DOI: 10.5281/zenodo.6685695
title: High-resolution remotely sensed datasets for saltwater intrusion across the Delmarva Peninsula

description: <strong>Abstract:</strong> Saltwater intrusion (SWI) on coastal farmlands can change the soil properties (physical and chemical), rendering it unusable for agricultural purposes. Globally, over a quarter of arable land is negatively impacted by soil salinization, including more than 50% of irrigated land. These salt-impacted lands account for more than 30% of food production worldwide. However, the visible impacts of SWI on coastal ecosystems are challenging to map due to the fine spatial resolution of the salt patches. Here we provide the first mapping of the early visual evidences of SWI impacts on the Delmarva (Delaware, Maryland, Virginia) Peninsula region's farmlands by quantifying and mapping the proportions of the farmlands where the spectral signature of a white salt patch was detected. We focus our effort on fourteen counties on the Delmarva Peninsula. We utilized very high-resolution (1-m) aerial imagery from the National Agriculture Imagery Program (NAIP) and seasonal information derived from the moderate resolution (30-m) Landsat satellite imagery collection. Using a Random Forest algorithm with 100 trees and over 94,240 reference points for training and testing, we developed high-resolution geospatial datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland counties witnessed an average of 79% increase in the salt patches on farmlands. The average increase across the state of Delaware is 81%. Virginia experienced an average of 243% increase in these salt patches. While the expansion rate is alarming, the absolute area with these salt deposits remained rather small even in 2017: about 122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland. Visible white salt patches remained a small fraction of total farmlands in each of these counties, ranging between 0.01% and 0.18% in 2011-2013, and between 0.01% and 0.39% in 2016-2017. <strong>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -</strong> This collection of gridded data layers provides the spatial distribution of salt patches along with seven other land cover classes for 14 counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula in the United States of America (USA). We developed high-resolution datasets for the study area for two time-steps: 2011-2013 and 2016-2017. The geospatial datasets are classified images for each time-step and have eight land cover categories as shown below: <strong>Raster value Land cover/use category</strong> 1 Forest 2 Marsh 3 Salt patch 4 Built 5 Open water 6 Farmland 7 Bare soil 8 Other vegetation <strong>Input Data:</strong> These geospatial data layers are derived using aerial data from the National Agriculture Imagery Program (NAIP) and satellite data from Landsat 5, 7, and 8. We accessed ortho-rectified NAIP images from June-July 2011 (Maryland), May 2012 (Virginia), September 2013 (Delaware), June 2016 (Virginia), June 2017 (Maryland), and July-August 2017 (Delaware) on the Google Earth Engine (GEE) platform. Cloud-masked top-of-atmosphere (TOA) reflectance images from Landsat 5 (2011, 2012), Landsat 7 (2013), and Landsat 8 (2016, 2017) were obtained using GEE. We derived several spectral indices from the original NAIP and Landsat bands and then used those as inputs into a Random Forest (RF) classifier on GEE. <strong>Methods:</strong> NAIP data contains 4 spectral bands (red, blue, green, and near-infrared) and have a 1 m spatial resolution. Several spectral indices were calculated from the NAIP imagery and used as input into the RF classifier, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and a Shadow Index (SI). A Principal Component Analysis (PCA) was used to generate four additional bands. In addition, four smoothed NAIP bands were generated using a 3x3 boxcar kernel. NDVI = (Near-infrared – Red) / (Near-infrared + Red) NDWI = (Green – Near-infrared) / (Green + Near-infrared) SI = (256 – Blue) * (256 + Blue) In order to address limited spectral resolution of the NAIP data and lack of year-long coverage, we incorporated seasonal information from Landsat data. Landsat is a series of satellites launched by the National Aeronautics and Space Administration (NASA) with satellite images distributed through the United States Geological Survey (USGS). Landsat data includes red, green, blue, near-infrared, shortwave infrared, aerosol, cirrus, panchromatic, and thermal bands. All bands are collected at a 30 m resolution except the panchromatic band, which is collected at a 15 m resolution and the thermal bands which are collected at a 100 m resolution. In this work, Landsat 5 was used for 2011 and 2012, Landsat 7 was used for 2013, and Landsat 8 was used for 2016 and 2017. Landsat data (spatial resolution: 30 m) were fused with NAIP data to a resolution of 1 m. An Enhanced Vegetation Index (EVI) was calculated from Landsat bands for each of the four seasons (June-August, September-November, December-February, and March-May) and was then used as input into the RF classifier. Seasonal data was reduced using a median reducer. Landsat