Land-Cover Changes to Surface-Water Buffers in the Midwestern USA: 25 Years of Landsat Data Analyses (1993–2017)

Abstract

To understand the timing, extent, and magnitude of land use/land cover (LULC) change in buffer areas surrounding Midwestern US waters, we analyzed the full imagery archive (1982–2017) of three Landsat footprints covering ~100,000 km2. The study area included urbanizing Chicago, Illinois and St. Louis, Missouri regions and agriculturally dominated landscapes (i.e., Peoria, Illinois). The Continuous Change Detection and Classification algorithm identified 1993–2017 LULC change across three Landsat footprints and in 90 m buffers for ~110,000 surface waters; waters were also size-binned into five groups for buffer LULC change analyses. Importantly, buffer-area LULC change magnitude was frequently much greater than footprint-level change. Surface-water extent in buffers increased by 14–35x the footprint rate and forest decreased by 2–9x. Development in buffering areas increased by 2–4x the footprint-rate in Chicago and Peoria area footprints but was similar to the change rate in the St. Louis area footprint. The LULC buffer-area change varied in waterbody size, with the greatest change typically occurring in the smallest waters (e.g., <0.1 ha). These novel analyses suggest that surface-water buffer LULC change is occurring more rapidly than footprint-level change, likely modifying the hydrology, water quality, and biotic integrity of existing water resources, as well as potentially affecting down-gradient, watershed-scale storages and flows of water, solutes, and particulate matter.

Improving Land Cover Change Detection and Classification With BRDF Correction and Spatial Feature Extraction Using Landsat Time Series: A Case of Urbanization in Tianjin, China

“…The Continuous Change Detection and Classification (CCDC) algorithm used all spectral bands to detect various kinds of land cover change continuously [16,17]. It has been implemented into the USGS Land Change Monitoring, Assessment, and Projection (LCMAP) program for generating land cover and land change products for the United States [18][19][20] and also widely used in many applications, such as urban expansion [19,21,22], hydrology dynamic [23], and forest disturbance [24].…”

A systematic framework for continuous monitoring of land use and vegetation dynamics in multiple heterogeneous mine sites

“…A total of 9899 pixels were selected to form the validation data and the number of pixels for each land cover type was proportional to its area coverage. A minimum linear distance of 250 meters between any two validation pixels was used to reduce autocorrelation (Berhane et al., 2020). Besides, a total of 200 points of them were randomly selected for the field survey, and compared the LULC maps with high‐resolution images and field survey information to validate the accuracy of LULC classification in 2019.…”

Continuous Change Mapping to Understand Wetland Quantity and Quality Evolution and Driving Forces: A Case Study in the Liao River Estuary from 1986 to 2018

“…We selected 9899 pixels proportional to the areal extent of each land cover as the validation dataset. We kept the minimum linear distance between any two pixels at 250 m (we selected this distance to reduce the potential for auto correlation) [31]. The user accuracy, producer accuracy, overall accuracy, and kappa coefficient of each land cover were calculated [32].…”