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Vegetation Mapping through Multiscale Remote Sensing

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

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 4530

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Special Issue Editors

Dr. Alberto García-Martín

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Guest Editor

1. Centro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, Spain
2. Environmental Sciences Institute (IUCA), University of Zaragoza, 50009 Zaragoza, Spain
Interests: multispectral and SAR remote sensing; GIS; field spectrometry; vegetation structure mapping; forest inventory; forest fires

Dr. Antonio Luis Montealegre Gracia

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Guest Editor

1. Centro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, Spain
2. Environmental Sciences Institute (IUCA), University of Zaragoza, 50009 Zaragoza, Spain
Interests: ALS and multispectral remote sensing; GIS; vegetation structure mapping; forest inventory; forest fires

Special Issue Information

Dear Colleagues,

Mapping the type and extent of vegetation, monitoring the changes in vegetation cover, and understanding its dynamics are becoming increasingly important topics directly related to the Sustainable Development Goals “Climate action” and “Life on Land”.

At present, the availability of multi-resolution remote sensing datasets allows multiscale and multitemporal approaches in order to perform analysis and modeling for the sustainable management of plant ecosystems. In addition, the democratization of data and open-source tools are improving the pool of knowledge to forest managers, stakeholders and decision makers. All of this requires the integration of different remote sensing techniques—satellite, aerial (from aircrafts and UAVs), as well as ground-based—in order to establish the link between local, regional and global observations (upscaling–downscaling).

This Special Issue welcomes contributions focusing on the integrated use of multi-scale remote sensing observations applied to vegetation mapping.

We particularly appreciate contributions exploiting novel methods and applications from multiscale/multisource observations. Review articles are also welcome. Articles may address, but are not limited to, the following topics:

Vegetation land cover mapping and pattern analysis;
Vegetation change;
Biotic and abiotic vegetation damage;
Wildfire studies (pre-fire, monitoring and post-fire);
Biophysical parameters (Biomass, LAI, canopy water content, canopy height, etc.);
Biodiversity and wildlife;
Novel strategies for multiscale data processing;
The role of scale in vegetation mapping;
Multiscale, multispectral and multi-temporal remote-sensing data fusion;
Upscaling or downscaling approaches.

Dr. Alberto García-Martín
Dr. Antonio Luis Montealegre Gracia
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

Vegetation distribution and dynamics
Environmental assessment and monitoring
Multiscale and multiplatform remote sensing
Spectral and structural data integration
Multispectral imagery
LiDAR/SAR
UAV
Field spectrometry
Upscaling
Downscaling

Published Papers (3 papers)

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Research

18 pages, 4738 KiB

Open AccessArticle

Multi-Temporal and Multiscale Satellite Remote Sensing Imagery Analysis for Detecting Pasture Area Changes after Grazing Cessation Due to the Fukushima Daiichi Nuclear Disaster

by Muxiye Muxiye and Chinatsu Yonezawa

Remote Sens. 2023, 15(22), 5416; https://doi.org/10.3390/rs15225416 - 18 Nov 2023

Viewed by 1083

Abstract

Despite advancements in remote sensing applications for grassland management, studies following the 2011 Fukushima Daiichi nuclear disaster have often been constrained by limited satellite imagery with insufficient focus on pasture changes. Utilizing different resolutions of optical satellite data is essential for monitoring spatiotemporal changes in grasslands. High resolutions provide detailed spatial information, whereas medium-resolution satellites offer an increased frequency and wider availability over time. This study had two objectives. First, we investigated the temporal changes in a mountainous pasture in Japan from 2007 to 2022 using high-resolution data from QuickBird, WorldView-2, and SPOT-6/7, along with readily available medium-resolution data from Sentinel-2 and Landsat-5/7/8. Second, we assessed the efficacy of different satellite image resolutions in capturing these changes. Grazing ceased in the target area after the 2011 Fukushima Daiichi nuclear accident owing to radiation. We categorized the images as grasses, broadleaf trees, and conifers. The results showed a 36% decline using high-resolution satellite image analysis and 35% using Landsat image analysis in the unused pasture area since grazing suspension in 2011, transitioning primarily to broadleaf trees, and relative stabilization by 2018. Tree encroachment was prominent at the eastern site, which has a lower elevation and steeper slope facing north, east, and south. WorldView-2 consistently outperformed Landsat-8 in accuracy. Landsat-8’s classification variation impedes its ability to capture subtle distinctions, particularly in zones with overlapping or neighboring land covers. However, Landsat effectively detected area reductions, similar to high-resolution satellites. Combining high- and medium-resolution satellite data leverages their respective strengths, compensates for their individual limitations, and provides a holistic perspective for analysis and decision-making. Full article

