Mapping Annual Urban Change Using Time Series Landsat and NLCD

Abstract

Annual urban change information is important for an improved understanding of urban dynamics and continuous modeling of urban ecosystem processes. This study examined Landsat-derived Normalized Difference Vegetation Index (NDVI) time series for characterizing annual urban change. To reduce impacts from cloud contamination and missing data, United States Geological Survey (USGS) Landsat Analysis Ready Data were processed to derive annual NDVI layers using a maximum value composite algorithm. National Land Cover Database land cover products from 2001 and 2011 were used as references for generating a decadal urban change mask. Within the decadal urban change mask and using annual NDVI as input, we examined three time-series change detection methods to pinpoint specific year of urban change: (a) minimum-value method, (b) break-point detection, and (c) simple-threshold identification. For accuracy assessment, we divided change pixels into urbanization and urban-intensification pixel groups, defined by initial land cover types. We used Google Earth's High-Resolution Imagery Archive as primary reference data for detailed accuracy assessment. Overall, the urbanization pixel group has good change detection accuracies of above 82% for all three change detection algorithms. The break-point detection method resulted in the highest overall accuracy of 88%. Overall accuracies for urban intensification pixel group were in the range of 35%–76%, depending on choice of change detection algorithm, length of input time-series, and further division of pixel subgroups.

Understanding Spatio-Temporal Patterns of Land Use/Land Cover Change under Urbanization in Wuhan, China, 2000–2019

“…Time series mappings have been reported to be helpful for exploring spatio-temporal patterns of LULC change and urban evolution law due to its abundant information [17,18]. For instance, Seto et al explored the landscape dynamics of four Chinese cities based on time series LULC maps [19].…”

“…As the largest developing country in the world, China has experienced unprecedented urbanization and significant landscape changes during the past decades of reform and opening-up [17,22]. The urbanization rate of China increased sharply from 17.92% to 59.58% during the period 1978-2018 [23].…”

Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms

“…Most of the above work focuses on changes in a few satellite images-two or three instead of dozens or hundreds. Previous work focusing on longer time series is typically concerned with larger scale changes than the addition of buildings, e.g., urban expansion (Wan et al, 2019), changing land cover (Zhu and Woodcock, 2014), forest disturbance (Kennedy et al, 2010;Huang et al, 2010), or vegetation (Verbesselt et al, 2010b;Browning et al, 2017).…”

“…A common approach for longer sequences of imagery is to reduce each image to a metric -a vegetation or drought index for instance -and apply more traditional changepoint detection techniques to the resulting time series. The Normalized Difference Vegetation index (NDVI) is one popular metric, used for example by both Wan et al (2019) to detect urban change in Landsat imagery, and by the Breaks for Additive Season Trend (bfast) algorithm (Verbesselt et al, 2010a). NDVI is defined as…”

Land Cover Change Analysis in the State of California Using NLCD Data

“…Remote Sensing data is being increasingly used for land cover change monitoring due to the availability of time series of data. Landsat data and other regionally available remote sensing data has been used for LULC change analysis (Singh and Dubey, 2012;Bijender and Joginder, 2014;Nguyen et al, 2016;Utomo and Kurniawan, 2016;Wan et al, 2019).…”