Land Use And Land Cover Change Detection Research Articles

Soft Computing Techniques for Land Use and Land Cover Monitoring with Multispectral Remote Sensing Images: A Review

Multispectral remote sensing images are the primary source in the land use and land cover (LULC) monitoring. This is achieved by LULC classification and LULC change detection. The change detection in LULC includes the detection of water bodies, forest fire, forest degradation, agriculture areas monitoring, etc. Various change detection and LULC classification methods have their own advantages and disadvantages, and no single method is optimal and finds applicability for all cases. This paper summarizes and analyses the various soft computing and feature extraction techniques used for LULC classification and change detection. Based on the average error rate, performances of the different soft computing techniques are evaluated. The broad usage of multispectral remote sensing images, object-based change detection, neural networks and various levels of image fusion methods offer more potential in LULC monitoring.

Support Vector Machine algorithm optimal parameterization for change detection mapping in Funil Hydroelectric Reservoir (Rio de Janeiro State, Brazil)

Change detection in Land Use and Land Cover (LULC) using Support Vector Machines (SVM) to mapping a geographic area is a way that has been studded and improved because of its advantages as low costs in terms of computing, field research and staff team. To use SVM, it is needed firstly to define the most efficient function to be used (linear, polynomial, and radial base function—RBF) and secondly to establish the most appropriate input parameters of them, based on the study area, which was the main challenge of using SVM algorithm. The main goal of this work was to test the performance of polynomial function and RBF, and to identify which input parameters combination are the best to use SVM algorithm for Funil Hydroelectric Reservoir (FHR) sub-watershed LULC mapping, using TM/Landsat-5 time-series images. After several tests and based on the obtained results, the RBF was identified as the best SVM’s function, which was used to classify the time-series images. The referred SVM function has the following parameters to be defined: the error tolerance (ξ or C), the pyramid depths (P), the radial basis function parameter (γ), and the threshold. The most effective combination of input parameters to RBF was C = 100; P = 2, γ = 0.1, threshold = 0.05. LULC change detection analyses demonstrates that the obtained SVM parameterization made the algorithm able to differentiate large and continuous classes, lengthy and thin areas, as borders, and not continuous small areas located inside wide classes, through the usage of effective, but small, training sample. The parameterization proposed for this work to FHR sub-watershed area resulted in great statistics classification with the overall’s accuracy among 86 and 98 % over the time-series, the producer’s accuracy of 90 %, the user’s accuracy higher than 86 %, and the Kappa statistics ranged from 86 to 91 %.

A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery

Abstract The relationship between land use and land cover (LULC) patterns and thermal characteristic has long been studied for examining the impact of urbanization on urban thermal environment. Previous studies were inclined to the use of one or a few images without fully considering the temporal domain of the reflective and thermal infrared data on board Landsat sensors. This paper took the Atlanta metropolitan area as a case study to illustrate LULC change and its impact on land surface temperature (LST) variations. The Landsat L1T (Standard terrain correction) images from TM/ETM + from 1984 to 2011 (507 in total) were downloaded through the USGS online portal and consistently calibrated to surface reflectance and brightness temperature (BT). The cloud-, cloud shadow-, and snow-contaminated pixels were excluded in the analysis according to the metadata, and a further screening procedure based on the RIRLS (Robust Iteratively Reweighted Least Squares) technique was implemented. The time series LSTs (TSLSTs) was derived using the single channel algorithm because it required only the parameters of water vapor and land surface emissivity and had a reported error close to 1 K. The LULC classification and change detection was accomplished by using the Continuous Change Detection and Classification (CCDC) algorithm. The TSLSTs were further decomposed into the seasonal and trend components by an additive model. Results showed that the overall LULC classification and change detection accuracies were 89% and 92%, respectively. Urban growth was mainly observed in Fulton County and Gwinnett County. High-intensity urban land had the largest mean LST value of 294.9 K and yearly amplitude of 17.4 K. A comparison of the trend component between urban and non-urban land covers showed a difference of 1.8 K per decade. Next, temporal thermal signatures were created to characterize and quantify the impact of urban LULC changes. Decomposition analysis showed that the conversion of evergreen forest to medium-intensity urban land generated the largest difference in annual LST variation (5.7 K) and the largest trend difference (0.0004 K/day).

DETECTION OF LAND USE AND LAND COVER CHANGES IN DIRAB REGION OF SAUDI ARABIA USING REMOTELY SENSED IMAGERIES

Knowledge of Land Use and Land Cover (LULC) changes is important for many planning and management activities. It is thought to be an essential elemen t for modeling and understanding the major land for ms, especially in arid regions like Saudi Arabia. This study investigates the LULC changes in Dirab region of Saudi Arabia between 1980 and 2010, using Landsat TM/ETM+images. After the geometric correction and radiometric normalization, multi-temporal image dat a sets were spectrally enhanced separately using Principal Component Analysis (PCA) and Tasseled Cap Transformation (TCT). Each image was then separately subjected to supervised classification a nd processed to identify and quantify LULC changes (vegetation, barren land and built-up area). Post C lassification Comparison (PCC) method was adopted for LULC change detection. Change trajectories (“fr om-to” classes) and accuracy assessments were made by comparing the detected land use change layers wi th medium/high resolution images of Google Earth data base. The TCT enhanced procedure gave better identification of the changed areas than PCA based method. The overall accuracy of PCA based change detection was 64.58, 62.68 and 62.12% for 19801990, 1990-2000 and 2000-2010 images, respectively. However, the TCT based change detection resulted in higher accuracy of 77.78, 75.62 and 77. 92% for 1980-1990, 1990-2000 and 2009-2010, respectively. The results suggested that significan t land use changes occurred in Dirab area from 1980 to 2010, which may be related to rapid development of agriculture between 1980 and 2000 and economic development and urban expansion between 2000 and 2010. It was further noted that most changes occurred in cropland areas due to urban encroachment.

Implications of Land Use/Land Cover Change in the Buffer Zone of a National Park in the Tropical Andes

The impacts of land use and land cover (LULC) change in buffer zones surrounding protected ecological reserves have important implications for the management and conservation of these protected areas. This study examines the spatial and temporal patterns of LULC change along the boundary of Rio Abiseo National Park in the Northern Peruvian Andes. Landscape change within four ecological zones was evaluated based on trends expected to occur between 1987 and 2001. Landsat TM and ETM imagery were used to produce LULC classification maps for both years using a hybrid supervised/unsupervised approach. LULC changes were measured using landscape metrics and from-to change maps created by post-classification change detection. Contrary to expectations, tropical upper wet montane forest increased despite being threatened by human-induced fires and cattle grazing of the highland grasslands inside the park. Within the park's buffer zone, tropical moist forest remnants were fragmented into more numerous and smaller patches between 1987 and 2001; this was in part due to conversion into agricultural land. The methods used in this study provide an effective way to monitor LULC change detection and support the management of protected areas and their surrounding environments.