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

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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 (31 March 2021) | Viewed by 31934

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Special Issue Editors

Dr. Garik Gutman

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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, Collections and Topics in MDPI journals

Dr. Krishna Vadrevu

E-Mail Website
Guest Editor

Earth Science Office, NASA Marshall Space Flight Center, Huntsville, AL, USA
Interests: remote sensing and GIS applications; land cover/land use changes; land-atmosphere interactions; satellite remote sensing of fires; biogeochemical cycling; biodiversity and ecology; agroecosystems and sustainability
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chris Justice

E-Mail Website
Guest Editor

Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Interests: global change research; land use/cover change; satellite-based agriculture monitoring; satellite-based fire monitoring; terrestrial observing systems/remote sensing
Special Issues, Collections and Topics 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 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

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

Published Papers (6 papers)

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Research

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15 pages, 5189 KiB

Open AccessCommunication

Using Multi-Resolution Satellite Data to Quantify Land Dynamics: Applications of PlanetScope Imagery for Cropland and Tree-Cover Loss Area Estimation

by Jeffrey Pickering, Alexandra Tyukavina, Ahmad Khan, Peter Potapov, Bernard Adusei, Matthew C. Hansen and André Lima

Remote Sens. 2021, 13(11), 2191; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112191 - 04 Jun 2021

Cited by 17 | Viewed by 4511

Abstract

The Planet constellation of satellites represents a significant advance in the availability of high cadence, high spatial resolution imagery. When coupled with a targeted sampling strategy, these advances enhance land-cover and land-use monitoring capabilities. Here we present example regional and national-scale area-estimation methods as a demonstration of the integrated and efficient use of mapping and sampling using public medium-resolution (Landsat) and commercial high resolution (PlanetScope) imagery. Our proposed method is agnostic to the geographic region and type of land cover and change, which is demonstrated by applying the method across two very different geographies and thematic classes. Wheat extent is estimated in Punjab, Pakistan, for the 2018/2019 growing season, and tree-cover loss area is estimated over Peru for 2017 and 2018. We used a time series of PlanetScope imagery to classify a sample of 5 × 5 km blocks for each region and produce area estimates of 55,947 km² (±9.0%) of wheat in Punjab and 5398 km² (±9.1%) of tree-cover loss in Peru. We also demonstrate the use of regression estimation utilizing population information from Landsat-based maps to reduce standard errors of the sample-based estimates. Resulting regression estimates have SEs of 3.6% and 5.1% for Pakistan and Peru, respectively. The combination of daily global coverage and high spatial resolution of Planet imagery improves our ability to monitor crop phenology and capture ephemeral tree-cover loss and degradation dynamics, while Landsat-based maps provide wall-to-wall information to target the sample and increase precision of the estimates through the use of regression estimation. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use/Cover Changes Using Very High Resolution Satellite Data)

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13 pages, 5725 KiB

Open AccessArticle

Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms

by Preeti Rao, Weiqi Zhou, Nishan Bhattarai, Amit K. Srivastava, Balwinder Singh, Shishpal Poonia, David B. Lobell and Meha Jain

Remote Sens. 2021, 13(10), 1870; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101870 - 11 May 2021

Cited by 36 | Viewed by 5860

Abstract

Remote sensing offers a way to map crop types across large spatio-temporal scales at low costs. However, mapping crop types is challenging in heterogeneous, smallholder farming systems, such as those in India, where field sizes are often smaller than the resolution of historically available imagery. In this study, we examined the potential of relatively new, high-resolution imagery (Sentinel-1, Sentinel-2, and PlanetScope) to identify four major crop types (maize, mustard, tobacco, and wheat) in eastern India using support vector machine (SVM). We found that a trained SVM model that included all three sensors led to the highest classification accuracy (85%), and the inclusion of Planet data was particularly helpful for classifying crop types for the smallest farms (<600 m²). This was likely because its higher spatial resolution (3 m) could better account for field-level variations in smallholder systems. We also examined the impact of image timing on the classification accuracy, and we found that early-season images did little to improve our models. Overall, we found that readily available Sentinel-1, Sentinel-2, and Planet imagery were able to map crop types at the field-scale with high accuracy in Indian smallholder systems. The findings from this study have important implications for the identification of the most effective ways to map crop types in smallholder systems. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use/Cover Changes Using Very High Resolution Satellite Data)

