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Land Surface Feature Extraction from High-Resolution Remote Sensing Imagery
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Land Surface Feature Extraction from High-Resolution Remote Sensing Imagery

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 6242

Special Issue Editors

Dr. Ying Zhang

E-Mail Website
Guest Editor
Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, ON K1S 5K2, Canada
Interests: optical remote sensing technology development and EO-based spatial analysis for applications related to urban and mining development
Special Issues, Collections and Topics in MDPI journals
Dr. Gang Chen

grade E-Mail Website
Guest Editor
Laboratory for Remote Sensing and Environmental Change, Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28277, USA
Interests: remote sensing; forest disturbances; GEOBIA; spatial ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advancements of high-resolution (HR) optical remote sensing technologies and imagery from spaceborne or airborne platforms in recent decades have opened up a broad new research area focused on significantly advancing the application of Earth observation (including disaster response, environment monitoring, risk analysis, infrastructure mapping, and mining development). With the detailed information of land surface features derived from HR remote sensing data, our understanding and capability of monitoring of the Earth’s surface and its environment have been unprecedently improved. Especially when it comes to the dynamic and intricately patterned manmade land surface features, HR imagery data play an essential role in information detection, extraction, monitoring, and analyses. However, challenges remain in the processing of and information extraction from HR data. Along with the use of HR data, new concepts and efficient technologies have been developed or are currently under development to address the challenges for improved information extraction. Considerable progresses have already been achieved in this regard.

This Special Issue provides a platform to review and synthesize the latest progress in land surface feature extraction from HR data and invites authors to submit their original and innovative research over a wide range of topics which may focus on, but are not limited to, the following topics:

  • Developments of frameworks and methodologies with new concepts for HR image processing and information extraction;
  • AI-based HR information extraction;
  • Multi-source and multi-modal data fusion for information extraction;
  • Mapping of target features from data acquired from a variety of platforms, such as satellites, International Space Station, airplanes, UAV, and ground vehicles;
  • High- and low-resolution data fusion;
  • Big data processing for information extraction;
  • Improvement of image information resolution;
  • Applications of HR feature extraction in urban or natural environments;
  • Time-series data analytics for mapping feature temporal dynamics.

Dr. Ying Zhang
Dr. Gang Chen
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.

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Published Papers (3 papers)

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27 pages, 10879 KiB  
Article
Fusion of Google Street View, LiDAR, and Orthophoto Classifications Using Ranking Classes Based on F1 Score for Building Land-Use Type Detection
by Nafiseh Ghasemian Sorboni, Jinfei Wang and Mohammad Reza Najafi
Remote Sens. 2024, 16(11), 2011; https://doi.org/10.3390/rs16112011 - 3 Jun 2024
Cited by 2 | Viewed by 977
Abstract
Building land-use type classification using earth observation data is essential for urban planning and emergency management. Municipalities usually do not hold a detailed record of building land-use types in their jurisdictions, and there is a significant need for a detailed classification of this [...] Read more.
Building land-use type classification using earth observation data is essential for urban planning and emergency management. Municipalities usually do not hold a detailed record of building land-use types in their jurisdictions, and there is a significant need for a detailed classification of this data. Earth observation data can be beneficial in this regard, because of their availability and requiring a reduced amount of fieldwork. In this work, we imported Google Street View (GSV), light detection and ranging-derived (LiDAR-derived) features, and orthophoto images to deep learning (DL) models. The DL models were trained on building land-use type data for the Greater Toronto Area (GTA). The data was created using building land-use type labels from OpenStreetMap (OSM) and web scraping. Then, we classified buildings into apartment, house, industrial, institutional, mixed residential/commercial, office building, retail, and other. Three DL-derived classification maps from GSV, LiDAR, and orthophoto images were combined at the decision level using the proposed ranking classes based on the F1 score method. For comparison, the classifiers were combined using fuzzy fusion as well. The results of two independent case studies, Vancouver and Fort Worth, showed that the proposed fusion method could achieve an overall accuracy of 75%, up to 8% higher than the previous study using CNNs and the same ground truth data. Also, the results showed that while mixed residential/commercial buildings were correctly detected using GSV images, the DL models confused many houses in the GTA with mixed residential/commercial because of their similar appearance in GSV images. Full article
(This article belongs to the Special Issue Land Surface Feature Extraction from High-Resolution Remote Sensing Imagery)
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16 pages, 1087 KiB  
Article
Tree-CRowNN: A Network for Estimating Forest Stand Density from VHR Aerial Imagery
by Julie Lovitt, Galen Richardson, Ying Zhang and Elisha Richardson
Remote Sens. 2023, 15(22), 5307; https://doi.org/10.3390/rs15225307 - 9 Nov 2023
Cited by 1 | Viewed by 2558
Abstract
Estimating the number of trees within a forest stand, i.e., the forest stand density (FSD), is challenging at large scales. Recently, researchers have turned to a combination of remote sensing and machine learning techniques to derive these estimates. However, in most cases, the [...] Read more.
Estimating the number of trees within a forest stand, i.e., the forest stand density (FSD), is challenging at large scales. Recently, researchers have turned to a combination of remote sensing and machine learning techniques to derive these estimates. However, in most cases, the developed models rely heavily upon additional data such as LiDAR-based elevations or multispectral information and are mostly applied to managed environments rather than natural/mixed forests. Furthermore, they often require the time-consuming manual digitization or masking of target features, or an annotation using a bounding box rather than a simple point annotation. Here, we introduce the Tree Convolutional Row Neural Network (Tree-CRowNN), an alternative model for tree counting inspired by Multiple-Column Neural Network architecture to estimate the FSD over 12.8 m × 12.8 m plots from high-resolution RGB aerial imagery. Our model predicts the FSD with very high accuracy (MAE: ±2.1 stems/12.8 m2, RMSE: 3.0) over a range of forest conditions and shows promise in linking to Sentinel-2 imagery for broad-scale mapping (R2: 0.43, RMSE: 3.9 stems/12.8 m2). We believe that the satellite imagery linkage will be strengthened with future efforts, and transfer learning will enable the Tree-CRowNN model to predict the FSD accurately in other ecozones. Full article
(This article belongs to the Special Issue Land Surface Feature Extraction from High-Resolution Remote Sensing Imagery)
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Other

