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Remote Sensing
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Remote Sensing Applications in Land Use and Land Cover Monitoring
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Remote Sensing Applications in Land Use and Land Cover Monitoring

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

Deadline for manuscript submissions: 26 April 2024 | Viewed by 4605

Special Issue Editors

Dr. Sananda Kundu

E-Mail Website
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

E-Mail Website
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (3 papers)

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Research

18 pages, 23742 KiB  
Article
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
Viewed by 777
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 [...] Read more.
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  
Article
Forest Loss Related to Brazil Nut Production in Non-Timber Forest Product Concessions in a Micro-Watershed in the Peruvian Amazon
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
Viewed by 916
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 [...] Read more.
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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Graphical abstract

24 pages, 28388 KiB  
Article
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 4 | Viewed by 1975
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 [...] Read more.
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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