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14 pages, 3701 KiB

Open AccessArticle

Assessing Deep Convolutional Neural Networks and Assisted Machine Perception for Urban Mapping

by Yang Shao, Austin J. Cooner and Stephen J. Walsh

Remote Sens. 2021, 13(8), 1523; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081523 - 15 Apr 2021

Cited by 4 | Viewed by 2436

Abstract

High-spatial-resolution satellite imagery has been widely applied for detailed urban mapping. Recently, deep convolutional neural networks (DCNNs) have shown promise in certain remote sensing applications, but they are still relatively new techniques for general urban mapping. This study examines the use of two DCNNs (U-Net and VGG16) to provide an automatic schema to support high-resolution mapping of buildings, road/open built-up, and vegetation cover. Using WorldView-2 imagery as input, we first applied an established OBIA method to characterize major urban land cover classes. An OBIA-derived urban map was then divided into a training and testing region to evaluate the DCNNs’ performance. For U-Net mapping, we were particularly interested in how sample size or the number of image tiles affect mapping accuracy. U-Net generated cross-validation accuracies ranging from 40.5 to 95.2% for training sample sizes from 32 to 4096 image tiles (each tile was 256 by 256 pixels). A per-pixel accuracy assessment led to 87.8 percent overall accuracy for the testing region, suggesting U-Net’s good generalization capabilities. For the VGG16 mapping, we proposed an object-based framing paradigm that retains spatial information and assists machine perception through Gaussian blurring. Gaussian blurring was used as a pre-processing step to enhance the contrast between objects of interest and background (contextual) information. Combined with the pre-trained VGG16 and transfer learning, this analytical approach generated a 77.3 percent overall accuracy for per-object assessment. The mapping accuracy could be further improved given more robust segmentation algorithms and better quantity/quality of training samples. Our study shows significant promise for DCNN implementation for urban mapping and our approach can transfer to a number of other remote sensing applications. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use/Cover Changes Using Very High Resolution Satellite Data)

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18 pages, 7393 KiB

Open AccessEditor’s ChoiceArticle

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

by Sergii Skakun, Natacha I. Kalecinski, Meredith G. L. Brown, David M. Johnson, Eric F. Vermote, Jean-Claude Roger and Belen Franch

Remote Sens. 2021, 13(5), 872; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050872 - 26 Feb 2021

Cited by 45 | Viewed by 7108

Abstract

Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R²) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R² was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields. Full article

(This article belongs to the Special Issue Remote Sensing of Land Use/Cover Changes Using Very High Resolution Satellite Data)

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22 pages, 15554 KiB

Open AccessArticle

Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA): A Scalable Open Source Method for Land Cover Monitoring Using Data Fusion

by Nathan Thomas, Christopher S. R. Neigh, Mark L. Carroll, Jessica L. McCarty and Pete Bunting

Remote Sens. 2020, 12(20), 3459; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203459 - 21 Oct 2020

Cited by 4 | Viewed by 4800

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 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

(This article belongs to the Special Issue Remote Sensing of Land Use/Cover Changes Using Very High Resolution Satellite Data)

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Other

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8 pages, 4615 KiB

Open AccessLetter

Remote Sensing of Coconut Trees in Tonga Using Very High Spatial Resolution WorldView-3 Data

by Eric F. Vermote, Sergii Skakun, Inbal Becker-Reshef and Keiko Saito

Remote Sens. 2020, 12(19), 3113; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193113 - 23 Sep 2020

Cited by 16 | Viewed by 4955

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 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

(This article belongs to the Special Issue Remote Sensing of Land Use/Cover Changes Using Very High Resolution Satellite Data)

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