Model for Identifying Stream Infrastructure Locations

Abstract: Management of hydraulic infrastructure for flood control, hydropower, navigation, and water supply is a critical component of the Army Dams and Transportation Infrastructure Program (ADTIP). This project provides a tool to locate stream infrastructure using a one-dimensional approach supplemented with geospatial filtering that only needs digital elevation model (DEM) files as primary input. The regions in and around Forts Liberty, Sill, and Cavazos were selected as study areas, and stream networks with corresponding stream elevation profiles were created and searched for elevation changes that met vertical threshold and search window criteria. Recall, Fβ, and a ratio of under to overprediction were used to evaluate performance. The search algorithm generally overpredicts the number of stream infrastructure locations and especially so for large search windows (20 or 25 cells) and small vertical threshold values (5 or 10 m). Overall, it was found that midrange vertical threshold values (2 or 2.5 m with long search windows (20 or 25 cells) with the land cover classification (LCC) check applied yielded results that minimized false negatives and overpredictions. The significance of this tool is that it may reduce costly field investigations, or at least aid in the prioritization of site visits for hydraulic infrastructure managers.

18 Sep 2024

Automated Mapping of Land Cover Type within International Heterogenous Landscapes Using Sentinel-2 Imagery with Ancillary Geospatial Data

Abstract: A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global and regional adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class, six-class, and five-class schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies. The results highlight performance in line with an intensive deep learning approach, and reasonably accurate models created without a full annual time series of imagery.

31 Aug 2022

Photographic Aerial Transects of Fort Wainwright, Alaska

Abstract: This report presents the results of low-altitude photographic transects conducted over the training areas of US Army Garrison Fort Wainwright, in the boreal biome of central Alaska, to document baseline land-cover conditions. Flights were conducted via a Cessna™ 180 on two flight paths over portions of the Tanana Flats, Yukon, and Donnelly Training Areas and covered 486 mi (782 km) while documenting GPS waypoints. Nadir photographs were made with two GoPro™ cameras operating at 5 sec time-lapse intervals and with a handheld digital camera for oblique imagery. This yielded 6,063 GoPro photos and 706 oblique photos. Each image was intersected with a land-cover-classification map, collectively representing 38 of the 44 cover categories.

29 Sep 2020

Using Unmanned Aircraft System (UAS) and Satellite Imagery to Map Aquatic and Terrestrial Vegetation

Purpose: The purpose of this study is to demonstrate the application potential of using unmanned aerial systems (UAS) combined with a time series of moderately high-resolution satellite imagery for mapping ecological restoration progress and resulting land cover changes. This technical note addresses a project under the US Army Corps of Engineers Ecosystem Management and Restoration Research Project (EMRRP) focusing on image acquisition and assessment, digital image processing techniques, analytical methodology, geospatial product development, and documentation of best practice for future data acquisition and analysis in support of ecological management efforts.