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4D (Multi-Temporal) Remote Sensing: Opportunities, Challenges and Issues for Environmental Monitoring over Time

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

Deadline for manuscript submissions: 30 July 2024 | Viewed by 4506

Special Issue Editors


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Guest Editor
Department of Land, Environment, Agriculture and Forestry, University of Padova, viale dell’Università 16, 35020 Legnaro, Italy
Interests: SfM; LiDAR; UAS; digital terrain analysis; geomorphometry; hydrology; sediment dynamics

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Guest Editor
Polytechnic Department of Engineering and Architecture, University of Udine, Via Delle Scienze, 206, 33100 Udine, Italy
Interests: image orientation; LiDAR; mobile mapping systems; semantic segmentation of point clouds

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Guest Editor
Pyrenean Ecology Institute, Spanish National Research Council (CSIC), Av. Montañana, 1005, 500559 Zaragoza, Spain
Interests: high resolution topography; geomorphic change detection; remote sensing; historical-SfM; sediment transport
Division for Ecology and Biodiversity, School of Biological Sciences, University of Hong Kong, Pokfulam Road, Hong Kong, China
Interests: SfM photogrammetry; UAS; soil spectroscopy; remote sensing

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Guest Editor
Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Iași, 700505 Iași, Romania
Interests: geomorphometry; geomorphic change detection; high-resolution topography; landslides; gullies; fluvial geomorphology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, multitemporal high-resolution topography (HRT; e.g., photogrammetry, LiDAR, GNSS) data sets are becoming increasingly available, improving our ability and opportunities to monitor landscape evolution at different scales and times. Indeed, some HRT techniques allow performing multitemporal (4D) surveys with adequate frequency to detect changes at an appropriately temporal scale at which surface processes operate. However, in order to obtain comparable results over time, it is necessary to implement methodologies and workflows that consider the issues associated with 4D surveys (e.g., the assessment of accuracy and uncertainties and the use of appropriate georeferencing and co-registration approaches). Moreover, topographic surveying platforms, georeferencing systems, and processing tools have seen important developments in the last two decades; therefore, HRT data acquired at different epochs may be characterized by different accuracy and precision over time. As a consequence, old “legacy” data sets and recent surveys can often show comparison problems, especially when multitemporal data are not homogeneous in terms of quality and uncertainties.

This Special Issue invites studies involving 4D surveys or datasets, focusing on in-depth assessment and comparison of current solutions or developing new methodologies for comparing multitemporal data acquired at different scales in various environmental contexts (e.g., steep slopes, agricultural, glacial) and possibly with diverse techniques. Topics may cover any type of technology, from historical data (e.g., historical images) to novel HRT techniques (e.g., UAS-LiDAR). Hence, multisource data integration (e.g., multispectral, hyperspectral, and thermal), multiscale approaches or studies focused on data fusion and comparison of HRT techniques are welcome. Articles may address but are not limited to the following monitoring applications:

  • Geomorphological changes;
  • Soil erosion process;
  • Land use changes;
  • Agricultural and crop dynamics;
  • Forest changes;
  • Glacial and periglacial dynamics.

Dr. Sara Cucchiaro
Dr. Eleonora Maset
Dr. Manel Llena
Dr. He Zhang
Dr. Mihai Niculita
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-resolution topography
  • co-registration
  • structure from motion
  • LiDAR
  • UAS
  • PPK and RTK-GNSS surveys
  • data fusion
  • geomorphic change detection
  • historical-structure from motion photogrammetry

Published Papers (2 papers)

