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Remote Sensing Applications in Land Use and Land Cover Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (26 April 2024) | Viewed by 12045

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Special Issue Editors

Dr. Sananda Kundu

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Guest Editor

Department of Geography, Manipur University, Canchipur, Imphal 795003, Manipur, India
Interests: remote sensing; hydrology; climate change; land use classification and change modeling; evapotranspiration; flood
Special Issues, Collections and Topics in MDPI journals

Dr. Arun Mondal

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Guest Editor

School of Earth, Ocean and Environment, University of South Carolina, Columbia, SC 29208, USA
Interests: land use change modeling; soil erosion; climate change; water balance; flood inundation modeling; snow cover change; remote sensing and GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research in land use and land cover (LULC) change at a global scale has become important with the increased human intervention impacting the use of natural resources as well as future changes associated with it. LULC changes are also becoming increasingly dominant in environment and climate change studies. Thoughtful planning and management form the basis of development and can improve the pattern of changes in land use, influencing human wellbeing. This is possible through rigorous research in this and associated fields. Moreover, the influence of LULC changes on various factors, such as alterations in the hydrological processes, ecosystems, climate, urban areas, etc., has been the focus of many research works for years. However, these changes are becoming increasingly complex and pose challenges for researchers in the LULC field as they are integrated with various natural and anthropogenic factors.

Remote sensing applications have aided in the understanding and analysis of the complexities of LULC changes and their interactions with various physical processes on Earth. The use of high-resolution satellite data and hybrid approaches to detect changes in the Earth’s objects is providing a breakthrough in the field. LULC has been the main focus of many research works as it alters various hydrological processes and is related to surface energy fluxes and changes in climatic processes. Anthropogenic impacts also add a different insight to LULC changes. Hence, understanding these effects is considered notable in many studies.

This Special Issue is aimed at collecting methodological contributions and land use modeling using various remote sensing techniques and emphasizing the integration of LULC change with other associating factors. The main focus areas include (but are not limited to):

Remote sensing and in situ observation and land use change;
Use of hybrid data and methodology for high LULC accuracy;
Land use degradation and land suitability;
Machine learning algorithms using satellite data;
Climate change impact on LULC/agriculture and future projection;
Application on agriculture, forest, water resources, urban area studies, etc.;
Integrated LULC–hydrology–crop production.

Dr. Sananda Kundu
Dr. Arun Mondal
Guest Editors

Manuscript Submission Information

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Keywords

high accuracy of land use
hybrid methods and approach
hydrology and land degradation
high-resolution satellite data

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Published Papers (6 papers)

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Research

23 pages, 31972 KiB

Open AccessArticle

Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach

by Tian Tian, Le Yu, Ying Tu, Bin Chen and Peng Gong

Remote Sens. 2024, 16(17), 3125; https://doi.org/10.3390/rs16173125 - 24 Aug 2024

Viewed by 1381

Abstract

Accurate, detailed, and long-term urban land use mapping is crucial for urban planning, environmental assessment, and health evaluation. Despite previous efforts, mapping essential urban land use categories (EULUCs) across multiple periods remains challenging, primarily due to the scarcity of enduring consistent socio-geographical data, such as the widely used Point of Interest (POI) data. Addressing this issue, this study presents an experimental method for mapping the time-series of EULUCs in Dalian city, China, utilizing Local Climate Zone (LCZ) data as a substitute for POI data. Leveraging multi-source geospatial big data and the random forest classifier, we delineate urban land use distributions at the parcel level for the years 2000, 2005, 2010, 2015, 2018, and 2020. The results demonstrate that the generated EULUC maps achieve promising classification performance, with an overall accuracy of 78% for Level 1 and 71% for Level 2 categories. Features derived from nighttime light data, LCZ, Sentinel-2 satellite imagery, and topographic data play leading roles in our land use classification process. The importance of LCZ data is second only to nighttime light data, achieving comparable classification accuracy to that when using POI data. Our subsequent correlation analysis reveals a significant correlation between POI and LCZ data (p = 0.4), which validates the rationale of the proposed framework. These findings offer valuable insights for long-term urban land use mapping, which can facilitate effective urban planning and resource management in the near future. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Use and Land Cover Monitoring)

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16 pages, 8324 KiB

Open AccessArticle

Land-Use and Land-Cover Changes in Cottbus City and Spree-Neisse District, Germany, in the Last Two Decades: A Study Using Remote Sensing Data and Google Earth Engine

by Rezwan Ahmed, Md. Abu Zafor and Katja Trachte

Remote Sens. 2024, 16(15), 2773; https://doi.org/10.3390/rs16152773 - 29 Jul 2024