(This article belongs to the Special Issue Vegetation Mapping through Multiscale Remote Sensing)

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17 pages, 3565 KiB

Open AccessArticle

Continuity of Top-of-Atmosphere, Surface, and Nadir BRDF-Adjusted Reflectance and NDVI between Landsat-8 and Landsat-9 OLI over China Landscape

by Yuanheng Sun, Binyu Wang, Senlin Teng, Bingxin Liu, Zhaoxu Zhang and Ying Li

Remote Sens. 2023, 15(20), 4948; https://doi.org/10.3390/rs15204948 - 13 Oct 2023

Viewed by 949

Abstract

The successful launch of Landsat-9 marks a significant achievement in preserving the data legacy and ensuring the continuity of Landsat’s calibrated Earth observations. This study comprehensively assesses the continuity of reflectance and the Normalized Difference Vegetation Index (NDVI) between Landsat-8 and Landsat-9 Operational Land Imagers (OLIs) over diverse Chinese landscapes. It reveals that sensor discrepancies minimally impact reflectance and NDVI consistency. Although Landsat-9’s top-of-atmosphere (TOA) reflectance is slightly lower than that of Landsat-8, small root-mean-square errors (RMSEs) ranging from 0.0102 to 0.0248 for VNIR and SWIR bands (and larger RMSE for NDVI at 0.0422) fall within acceptable ranges for Earth observation applications. Applying atmospheric corrections markedly enhances reflectance uniformity and brings regression slopes closer to unity. Further, Bidirectional Reflectance Distribution Function (BRDF) adjustments improve comparability, ensuring measurement reliability, and the NDVI maintains robust consistency across various reflectance types, time series, and land cover classes. These findings affirm Landsat-9’s success in achieving data continuity within the Landsat program, allowing interchangeable use of Landsat-8 and Landsat-9 OLI data for diverse Earth observation purposes. Future research may explore specific sensor correlations across different vegetation types and seasons while integrating data from complementary platforms, such as Sentinel-2, to enhance the understanding of data continuity factors. Full article

(This article belongs to the Special Issue Vegetation Mapping through Multiscale Remote Sensing)

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14 pages, 1767 KiB

Open AccessArticle

Exploring the Potential of Lidar and Sentinel-2 Data to Model the Post-Fire Structural Characteristics of Gorse Shrublands in NW Spain

by José María Fernández-Alonso, Rafael Llorens, José Antonio Sobrino, Ana Daría Ruiz-González, Juan Gabriel Alvarez-González, José Antonio Vega and Cristina Fernández

Remote Sens. 2022, 14(23), 6063; https://doi.org/10.3390/rs14236063 - 30 Nov 2022

Cited by 3 | Viewed by 1490

Abstract

The characterization of aboveground biomass is important in forest management planning, with various objectives ranging from prevention of forest fires to restoration of burned areas, especially in fire-prone regions such as NW Spain. Although remotely sensed data have often been used to assess the recovery of standing aboveground biomass after perturbations, the data have seldom been validated in the field, and different shrub fractions have not been modelled. The main objective of the present study was to assess different vegetation parameters (cover, height, standing AGB and their fractions) in field plots established in five areas affected by wildfires between 2009 and 2016 by using Sentinel-2 spectral indices and LiDAR metrics. For this purpose, 22 sampling plots were established in 2019, and vegetation variables were measured by a combination of non-destructive measurement (cover and height) and destructive sampling (total biomass and fine samples of live and dead fractions of biomass).The structural characterization of gorse shrublands was addressed, and models of shrub cover—height, total biomass, and biomass by fraction and physiological condition—were constructed, with adjusted coefficients of determination ranging from 0.6 to 0.9. The addition of LiDAR data to optical remote sensing images improved the models. Further research should be conducted to calibrate the models in other vegetation communities. Full article

(This article belongs to the Special Issue Vegetation Mapping through Multiscale Remote Sensing)

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