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Remote Sensing
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Remote Sensing for Land and Vegetation Mapping
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Remote Sensing for Land and Vegetation Mapping

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 6794

Special Issue Editors

Prof. Dr. Marco Piras

E-Mail Website1 Website2
Guest Editor
Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: geomatics; GNSS; mapping; earth observations; remote sensing; geographic information system; spatial analysis; image analysis; UAV; artificial intelligence; low-cost sensors; sensors integrations; data fusion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Emanuele Lingua

E-Mail Website
Guest Editor
Department of Land, Environment, Agriculture and Forestry (TESAF), University of Padova, Viale dell’Università 16, 35020 Legnaro (PD), Italy
Interests: forest ecology and management; natural disturbances; RS for forestry; spatial analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Raffaella Marzano

E-Mail Website
Guest Editor
Department of Agricultural, Forest and Food Sciences (DISAFA), University of Torino, Largo Braccini 2, 10095 Grugliasco (TO), Italy
Interests: fire ecology; restoration ecology; natural disturbances; RS for natural disturbances; regeneration dynamics; post-disturbance management; nature-based solutions
Special Issues, Collections and Topics in MDPI journals
Dr. Elena Belcore

E-Mail Website
Guest Editor
Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: forest; GIS; Copernicus; remote sensing; machine learning; land cover; vegetation mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land and vegetation maps are fundamental tools to acquire information on key territory attributes at different spatial and temporal resolutions, particularly when the management and planning of natural resources are involved, from local to global scales. Maps are based on two components: (1) classification and (2) spatial attribution of these classifications.

How land and vegetation are mapped today is, therefore, a critical issue, and mapping can be undertaken using a huge variety of approaches and for a diversity of purposes. Using the most recent and innovative remote sensing technologies, it is possible to produce multiresolution maps and multilayers, where several types of information are included, allowing to perform analyses for different purposes. These technological advancements have led to accurate observation of spatiotemporal variability. Today, thanks to new technologies, it is possible to acquire geospatial data and Earth Observation working in a better way. In particular, using satellites (e.g., Sentinel), aerial and unmanned aerial vehicles (UAV) with digital cameras (e.g., RGB, multi and hyperspectral) and LiDAR, it is possible to collect data with different resolution, multitemporal, high-quality and on-demand. In addition, it is possible to combine these data with other observation collected by other kinds of terrestrial sensors devoted to mapping the land and vegetation for different aims, such as agricultural, forestry applications, and more.

The Special Issue “Remote Sensing for Land and Vegetation Mapping” encourages discussions concerning innovative techniques/approaches that are based on any type of remote sensing data, which are used for land and vegetation mapping in various ecosystems at different spatial and temporal scales, even including data fusion and data processing.

In particular, contributions covering the following subtopics are welcome:

  • Forest disturbance mapping and dynamics (change detection)
  • Agricultural monitoring
  • Urban mapping
  • Fires and biomasses
  • Mapping and monitoring of land management practices
  • New tools for data collection and mapping
  • Data fusion
  • New algorithms for classifications and segmentations
  • Copernicus

Prof. Dr. Marco Piras
Prof. Dr. Emanuele Lingua
Prof. Dr. Raffaella Marzano
Dr. Elena Belcore
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

  • UAV
  • Multispectral camera
  • Hyperspectral camera
  • Machine learning
  • Neural network
  • LiDAR
  • Copernicus
  • GIS
  • Climate change

