Comprehensive review on land use/land cover change classification in remote sensing

Abstract

Research in the field of remote sensing of the environment is valuable and informative. Hyperspectral (HSP) and multispectral (MSP) satellite images have been used for different remote sensing applications. Land Use/Land Cover (LU/LC) change classification has been considered as important research in the field of remote sensing environment. This review aims to identify the various LU/LC applications, remote sensing satellites, geospatial software, pre-processing techniques, LU/LC classification, clustering, spectral unmixing, landscape change models and evaluation metrics. The main objective of this review is to present the more frequently used techniques for analysing LU/LC change with MSP and HSP satellite images. An aim of this review is to motivate future researchers to work efficiently with MSP and HSP satellite images in the field of remote sensing.

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

“…In general, machine learning approaches have high potential to capture the non-linear relationship between remote sensing data and vegetation parameters and have the capability of integrating multisource information at different levels (Yao, Qin, and Chen, 2019). In addition, the results acquired confirmed the importance of using machine learning algorithms in remote sensing vegetation classification, due to their powerful adaptation, self-learning, and parallel processing capabilities (Navin and Agilandeeswari, 2020;Meng et al, 2021;. Therefore, some challenges need to be emphasized and both the hardware and software of UAS remote sensing technology require improvements: [1] The endurance of UAS is relatively limited, the flight stability is not strong enough in areas with large terrain fluctuation and the lack of flight altitude limits the image size; [2] Although more lightweight and smaller sensor systems have become available, such as hyperspectral and LiDAR sensors, but they are still expensive; [3] The integration between UAS platforms and sensors requires improvement, e.g., most of the multispectral, hyperspectral, and thermal sensors are built independent of the UAV platform, so, need an extra GPS module and, also, UAS are often equipped with a single sensor, multisensor integration is beneficial to improve monitoring accuracy and efficiency; [4] The mosaic workload is significantly higher than satellite imagery, which takes up more time for image processing, resulting in the need to develop more robust algorithms, like deep learning techniques, in addition, the technology of mass data processing needs to be improved due to the richness and variety of data obtained; [5] The most vegetation classifications via UAS require human participation and interpretation, indicating that the combination between UAS Frontiers in Environmental Science frontiersin.org remote sensing with ground data and satellite data needs to be strengthened; if the dataset used for training is extent, computer learning techniques would generate a satisfactory classification outcome; [6] The use of UAS images to monitor tropical savannas leaf phenology is a challenge due to difficult methods to extract accurate quantitative phenology estimates under variable lighting and viewing conditions; [7] The application scenarios of UAS remote sensing in grassland ecosystem monitoring need to be expanded and deepened, mainly in animal investigation and soil physical and chemical monitoring; and also, the correlation between the scientific research of UAS remote sensing monitoring and practical decision making of grassland management is still insufficient (Neumann et al, 2019;Park et al, 2019;Lyu et al, 2020;Sun et al, 2021).…”

The impact of UiTM Cawangan Pulau Pinang Permatang Pauh campus on urban development in Central Seberang Perai District

“…The data were downloaded from the U.S. Geological Survey (USGS) website, https://earthexplorer.usgs.gov/ in the WGS84 coordinate system, and all the bands were combined using a layer stacking method in ERDAS Imagine software. Later, [14,[20][21][22] to classify the land cover area surrounding the campus. The satellite imageries were subset and classified into nine (9) types of land-cover within 5 km and 10 km offset from the boundary of UiTMCPP Permatang Pauh campus.…”

Novel Vision Transformer–Based Bi-LSTM Model for LU/LC Prediction—Javadi Hills, India

“…The expressions are shown in Equations ( 17) and (18). By using the results of TOA brightness temperature, emitted radiance wavelength, and LSE, the LST was calculated and is shown in Equation (19).…”

“…Based on the accuracy assessment, the performance of the classification method has been measured. The LU/LC change detection has been performed between the LU/LC time-series classified map [18][19][20][21][22].…”