thermal bands for each season were also used in the classification. Again, bands were smoothed using a 3x3 boxcar kernel. EVI = (NIR-Red) / (NIR+6*Red-7.5*Blue+1) A Random Forest (RF) classifier was used with input data comprised of the four NAIP bands, four PCA bands from NAIP, three indices from NAIP, four smoothed NAIP bands, four smoothed seasonal EVI bands from Landsat, and four smoothed seasonal thermal bands (from Landsat 5) or eight when (for Landsat 7 or 8) – all sampled to a 1 m resolution to match the NAIP input bands. Due to the high resolution of the input data, there is a considerable 'salt-and-pepper' effects or speckle effects on the classified image, especially for the salt deposit class and its surroundings. As a post-processing step to reduce such speckle effects, we applied a majority filter to the classified image using eight pixel neighbors. For example, any solitary salt patch pixel was reclassified as the majority land cover within the immediate neighborhood. Furthermore, we considered only patches of 10 or more connected 'salt patch' pixels as a valid salt signature. We also used a road mask to minimize the confusion between impervious streets and salt deposits. <strong>Accuracy assessment:</strong> A total of 94,240 reference points were collected from ground surveys and visual interpretation of NAIP imagery from both time periods. 70% of these points were used to train the RF classifier and 30% were used to test accuracy. We calculated user’s accuracy, producer’s accuracy, overall accuracy, kappa statistic, and the F-Score as shown below. <strong>Delaware 2013</strong> Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 88.46% 90.89% 0.90 86.37% 0.83 Marsh 84.03% 80.13% 0.82 Salt patch 97.02% 71.18% 0.82 Built 94.56% 95.06% 0.95 Water 91.01% 96.63% 0.94 Farmland 83.16% 86.19% 0.85 Bare Soil 87.70% 87.30% 0.88 Other Vegetation 82.46% 84.20% 0.83 <strong>Delaware 2017</strong> Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 95.07% 90.30% 0.93 91.37% 0.90 Marsh 88.86% 92.44% 0.91 Salt patch 91.82% 85.59% 0.89 Built 87.58% 93.54% 0.90 Water 92.68% 90.48% 0.92 Farmland 91.61% 93.61% 0.93 Bare Soil 95.67% 87.67% 0.91 Other Vegetation 91.16% 90.24% 0.91 <strong>Maryland 2011</strong> Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 88.32% 90.97% 0.90 87.20% 0.85 Marsh 87.14% 82.08% 0.85 Salt patch 96.74% 78.76% 0.87 Built 89.02% 88.50% 0.89 Water 92.74% 96.10% 0.94 Farmland 84.31% 89.83% 0.87 Bare Soil 87.53% 90.05% 0.89 Other Vegetation 86.11% 80.57% 0.83 <strong>Maryland 2017</strong> Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 88.66% 88.66% 0.89 87.34% 0.84 Marsh 87.65% 88.35% 0.88 Salt patch 93.29% 68.30% 0.79 Built 93.44% 86.92% 0.90 Water 92.36% 92.36% 0.92 Farmland 83.84% 94.55% 0.89 Bare Soil 92.86% 82.61% 0.87 Other Vegetation 86.56% 76.00% 0.81 <strong>Virginia 2012</strong> Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 84.65% 91.18% 0.88 86.88% 0.84 Marsh 87.03% 84.39% 0.86 Salt patch 97.67% 72.41% 0.83 Built 90.97% 86.24% 0.89 Water 94.17% 87.39% 0.91 Farmland 86.87% 91.81% 0.89 Bare Soil 85.82% 83.04% 0.84 Other Vegetation 83.60% 80.59% 0.82 <strong>Virginia 2016</strong> Categories User’s accuracy Producer’s accuracy F-score Overall Kappa Forest 84.65% 86.00% 0.85 85.83% 0.83 Marsh 86.25% 90.72% 0.88 Salt patch 90.61% 62.12% 0.74 Built 88.70% 77.72% 0.83 Water 92.12% 84.41% 0.88 Farmland 85.17% 90.36% 0.88 Bare Soil 83.30% 88.54% 0.86 Other Vegetation 84.49% 84.17% 0.84 While our datasets have an overall high accuracy, a few caveats should be considered when utilizing the data for other applications. Misclassifications of salt patches might arise from a flooding event immediately prior to the image acquisition or spectral similarity with marsh. Misclassifications might also arise from spectral similarities between crop fields and other vegetation, which typically encompasses open fields and lawns. Shadows are sometimes misclassified as water, or built. The algorithm used in this work often under-predicted salt patches, because the typical bright white signature of these patches can be altered when those areas become wet, leading these areas to be classified as crop fields. Some of the areas classified as salt patches might be bleached siliceous minerals visible on the soil surface. <strong>Data format:</strong> The spatial resolution of all the derived datasets is 1 m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis. <strong>Code:</strong> Sample code is available at https://code.earthengine.google.com/a3c66ac5f06a796fc221a5c902486806. The user would need to upload study area boundaries and reference points i
description: Funding acknowledgment: This research was made possible by the National Science Foundation EPSCoR Grant No. 1757353 and the State of Delaware. This work was also supported by the National Aeronautics and Space Administration EPSCoR Grant No. DE-80NSSC20M0220, and the Delaware Space Grant College and Fellowship Program (NASA Grant 80NSSC20M0045). We acknowledge partial support provided by the National Fish and Wildlife Foundation, the State of Maryland, and Harry R. Hughes Center for Agro-Ecology.