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

17 pages, 5477 KiB  
Article
A Comparison of Machine Learning and Geostatistical Approaches for Mapping Forest Canopy Height over the Southeastern US Using ICESat-2
by Kasip Tiwari and Lana L. Narine
Remote Sens. 2022, 14(22), 5651; https://doi.org/10.3390/rs14225651 - 9 Nov 2022
Cited by 9 | Viewed by 3102
Abstract
The availability of canopy height information in the Ice, Cloud, and Land Elevation Satellite-2’s (ICESat-2’s) land and vegetation product, or ATL08, presents opportunities for developing full-coverage products over broad spatial scales. The primary goal of this study was to develop a 30-meter canopy [...] Read more.
The availability of canopy height information in the Ice, Cloud, and Land Elevation Satellite-2’s (ICESat-2’s) land and vegetation product, or ATL08, presents opportunities for developing full-coverage products over broad spatial scales. The primary goal of this study was to develop a 30-meter canopy height map over the southeastern US, for the Southeastern Plains ecoregion and the Middle Atlantic Coastal Plains ecoregion. More specifically, this work served to compare well-known modeling approaches for upscaling canopy information from ATL08 to develop a wall-to-wall product. Focusing on only strong beams from nighttime acquisitions, the h_canopy parameter was extracted from ATL08 data. Landsat-8 bands and derived vegetation indices (normalized difference vegetation index, enhanced vegetation index, and modified soil-adjusted vegetation index) along with National Land Cover Database’s canopy cover and digital elevation models were used to extrapolate ICESat-2 canopy height from tracks to the regional level. Two different modeling techniques, random forest (RF) and regression kriging (RK), were applied for estimating canopy height. The RF model estimated canopy height with a coefficient of determination (R2) value of 0.48, root-mean-square error (RMSE) of 4.58 m, mean absolute error (MAE) of 3.47 and bias of 0.23 for independent validation, and an R2 value of 0.38, RMSE of 6.39 m, MAE of 5.04 and bias of −1.39 when compared with airborne lidar-derived canopy heights. The RK model estimated canopy heights with an R2 value of 0.69, RMSE of 3.49 m, MAE of 2.61 and bias of 0.03 for independent validation, and an R value of 0.68, R2 value of 0.47, RMSE of 5.96m, MAE of 4.52 and bias of −1.81 when compared with airborne lidar-derived canopy heights. The results suggest feasibility for the implementation of the RK method over a larger spatial extent and potential for combining other remote sensing and satellite data for future monitoring of canopy height dynamics. Full article
(This article belongs to the Special Issue Remote Sensing for Land and Vegetation Mapping)
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20 pages, 5544 KiB  
Article
Mapping Riparian Habitats of Natura 2000 Network (91E0*, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data
by Elena Belcore, Marco Pittarello, Andrea Maria Lingua and Michele Lonati
Remote Sens. 2021, 13(9), 1756; https://doi.org/10.3390/rs13091756 - 30 Apr 2021
Cited by 20 | Viewed by 2771
Abstract
Riparian habitats provide a series of ecological services vital for the balance of the environment, and are niches and resources for a wide variety of species. Monitoring riparian environments at the intra-habitat level is crucial for assessing and preserving their conservation status, although [...] Read more.
Riparian habitats provide a series of ecological services vital for the balance of the environment, and are niches and resources for a wide variety of species. Monitoring riparian environments at the intra-habitat level is crucial for assessing and preserving their conservation status, although it is challenging due to their landscape complexity. Unmanned aerial vehicles (UAV) and multi-spectral optical sensors can be used for very high resolution (VHR) monitoring in terms of spectral, spatial, and temporal resolutions. In this contribution, the vegetation species of the riparian habitat (91E0*, 3240 of Natura 2000 network) of North-West Italy were mapped at individual tree (ITD) level using machine learning and a multi-temporal phenology-based approach. Three UAV flights were conducted at the phenological-relevant time of the year (epochs). The data were analyzed using a structure from motion (SfM) approach. The resulting orthomosaics were segmented and classified using a random forest (RF) algorithm. The training dataset was composed of field-collected data, and was oversampled to reduce the effects of unbalancing and size. Three-hundred features were computed considering spectral, textural, and geometric information. Finally, the RF model was cross-validated (leave-one-out). This model was applied to eight scenarios that differed in temporal resolution to assess the role of multi-temporality over the UAV’s VHR optical data. Results showed better performances in multi-epoch phenology-based classification than single-epochs ones, with 0.71 overall accuracy compared to 0.61. Some classes, such as Pinus sylvestris and Betula pendula, are remarkably influenced by the phenology-based multi-temporality: the F1-score increased by 0.3 points by considering three epochs instead of two. Full article
(This article belongs to the Special Issue Remote Sensing for Land and Vegetation Mapping)
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