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Research

24 pages, 6366 KiB  
Article
An Optimized Workflow for Digital Surface Model Series Generation Based on Historical Aerial Images: Testing and Quality Assessment in the Beach-Dune System of Sa Ràpita-Es Trenc (Mallorca, Spain)
by Christian Mestre-Runge, Jorge Lorenzo-Lacruz, Aaron Ortega-Mclear and Celso Garcia
Remote Sens. 2023, 15(8), 2044; https://doi.org/10.3390/rs15082044 - 12 Apr 2023
Cited by 2 | Viewed by 1579
Abstract
We propose an optimized Structure-from-Motion (SfM) Multi-View Stereopsis (MVS) workflow, based on minimizing different errors and inaccuracies of historical aerial photograph series (1945, 1979, 1984, and 2008 surveys), prior to generation of elevation-calibrated historical Digital Surface Models (hDSM) at 1 m resolution. We [...] Read more.
We propose an optimized Structure-from-Motion (SfM) Multi-View Stereopsis (MVS) workflow, based on minimizing different errors and inaccuracies of historical aerial photograph series (1945, 1979, 1984, and 2008 surveys), prior to generation of elevation-calibrated historical Digital Surface Models (hDSM) at 1 m resolution. We applied LiDAR techniques on Airborne Laser Scanning (ALS) point clouds (Spanish PNOA LiDAR flights of 2014 and 2019) for comparison and validation purposes. Implementation of these products in multi-temporal analysis requires quality control due to the diversity of sources and technologies involved. To accomplish this, (i) we used the Mean Absolute Error (MAE) between GNSS-Validation Points and the elevations observed by DSM-ALS to evaluate the elevation accuracy of DSM-ALS generated with the LAScatalog processing engine; (ii) optimization of the SfM sparse clouds in the georeferencing step was evaluated by calculating the Root Mean Square Error (RMSE) between the Check Points extracted from DSM-ALS and the predicted elevations per sparse cloud; (iii) the MVS clouds were evaluated by calculating the MAE between ALS-Validation Points and the predicted elevations per MVS cloud; iv) the accuracy of the resulting historical SfM-MVS DSMs were assessed using the MAE between ALS-Validation Points and the observed elevations per historical DSM; and (v) we implemented a calibration method based on a linear correction to reduce the elevation discrepancies between historical DSMs and the DSM-ALS 2019 reference elevations. This optimized workflow can generate high-resolution (1 m pixel size) hDSMs with reasonable accuracy: MAE in z ranges from 0.41 m (2008 DSM) to 5.21 m (1945 DSM). Overall, hDSMs generated using historical images have great potential for geo-environmental processes monitoring in different ecosystems and, in some cases (i.e., sufficient image overlapping and quality), being an acceptable replacement for LiDAR data when it is not available. Full article
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17 pages, 6470 KiB  
Article
Mapping Forage Biomass and Quality of the Inner Mongolia Grasslands by Combining Field Measurements and Sentinel-2 Observations
by Xia Zhao, Bo Wu, Jinxin Xue, Yue Shi, Mengying Zhao, Xiaoqing Geng, Zhengbing Yan, Haihua Shen and Jingyun Fang
Remote Sens. 2023, 15(8), 1973; https://doi.org/10.3390/rs15081973 - 8 Apr 2023
Cited by 2 | Viewed by 1881
Abstract
Grasslands provide essential forage sources for global livestock production. Remote sensing approaches have been widely used to estimate the biomass production of grasslands from regional to global scales, but simultaneously mapping the forage biomass and quality metrics (e.g., crude fiber and crude protein) [...] Read more.
Grasslands provide essential forage sources for global livestock production. Remote sensing approaches have been widely used to estimate the biomass production of grasslands from regional to global scales, but simultaneously mapping the forage biomass and quality metrics (e.g., crude fiber and crude protein) is still relatively lacking despite an increasing need for better livestock management. We conducted novel gradient grass-cutting experiments and measured hyperspectral reflectance, forage biomass, crude fiber per area (CFarea), and crude protein per area (CParea) across 19 temperate grassland sites in the Xilingol region, Inner Mongolia, China. Based on these measurements, we identified sensitive spectral bands, calculated nine potential spectral indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index, Red Edge Normalized Difference Vegetation Index, Red-Edge Inflection Point, Inverted Red-Edge Chlorophyll Index algorithm, Normalized Difference Red Edge Index, Nitrogen Reflectance Index, Normalized Greenness Index, Land Surface Water Index) and established Random Forest (RF) models that well predicted forage biomass (R2 = 0.67, NRMSE = 12%), CFarea (R2 = 0.59, NRMSE = 14%), and CParea (R2 = 0.77, NRMSE = 10%). Among these nine indices, Land Surface Water Index (LSWI, calculated by R785-900 and R2100-2280) was identified to be the most important predictor and was then used to establish empirical power law models, showing comparable prediction accuracies (forage biomass, R2 = 0.53; NRMSE = 14%; CFarea, R2 = 0.40, NRMSE = 17%; CParea, R2 = 0.72, NRMSE = 11%) in comparison to Random Forest models. Combining the empirical power law models with the LSWI calculated from Sentinel-2 observations, we further mapped the forage biomass and quality and estimated the livestock carrying capacity. The predicted forage biomass, CFarea, and CParea all showed a significant increase with higher mean annual precipitation, but showed no significant correlations with mean annual temperature. Compared with the estimates based on crude protein, the conventional approach solely based on forage biomass consistently overestimated livestock carrying capacity, especially in wetter areas. Our work provides an approach to simultaneously map the forage biomass and quality metrics and recommends a LSWI-based power law model for rapid and low-cost assessment of regional forage status to guide better livestock management. Full article
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