Special Issue "Remote Sensing of Land Use/Cover Changes Using Very High Resolution Satellite Data"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editors

Dr. Garik Gutman
Website
Guest Editor
NASA Headquarters, Washington DC, USA
Interests: Remote sensing of land use/cover change; land-atmospheric interactions; big-data processing; remote sensing of the environment.
Special Issues and Collections in MDPI journals
Dr. Krishna Prasad Vadrevu
Website
Guest Editor
1. NASA Marshall Space Flight Center, Huntsville, Alabama, USA
2. Adjunct Professor, Department of Geographical Sciences, University of Maryland College Park, USA.
Interests: satellite remote sensing of land use/cover changes; land atmospheric interactions; remote sensing of fires; biogeochemical cycling; agroecosystems.
Special Issues and Collections in MDPI journals
Prof. Dr. Chris Justice
Website
Guest Editor
Dept. of Geographical Sciences, University of Maryland College Park, USA
Interests: global change research; land use/cover change; satellite-based agriculture monitoring; satellite-based fire monitoring; terrestrial observing systems/remote sensing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear colleagues,

Land-use/cover change (LU/CC) is one of the most important types of environmental change occurring in many regions of the world. It is widely accepted that LU/CC is in large part driven by demographic changes, e.g., population growth or migration, and economic changes or governmental policies. The most common forms of LU/CC include peri-urban expansion, agricultural land conversion/loss, land abandonment, deforestation, logging, and reforestation. The drivers of LU/CC vary widely in different regions of the world and include factors such as land tenure, local economic development, government policies, changing agricultural practices, inappropriate land management, land speculation, improved transport networks, etc. Variability in the regional weather and climate, and socioeconomic factors also drive LU/CC often resulting in significant impacts on biogeochemical cycles, hydrological cycle, radiation, and surface energy fluxes. Documenting the LU/CC and the associated impacts is gaining regional significance, as this knowledge can be useful for improved land management. Satellite remote sensing due to its large-scale, multi-temporal, multi-spectral, and repetitive coverage capabilities can be effectively used to document LU/CC and associated impacts.

A number of commercial companies such as Planet Labs and Maxar/Digital Globe have been acquiring remote sensing very high resolution (VHR) data useful for LU/CC applications. VHR observations increase our capabilities in extracting land-cover/use fine features. Planet’s constellation consists of over 150 satellites providing spectral observations daily with PLANETSCOPE (RGB and NIR), RAPIDEYE (RGB, red edge and NIR), and SKYSAT (RGB, NIR, and PAN) satellites with 3m, 5m, and 0.8m resolutions respectively. Currently, DigitalGlobe operates four satellites: GeoEye-1, WorldView-1, -2, and -3. Combination of GeoEye-1, which can revisit any point on Earth once every three days, and WorldView constellation makes the frequency of Digital Globe VHR data useful for short-term LU/CC monitoring at 1-m resolution (or higher for panchromatic bands).

This Special Issue invites articles that highlight the integration of VHR data with novel algorithms, e.g., using Machine Learning approaches, which could include deep learning and data mining for LU/CC mapping, monitoring and impact assessment studies, such as the following:

  • Forest disturbance mapping and changes
  • Agricultural monitoring that would include remote sensing of crop growth stages, crop production, farming practices, and impacts on water/energy balance
  • Urbanization and associated impacts (urban heat island effect, air and water pollution, etc.)
  • Monitoring fires, biomass burning, and its impacts
  • Mapping and monitoring of land management practices, disturbances, and interactions
  • New tools and methods for fusing VHR and moderate resolution data

The current call for papers is targeting NASA-funded researchers who have been using VHR data in LU/CC research and applications. The issue is open for non-NASA (and non-US) researchers if the critical mass of accepted papers is not reached. Potential non-NASA authors may contact Guest Editors for further inquiries.

Dr. Garik Gutman
Dr. Krishna Prasad Vadrevu
Prof. Dr. Chris Justice
Guest Editor

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 papers will be 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 2200 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

  • Very high-resolution satellite data
  • Land use/cover change
  • Machine learning and data fusion
  • Land degradation
  • Land use change impacts

Published Papers (2 papers)

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Open AccessArticle
Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA): A Scalable Open Source Method for Land Cover Monitoring Using Data Fusion
Remote Sens. 2020, 12(20), 3459; https://doi.org/10.3390/rs12203459 - 21 Oct 2020
Abstract
The increasing availability of very-high resolution (VHR; <2 m) imagery has the potential to enable agricultural monitoring at increased resolution and cadence, particularly when used in combination with widely available moderate-resolution imagery. However, scaling limitations exist at the regional level due to big [...] Read more.
The increasing availability of very-high resolution (VHR; <2 m) imagery has the potential to enable agricultural monitoring at increased resolution and cadence, particularly when used in combination with widely available moderate-resolution imagery. However, scaling limitations exist at the regional level due to big data volumes and processing constraints. Here, we demonstrate the Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA), using a suite of open source software capable of efficiently characterizing time-series field-scale statistics across large geographical areas at VHR resolution. We provide distinct implementation examples in Vietnam and Senegal to demonstrate the approach using WorldView VHR optical, Sentinel-1 Synthetic Aperture Radar, and Sentinel-2 and Sentinel-3 optical imagery. This distributed software is open source and entirely scalable, enabling large area mapping even with modest computing power. FARMA provides the ability to extract and monitor sub-hectare fields with multisensor raster signals, which previously could only be achieved at scale with large computational resources. Implementing FARMA could enhance predictive yield models by delineating boundaries and tracking productivity of smallholder fields, enabling more precise food security observations in low and lower-middle income countries. Full article
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Remote Sensing of Coconut Trees in Tonga Using Very High Spatial Resolution WorldView-3 Data
Remote Sens. 2020, 12(19), 3113; https://doi.org/10.3390/rs12193113 - 23 Sep 2020
Abstract
This paper presents a simple and efficient image processing method for estimating the number of coconut trees in the Tonga region using very high spatial resolution data (30 cm) in the blue, green, red and near infrared spectral bands acquired by the WorldView-3 [...] Read more.
This paper presents a simple and efficient image processing method for estimating the number of coconut trees in the Tonga region using very high spatial resolution data (30 cm) in the blue, green, red and near infrared spectral bands acquired by the WorldView-3 sensor. The method is based on the detection of tree shadows and the further analysis to reject false detection using geometrical properties of the derived segments. The algorithm is evaluated by comparing coconut tree counts derived by an expert through photo-interpretation over 57 randomly distributed (4% sampling rate) segments of 200 m × 200 m over the Vaini region of the Tongatapu island. The number of detected trees agreed within 5% versus validation data. The proposed method was also evaluated over the whole Tonga archipelago by comparing satellite-derived estimates to the 2015 agricultural census data—the total tree counts for both Tonga and Tongatapu agreed within 3%. Full article
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