Multitemporal land use and land cover classification of urbanized areas within sensitive coastal environments

Abstract

In the presented methodology, multitemporal Landsat images were used to develop enhanced information about complex assemblages of vegetation and patterns of seasonal land cover variability, thereby facilitating improved land use and land cover (LULC) classification of urbanized areas among sensitive environments along the Mississippi Gulf Coast. For Landsat-5 and Landsat-7 images acquired for leaf-off and leaf-on conditions for 1991 and 2000, exploratory spectral analyses and field studies were conducted to detect and analyze patterns of spectral variability in land cover observed in the multitemporal image data. Patterns were identified of seasonal spectral data changes associated with seasonal vegetation changes for known land cover and land use types, thus characterizing patterns of seasonal LULC thematic change for the area. Detected seasonal variability for known land use and land cover types were used to develop formal classification rules based upon a thematic-change logic table. An image subset area based on United States Geological Survey (USGS) 1 : 24,000 quadrangles was used to develop a class-learning area within which unsupervised classification results were grouped into thematic classes. Signature files from the unsupervised classification results were applied to classify the balance of the study area. Individual images were classified for leaf-off and leaf-on conditions and thematic-change analyses were conducted. The formal class rules based on thematic-change logic were applied resulting in a classification that provided a level one accuracy exceeding 90% and a level two accuracy exceeding 85%. Index Terms-Coastal remote sensing, multitemporal, urban remote sensing, wetlands. I. INTRODUCTION L ULC classification of urbanized areas within lushly vegetated, sensitive coastal environments is a challenging task to accurately accomplish using synoptic Landsat imagery. Multitemporal imagery provides increased information about urbanized areas within complex assemblages of seasonally variable vegetation in temperate coastal regions. However, the com-Manuscript

Geospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products, using Landsat data and C-CAP products

“…Since the launch of Landsat 5 in 1984, several studies have employed multi-date Landsat imagery for assessing LULC change in the Gulf of Mexico coastal region (e.g., Ramsey and Laine 1997, Kelly 2001, Ramsey et al 2001, Nelson et al 2002, O'Hara et al 2003, Yang and Liu 2005, Hilbert 2006, Martinez and Shealand 2009 and in other coastal regions of the United States, such as the Chesapeake Bay (Goetz et al 2004, Jantz et al 2005 of the Atlantic seaboard and the San Diego region of the Pacific coast (Rogan et al 2003). Table 1 provides a summary of coastal LULC change studies that employed Landsat as the primary data source.…”

Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China

“…Yan et al (2005) applied time-series MODIS NDVI dataset within 1 year to extract double and single cropping systems in China. O'Hara et al (2003) examined the complex assemblages of vegetation and their seasonal variability based on Landsat imagery acquired from leaf-off and leaf-on conditions in 1991 and 2000. Lu et al (2011) used multiple resolution images (MSS, TM/ ETM) to extract various LULC types, including forest and orchard, double-cropping land, single-cropping land, no-vegetable land, and water, in the Shandong Peninsula, a coastal region in China, based on temporal characteristics of vegetation, water, barren land, and urban areas.…”

An assessment of coastal land-use and land-cover change from 1974–2008 in the vicinity of Mobile Bay, Alabama

“…Approximately 98% of Gulf of Mexico harvested commercial fish and shellfish depend on estuaries and wetlands (NOAA-NMFS 2009 In recent years, it has become common to employ data from the suite of Landsat sensors to determine LULC change in the Gulf of Mexico region of the United States (e.g., Ramsey and Laine 1997;Ramsey et al 2001;Nelson et al 2002;O'Hara et al 2003;Xian and Crane 2005;Yang and Liu 2005;Hilbert 2006;Fearnley et al 2009) and assess changes in other coastal regions of the country (e.g., Jantz et al 2005). National Oceanic and Atmospheric Administration's (NOAA) Coastal Change Analysis Program (C-CAP) has produced a national standard geospatial database to track coastal LULC change (NOAA-CSC 2008).…”