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Land Surface Feature Extraction from High-Resolution Remote Sensing Imagery

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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

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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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16 pages, 1087 KiB

Open AccessArticle

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 - 09 Nov 2023

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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 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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17 pages, 6202 KiB

Open AccessTechnical 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

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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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