Viewed by 1253

Abstract

Regular detection of land-use and land-cover (LULC) changes with high accuracy is necessary for natural resources management and sustainable urban planning. The produced LULC maps from Google Earth Engine (GEE) also illustrate the transformation of the LULC for the respective landscape over time. The selected study area, Cottbus City and the Spree-Neisse district in northeastern Germany, has undergone significant development over the past decades due to various factors, including urbanization and industrialization; also, the landscape has been converted in some areas for post-mining activities. Detection of LULC changes that have taken place over the last few decades thus plays a vital role in quantifying the impact of these factors while improving the knowledge of these developments and supporting the city planners or urban management officials before implementing further long-term development initiatives for the future. Therefore, the study aims to (i) detect LULC changes for the time slices 2002 and 2022, testing machine learning (ML) algorithms in supervised and unsupervised classification for Landsat satellite imageries, and (ii) validate the newly produced LULC maps with the available regional database (RDB) from the federal and state statistical offices, Germany, and the Dynamic World (DW) near real-time 10 m global LULC data set powered by artificial intelligence (AI). The results of the Random Forest (RF) and the Smilecart classifiers of supervised classification using Landsat 9 OLI-2/TIRS-2 in 2022 demonstrated a validation accuracy of 88% for both, with Kappa Index (KI) of 83% and 84%, respectively. Moreover, the Training Overall Accuracy (TOA) was 100% for both years. The wekaKMeans cluster of the unsupervised classification also illustrated a similar transformation pattern in the LULC maps. Overall, the produced LULC maps offered an improved representation of the selected region’s various land-cover classes (i.e., vegetation, waterbodies, built areas, and bare ground) in the last two decades (20022 to 2022). Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Use and Land Cover Monitoring)

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21 pages, 2157 KiB

Open AccessArticle

AMFNet: Attention-Guided Multi-Scale Fusion Network for Bi-Temporal Change Detection in Remote Sensing Images

by Zisen Zhan, Hongjin Ren, Min Xia, Haifeng Lin, Xiaoya Wang and Xin Li

Remote Sens. 2024, 16(10), 1765; https://doi.org/10.3390/rs16101765 - 16 May 2024

Cited by 3 | Viewed by 1362

Abstract

Change detection is crucial for evaluating land use, land cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity of image content, poses challenges for traditional change detection algorithms in terms of accuracy and applicability. The recent emergence of deep learning methods has led to substantial progress in the field of change detection. However, existing frameworks often involve the simplistic integration of bi-temporal features in specific areas, lacking the fusion of temporal information and semantic details in the images. In this paper, we propose an attention-guided multi-scale fusion network (AMFNet), which effectively integrates bi-temporal image features and diverse semantics at both the encoding and decoding stages. AMFNet utilizes a unique attention-guided mechanism to dynamically adjust feature fusion, enhancing adaptability and accuracy in change detection tasks. Our method intelligently incorporates temporal information into the deep learning model, considering the temporal dependency inherent in these tasks. We decode based on an interactive feature map, which improves the model’s understanding of evolving patterns over time. Additionally, we introduce multi-level supervised training to facilitate the learning of fused features across multiple scales. In comparison with different algorithms, our proposed method achieves F1 values of 0.9079, 0.8225, and 0.8809 in the LEVIR-CD, GZ-CD, and SYSU-CD datasets, respectively. Our model outperforms the SOTA model, SAGNet, by 0.69% in terms of F1 and 1.15% in terms of IoU on the LEVIR-CD dataset, by 2.8% in terms of F1 and 1.79% in terms of IoU on the GZ-CD dataset, and by 0.54% in terms of F1 and 0.38% in terms of IoU on the SYSU-CD dataset. The method proposed in this study can be applied to various complex scenarios, establishing a change detection method with strong model generalization capabilities. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Use and Land Cover Monitoring)

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18 pages, 23742 KiB

Open AccessArticle

A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images

by Wei Wang, Yong Cheng, Zhoupeng Ren, Jiaxin He, Yingfen Zhao, Jun Wang and Wenjie Zhang

Remote Sens. 2023, 15(23), 5472; https://doi.org/10.3390/rs15235472 - 23 Nov 2023

Cited by 2 | Viewed by 1536

Abstract

The comprehensive use of high-resolution remote sensing (HRS) images and deep learning (DL) methods can be used to further accurate urban green space (UGS) mapping. However, in the process of UGS segmentation, most of the current DL methods focus on the improvement of the model structure and ignore the spectral information of HRS images. In this paper, a multiscale attention feature aggregation network (MAFANet) incorporating feature engineering was proposed to achieve segmentation of UGS from HRS images (GaoFen-2, GF-2). By constructing a new decoder block, a bilateral feature extraction module, and a multiscale pooling attention module, MAFANet enhanced the edge feature extraction of UGS and improved segmentation accuracy. By incorporating feature engineering, including false color image and the Normalized Difference Vegetation Index (NDVI), MAFANet further distinguished UGS boundaries. The UGS labeled datasets, i.e., UGS-1 and UGS-2, were built using GF-2. Meanwhile, comparison experiments with other DL methods are conducted on UGS-1 and UGS-2 to test the robustness of the MAFANet network. We found the mean Intersection over Union (MIOU) of the MAFANet network on the UGS-1 and UGS-2 datasets was 72.15% and 74.64%, respectively; outperforming other existing DL methods. In addition, by incorporating false color image in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.64%; by incorporating vegetation index (NDVI) in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.09%; and by incorporating false color image and the vegetation index (NDVI) in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.73%. Our experimental results demonstrated that the proposed MAFANet incorporating feature engineering (false color image and NDVI) outperforms the state-of-the-art (SOTA) methods in UGS segmentation, and the false color image feature is better than the vegetation index (NDVI) for enhancing green space information representation. This study provided a practical solution for UGS segmentation and promoted UGS mapping. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Use and Land Cover Monitoring)

