Developments in Landsat Land Cover Classification Methods: A Review

Assessment of Land Cover Changes in the Hinterland of Barranquilla (Colombia) Using Landsat Imagery and Logistic Regression

Barranquilla is known as a dynamically growing city in the Colombian Caribbean. Urbanisation induces land use and land cover (LULC) changes in the city and its hinterland affecting the region’s climate and biodiversity. This paper aims to identify the trends of land use and land cover changes in the hinterland of Barranquilla corresponding to 13 municipalities in the north of the Department Atlántico. Landsat TM/ETM/OLI imagery from 1985 to 2017 was used to map and analyse the spatio-temporal development of land use and land cover changes. During the investigation period, the settlement areas grew by approximately 50% (from 103.3 to 153.6 km2), while areas with woody vegetation cover experienced dynamic changes and increased in size since 2001. Peri-urban and rural areas were characterized by highly dynamic changes, particularly regarding clearing and recovery of vegetated areas. Regression analyses were performed to identify the impact factors of detected vegetation cover changes. Computed logistic regression models included 20 independent variables, such as relief, climate, soil, proximity characteristics and socio-economic data. The results of this study may act as a basis to enable researchers and decision-makers to focus on the most important signals of systematic landscape transformations and on the conservation of ecosystems and the services they provide.

Continuous Change Detection of Forest/Grassland and Cropland in the Loess Plateau of China Using All Available Landsat Data

Accurate identification of the spatiotemporal distribution of forest/grassland and cropland is necessary for studying hydro-ecological effects of vegetation change in the Loess Plateau, China. Currently, the accuracy of change detection of land cover using Landsat data in the loess hill and gully areas is seriously affected by insufficient temporal information from observations and irregular fluctuations in vegetation greenness caused by precipitation and human activities. In this study, we propose a method for continuous change detection for two types of land cover, mosaic forest/grassland and cropland, using all available Landsat data. The period with vegetation coverage is firstly identified using normalized difference vegetation index (NDVI) time series. The intra-annual NDVI time series is then developed at a 1-day resolution based on linear interpolation and S-G filtering using all available NDVI data during the period when vegetation types are stable. Vegetation type change is initially detected by comparing the NDVI of intra-annual composites and the newly observed NDVI. Finally, the time of change and classification for vegetation types are determined using decision tree rules developed using a combination of inter-annual and intra-annual NDVI temporal metrics. Validation results showed that the change detection was accurate, with an overall accuracy of 88.9% ± 1.0%, and a kappa coefficient of 0.86, and the time of change was successfully retrieved, with 85.2% of the change pixels attributed to within a 2-year deviation. Consequently, the accuracy of change detection was improved by reducing temporal false detection and enhancing spatial classification accuracy.

Geospatial Analysis of Horizontal and Vertical Urban Expansion Using Multi-Spatial Resolution Data: A Case Study of Surabaya, Indonesia

Urbanization addresses urban expansion, and it leads conversion of the green space into the built-up area. However, previous studies mainly focused on two-dimensional (2D) urban expansion rather than three-dimensional (3D) growth. Here, the purpose of this study is to examine the urban expansion, including built-up and green space for both horizontal and vertical dimensions using geospatial analysis including remote sensing (RS) and Geographic Information System (GIS) in the sub-Central Business District (CBD) area of Surabaya, Indonesia. The medium resolution remote sensing data for both image and Digital Surface Model (DSM) acquired by Advanced Land-Observing Satellite (ALOS) were applied for time-1 (2010). The orthophoto and DSM derived by LiDAR were used for time-2 (2016). We quantified the built-up and green expansions in 2D (area), which were extracted from land use/land cover (LU/LC) by applying hybrid classification. The built-up and green expansions in 3D (volume) were estimated by generating a surface feature model. The spatial configuration of area expansion was investigated using patch metric, while the volume growth was examined using the volume expansion rate. We got three findings. (1) The built-up and green area had expanded about 11.54% and 95.61%, respectively, from 2010 to 2016. The expansion of green area presented in a notable portion, which was mainly contributed by the conversion of bareland to playground or park. However, the expansion of built-up area was less than the volume expansion of 20.6%. It revealed that built-up growth led to vertical rather than horizontal development. (2) The built-up area expansion tended to scatter configuration, whereas, the green area expansion tended to aggregate in a linear pattern. (3) The ratio of built-up volume expansion to green volume expansion showed a mean of 3.7, indicating that the development of built-up and green volume was imbalanced. The built-up growth presented higher than the green growth, mainly in the areas with more vertical building establishment. The pressing need for higher green volume in the study area was identified in several sites located at surrounding artery and toll roads. Overall, our approach can be applied as a reference in monitoring neighborhood environment through greening programs for sustainable urban development.

Land Surface Temperature and Urban Density: Multiyear Modeling and Relationship Analysis Using MODIS and Landsat Data

This work aims to model and relate the urban density and land surface temperature (LST) by a straightforward and efficient approach. Although the urban density-LST relation is widely addressed in literature, this study allows for its modeling and parameterization in an accurate way, providing a further scientific support for the city planning policy. The urban density and the LST analysis is carried out in the Bangkok area for the years 2004, 2008, 2012, and 2016; in this time interval, the city exhibited an evident urban expansion. Firstly, by using land cover maps obtained from Landsat reflective observations, the urban land density growth across the years studied is evaluated by applying a ring-based approach, a method employed in urban theory, providing urban density curves as a function of the distance from the city center. For each year, the urban density curve is well modeled by an inverse S-shape function, the parameters of which highlight an urban sprawl over the years studied and an outskirt growth in recent years. Then, employing 237 MODIS LST images, the night-time and daytime mean LST patterns for each year were processed applying the same ring-based analysis, obtaining LST trends versus distance. Albeit the mean LST decreases away from the city core, the daytime and night-time trends are different in both shape and values. The daytime LST exhibits a trend also modeled by an inverse S-shape function, whereas the night-time one is modeled by a quadratic function. Finally, the urban density-LST relationship is inferred across the years: For daytime, the relation is quadratic with a coefficient of determination r2 around 0.98–0.99, whereas for night-time the relation is linear with r2 of the order of 0.95–0.96. The proposed approach allows for reliable modeling and to straightforwardly infer a very accurate urban density-LST relationship.

Object-based land-use/land-cover change detection using Landsat imagery: a case study of Ardabil, Namin, and Nir counties in northwest Iran

In this study, land-use/land-cover (LULC) change in the Ardabil, Namin, and Nir counties, in the Ardabil province in the northwest of Iran, was detected using an object-based method. Landsat images including Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM⁺), and Operational Land Imager (OLI) were used. Preprocessing methods, including geometric and radiometric correction, and topographic normalization were performed. Image processing was conducted according to object-based image analysis using the nearest neighbor algorithm. An accuracy assessment was conducted using overall accuracy and Kappa statistics. Results show that maps obtained from images for 1987, 2002, and 2013 had an overall accuracy of 91.76, 91.06, and 93.00%, and a Kappa coefficient of 0.90, 0.83, and 0.91, respectively. Change detection between 1987 and 2013 shows that most of the rangelands (97,156.6 ha) have been converted to dry farming; moreover, residential and other urban land uses have also increased. The largest change in land use has occurred for irrigated farming, rangelands, and dry farming, of which approximately 3539.8, 3086.9, and 2271.9 ha, respectively, have given way to urban land use for each of the studied years.