Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges

Navin, M. Sam; Agilandeeswari, L.; Manoharan, Prabukumar

doi:10.1007/s11356-020-09091-7

Cited by 125 publications

(56 citation statements)

References 71 publications

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“…Currently, large field surveys are conducted to obtain information about land cover [13,14]. A manual analysis of large fields is a laborious and time-consuming job and often misses valuable information about the land cover [15,16].…”

Section: Introductionmentioning

confidence: 99%

Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images

2021

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Land cover semantic segmentation in high-spatial resolution satellite images plays a vital role in efficient management of land resources, smart agriculture, yield estimation and urban planning. With the recent advancement in remote sensing technologies, such as satellites, drones, UAVs, and airborne vehicles, a large number of high-resolution satellite images are readily available. However, these high-resolution satellite images are complex due to increased spatial resolution and data disruption caused by different factors involved in the acquisition process. Due to these challenges, an efficient land-cover semantic segmentation model is difficult to design and develop. In this paper, we develop a hybrid deep learning model that combines the benefits of two deep models, i.e., DenseNet and U-Net. This is carried out to obtain a pixel-wise classification of land cover. The contraction path of U-Net is replaced with DenseNet to extract features of multiple scales, while long-range connections of U-Net concatenate encoder and decoder paths are used to preserve low-level features. We evaluate the proposed hybrid network on a challenging, publicly available benchmark dataset. From the experimental results, we demonstrate that the proposed hybrid network exhibits a state-of-the-art performance and beats other existing models by a considerable margin.

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Section: Introductionmentioning

confidence: 99%

Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images

2021

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“…In LULC change analysis, a lack of accurate GIS data is a big issue, especially in developing countries, as accurate, long-time-series data availability is a must. To date, remote sensing images have been the most widely and efficiently used data [29]. The recent developments in artificial intelligence and cloud computing, i.e., Microsoft Azure Cloud [30], Google Earth Engine (GEE) [31], Geospatial Amazon Web Services [32,33], and Open Data Cube [34], have made it easier to derive LULC classes from remotely sensed data.…”

Section: Introductionmentioning

confidence: 99%

Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine

Bình¹,

Nhut²,

An³

et al. 2021

IJGI

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The main purpose of this paper is to assess the land use and land cover (LULC) changes for thirty years, from 1990–2020, in the Dong Thap Muoi, a flooded land area of the Mekong River Delta of Vietnam using Google Earth Engine and random forest algorithm. The specific purposes are: (1) determine the main LULC classes and (2) compute and analyze the magnitude and rate of changes for these LULC classes. For the above purposes, 128 Landsat images, topographic maps, land use status maps, cadastral maps, and ancillary data were collected and utilized to derive the LULC maps using the random forest classification algorithm. The overall accuracy of the LULC maps for 1990, 2000, 2010, and 2020 are 88.9, 83.5, 87.1, and 85.6%, respectively. The result showed that the unused land was dominant in 1990 with 28.9 % of the total area, but it was primarily converted to the paddy, a new dominant LULC class in 2020 (45.1%). The forest was reduced significantly from 14.4% in 1990 to only 5.5% of the total area in 2020. Whereas at the same time, the built-up increased from 0.3% to 6.2% of the total area. This research may help the authorities design exploitation policies for the Dong Thap Muoi’s socio-economic development and develop a new, stable, and sustainable ecosystem, promoting the advantages of the region, early forming a diversified agricultural structure.

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“…Multispectral images are primarily applied in detection of urban land-use changes, rather than hyperspectral images [34,35]. Digital data in the form of satellite images enable accurate computing of various LULC categories and help maintain the spatial data infrastructure, which is essential for monitoring urban expansion and for land-use studies [36].…”

Section: Introductionmentioning

confidence: 99%

Surface Runoff Responses to Suburban Growth: An Integration of Remote Sensing, GIS, and Curve Number

Jahan

Pradhanang

Bhuiyan

2021

Land

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Suburban growth and its impacts on surface runoff were investigated using the soil conservation service curve number (SCS-CN) model, compared with the integrated advanced remote sensing and geographic information system (GIS)-based integrated approach, over South Kingston, Rhode Island, USA. This study analyzed and employed the supervised classification method on four Landsat images from 1994, 2004, 2014, and 2020 to detect land-use pattern changes through remote sensing applications. Results showed that 68.6% urban land expansion was reported from 1994 to 2020 in this suburban area. After land-use change detection, a GIS-based SCS-CN model was developed to examine suburban growth and surface runoff estimation. The developed model demonstrated the spatial distribution of runoff for each of the studied years. The results showed an increasing spatial pattern of 2% to 10% of runoff from 1994 to 2020. The correlation between runoff co-efficient and rainfall indicated the significant impact of suburban growth in surface runoff over the last 36 years in South Kingstown, RI, USA, showing a slight change of forest (8.2% area of the total area) and agricultural land (4.8% area of the total area). Suburban growth began after 2000, and within 16 years this land-use change started to show its substantial impact on surface runoff. We concluded that the proposed integrated approach could classify land-use and land cover information to understand suburban growth and its potential impact on the area.

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Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges

Cited by 125 publications

References 71 publications

Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images

Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images

Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine

Surface Runoff Responses to Suburban Growth: An Integration of Remote Sensing, GIS, and Curve Number

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