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17 pages, 6202 KiB  
Technical Note
Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand
by Gang Chen, Colleen Hammelman, Sutee Anantsuksomsri, Nij Tontisirin, Amelia R. Todd, William W. Hicks, Harris M. Robinson, Miles G. Calloway, Grace M. Bell and John E. Kinsey III
Remote Sens. 2024, 16(6), 1035; https://doi.org/10.3390/rs16061035 - 14 Mar 2024
Cited by 2 | Viewed by 1934
Abstract
This study aims to understand the spatiotemporal changes in patterns of tropical crop cultivation in Eastern Thailand, encompassing the periods before, during, and after the COVID-19 pandemic. Our approach involved assessing the efficacy of high-resolution (10 m) Sentinel-2 dense image time series for [...] Read more.
This study aims to understand the spatiotemporal changes in patterns of tropical crop cultivation in Eastern Thailand, encompassing the periods before, during, and after the COVID-19 pandemic. Our approach involved assessing the efficacy of high-resolution (10 m) Sentinel-2 dense image time series for mapping smallholder farmlands. We integrated harmonic regression and random forest to map a diverse array of tropical crop types between summer 2017 and summer 2023, including durian, rice, rubber, eucalyptus, oil palm, pineapple, sugarcane, cassava, mangosteen, coconut, and other crops. The results revealed an overall mapping accuracy of 85.6%, with several crop types exceeding 90%. High-resolution imagery demonstrated particular effectiveness in situations involving intercropping, a popular practice of simultaneously growing two or more plant species in the same patch of land. However, we observed overestimation in the majority of the studied cash crops, primarily those located in young plantations with open tree canopies and grass-covered ground surfaces. The adverse effects of the COVID-19 pandemic were observed in specific labor-intensive crops, including rubber and durian, but were limited to the short term. No discernible impact was noted across the entirety of the study timeframe. In comparison, financial gain and climate change appeared to be more pivotal in influencing farmers’ decisions regarding crop cultivation. Traditionally dominant crops such as rice and oil palm have witnessed a discernible decline in cultivation, reflecting a decade-long trend of price drops preceding the pandemic. Conversely, Thai durian has seen a significant upswing even over the pandemic, which ironically served as a catalyst prompting Thai farmers to adopt e-commerce to meet the surging demand, particularly from China. Full article
(This article belongs to the Special Issue Land Surface Feature Extraction from High-Resolution Remote Sensing Imagery)
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