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18 pages, 8194 KiB

Open AccessArticle

by Gabriel Alarcon-Aguirre, Maritza Mamani Mamani, Rembrandt Ramiro Canahuire-Robles, Telesforo Vasquez Zavaleta, Joel Peña Valdeiglesias, Jorge Diaz Revoredo, Liset Rodríguez Achata, Dalmiro Ramos Enciso and Jorge Garate-Quispe

Remote Sens. 2023, 15(23), 5438; https://doi.org/10.3390/rs15235438 - 21 Nov 2023

Cited by 1 | Viewed by 1642

Abstract

Madre de Dios is considered an important center of biodiversity in Peru due to its extensive Amazonian forests. However, the forests are under growing pressure due to land invasion, agricultural expansion, and gold mining. This makes support for forest management very important. This study aimed to evaluate the relationship between forest loss, land cover, land-use changes, and Brazil nut (Bertholletia excelsa Humb. & Bonpl) production in forest concessions in the Peruvian Amazon (2004–2020). Remote sensing techniques were used to classify images using the random forest algorithm, which was applied to the Landsat-5 thematic mapper, Landsat-7 enhanced thematic mapper, and Landsat-8 operational land imagery. Brazil nut production data from 2004–2020 was provided by the Regional Forest and Wildlife Service of Madre de Dios. In forest concessions, the forest area decreased continuously over the whole study period (160.11 ha/year). During the same time period, the change in forest cover in the concessions from Brazil nut to other uses was 4681 ha. At the same time, the authorization and extraction of Brazil nuts varied during the study period but did not show a downward trend. We found a significant and inverse relationship between the conversion of forest to agricultural land and Brazil nut production. However, there were insignificant relationships between forest loss, the persistence of agricultural and forest areas, and Brazil nut production. Therefore, despite the forest loss in the forest concession areas, Brazil nut production has not decreased. Production may not be affected because land pressure is higher near access roads, affecting only the areas near the roads rather than the actual areas where the Brazil nut-producing trees are located. Our results showed that nut production in non-timber forest product concessions would be negatively affected by deforestation and forest degradation, but only slightly. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Use and Land Cover Monitoring)

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24 pages, 28388 KiB

Open AccessEditor’s ChoiceArticle

Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data in an Agricultural Watershed

by Padmanava Dash, Scott L. Sanders, Prem Parajuli and Ying Ouyang

Remote Sens. 2023, 15(16), 4020; https://doi.org/10.3390/rs15164020 - 14 Aug 2023

Cited by 15 | Viewed by 3569

Abstract

Classification of remotely sensed imagery for reliable land use and land cover (LULC) remains a challenge in areas where spectrally similar LULC features occur. For example, bare soils of harvested crop fields in agricultural watersheds exhibit spectral characteristics similar to high-intensity developed regions and impede an accurate classification. The goal of this study is to improve the accuracy of LULC classification of satellite imagery for the Big Sunflower River Watershed, Mississippi using ancillary data, multiple classification methods, and a post-classification correction (PCC). To determine the best approach, the methodology was applied to Landsat 8 Operational Land Imager (OLI) imagery during the growing season and post-harvest. Imagery for the growing season was acquired on 25 August 2015, and post-harvest was acquired on 7 January 2018. Three classification methods were applied: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). LULC imagery was classified as open water, woody wetlands, harvested crop, rangeland, cultivated crop, high-intensity developed, and mid-low intensity developed areas. Ancillary data such as normalized difference vegetation index (NDVI), thematic maps of urban areas, river networks, transportation networks, high-resolution National Agriculture Imagery Program (NAIP) imagery, Google Earth time-series data, and phenology were used to determine the training dataset. Initially none of the three classification methods performed adequately. Hence, a post-classification correction (PCC) was implemented by masking and applying a majority filter using thematic maps of urban areas. Once PCC was implemented, the accuracies from each of the classification methods increased significantly with the SVM classification method performing best in both the growing season and post-harvest with an overall classification accuracy of 93.5% with a Kappa statistic of 0.88 in the post-harvest imagery and an overall classification accuracy of 84% with a Kappa statistic of 0.789 in the imagery from the growing season. It was found that SVM was the best classification method while PCC is an effective strategy to implement when dealing with spectrally similar LULC features. The use of SVM together with PCC increased the reliability of the information extracted. Strategies from this study can help to evaluate the LULC in agricultural and other watersheds. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Use and Land Cover Monitoring)

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