An Approach for Monitoring and Classifying Marshlands Using Multispectral Remote Sensing Imagery in Arid and Semi-Arid Regions

Abstract

Marshlands in arid and semi-arid areas are considered constantly changing environments due to unsecured water supplies as a result of high evapotranspiration and limited and highly variable rainfall. Classification of marshlands in these regions and mapping of their land cover is not an easy task and maps need to be upgraded frequently. Satellites provide enormous amounts of information and data for the continuous monitoring of changes. The aim of this paper is to introduce an approach using multispectral satellite imagery that was adopted to classify and monitor the Al Hammar Marsh (Iraq) over several years and to suggest a relationship between the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and the Normalized Difference Water Index (NDWI), using Landsat 8 data with a resolution of 30 m × 30 m, validated with Sentinel-2 datasets at 10 m × 10 m. Six land cover classes were used: (1) open water, (2) dry area, (3) dense vegetation, (4) medium-density vegetation, (5) sparse vegetation, and (6) wet soil. Three indices, NDWI, NDMI, and NDVI, were chosen for the automatic classification of each pixel and the creation of a time series of land cover maps. The proposed method can efficiently classify and monitor marshlands and can be used to study different marshlands by adjusting the thresholds for NDVI, NDMI, and NDWI. Overall, the correlation for all classes (R) between Landsat 8 and Sentinel-2 is about 0.78. Thus, this approach will help to preserve marshes through improved water management.

Estimating Changing Marshland Habitat and Conservation Potential for Diamondback Terrapins (Malaclemys terrapin) in New Jersey under Climate Change

“…Areas classified with the field values of "phragmites dominate coastal wetlands", "saline marsh (high marsh)", "saline marsh (low marsh)", and "tidal mud flat", were selected and extracted. As the most recent data were released 8 years prior to this study, the method proposed by Al-Maliki and colleagues was reproduced to eliminate areas not suitable for marshes, masking the initial marsh land uses with areas derived from remotely sensed data in which marshes were either inundated, too dry, or lacked vegetation [25]. Metrics of the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized difference moisture index (NDMI) were used for this calculation.…”

“…Sea level rise projections from the 2020 New Jersey Scientific Report on Climate Change were used for 17% and 83% likelihood scenarios for 2050 and 2100 [25]. The digital elevation model was rescaled to 30 m/pixel to match the Landsat resolution and thresholded according to the inundated area under projected sea level rise to further mask the current habitat and nesting area.…”