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Remote Sensing of Vegetation Function and Traits

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 17985

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Special Issue Editors

Dr. Tawanda W. Gara

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Guest Editor

Department of Environmental Science and Management, California State Polytechnic University Humboldt, Arcata, CA 95521, USA
Interests: geographic information science; remote sensing; natural resources management; vegetation spectroscopy; environmental modelling; species distribution modelling; wildlife movement ecology

Dr. Cletah Shoko

E-Mail Website
Guest Editor

Division of Geography, School of Geography, Archaeology and Environmental Studies, University of Witwatersrand, Johannesburg, South Africa
Interests: vegetation characteristics; plant-water use modelling; productivity assessment; carbon estimation

Prof. Dr. Timothy Dube

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Guest Editor

Institute for Water Studies, Department of Earth Sciences, University of The Western Cape, Robert Sobukwe, Bellville, South Africa
Interests: landscape ecology; biodiversity; land use/land cover; GIS; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plant functional traits—defined herein as plant features (morphological, physiological, and phenological) that represent ecological strategies and determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties—are important in ecological monitoring and management. Plant functional trait variation and trait syndromes have proven useful for addressing many important ecological questions at various scales, resulting in a demand for standardized methods to quantify ecologically meaningful plant traits. This area of study has been one of the most fruitful in terms of understanding ecological and evolutionary patterns and processes in plant functional traits. Thus, an accurate and timely retrieval of vegetation functional traits contributes to improving our understanding and predicting the adaptation capacity to environmental change and/or disturbance. Remote sensing has the capacity to monitor the status of vegetation function through traits across multiple spatial and temporal scales. Spectral signals provide the capacity to extract different functionally relevant traits, such as pigments (e.g., chlorophylls, carotenoids, etc.) and dry-matter-related traits (foliar nitrogen, LMA) based on the optical properties of leaves in a canopy. A variety of physiological attributes of vegetation can now be successfully estimated and monitored at multiple spatial and temporal scales, courtesy of recent advancements in remote sensing technologies and data modeling approaches. For example, the maximum carboxylation capacity (a key trait related to photosynthesis) can be inferred from spectral reflectance using radiative transfer model inversion, machine learning and deep learning. Techniques ranging from parametric models (e.g., partial least square regression) and machine learning (random forest) to deep learning, which are among the most promising methods, provide a pathway to interpret information from a spectral signature, outperforming traditional approaches. With the increased availability of remote sensing data, sensor fusion has emerged as an intriguing avenue for the improved retrieval of vegetation function and traits from space.

This Special Issue, entitled “Remote Sensing Vegetation Function and Traits”, encourages the submission of novel techniques/approaches for retrieving and estimating vegetation function and traits at various spatial scales (e.g., leaf level, canopy, stand, landscape, and regional) and temporal scales, using any form of remote sensing data (proximal, airborne, and satellite), across various ecosystems and vegetation types.

Original research or review articles on one or more of the following topics are welcome:

Remote sensing of vegetation function and traits (e.g., photosynthesis, primary production, LAI/ N, EWT, LMA): Techniques, evaluations and future missions;
Very-high-resolution remote sensing of vegetation function and traits (e.g., Worldview, GeoEye, high-resolution airborne lidar, etc.): Techniques and evaluations;
Application of new sensors/algorithms to pigments and morphological and physiological traits;
Remote sensing of crop health and stress through vegetation function and traits;
Comparison and evaluation of different remote sensing methods (statistical, physical and hybrid models);
Retrieval of vegetation biochemical/biophysical parameters (e.g., LAI, vegetation productivity, biomass, pigments, and EWT);
Sources of uncertainties in remote sensing scaling vegetation function and traits, from leaf to canopy;
Multi-sensor data fusion for vegetation function and traits (e.g., AVHRR-to-MODIS-to-Landsat-Sentinel-WorldView, Harmonized Landsat-to-Sentinel-2, etc.).

Dr. Tawanda W. Gara
Dr. Cletah Shoko
Prof. Dr. Timothy Dube
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

biochemical/biophysical parameters
multi-sensor data fusion
environmental change
scaling and uncertainties
radiative transfer modeling
plant health

Published Papers (8 papers)

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Research

Jump to: Review

24 pages, 8435 KiB

Open AccessArticle

A Novel Approach to Match Individual Trees between Aerial Photographs and Airborne LiDAR Data

by Yi Xu, Tiejun Wang, Andrew K. Skidmore and Tawanda W. Gara

Remote Sens. 2023, 15(17), 4128; https://doi.org/10.3390/rs15174128 - 23 Aug 2023

Viewed by 1217

Abstract

Integrating multimodal remote sensing data can optimize the mapping accuracy of individual trees. Yet, one issue that is not trivial but generally overlooked in previous studies is the spatial mismatch of individual trees between remote sensing datasets, especially in different imaging modalities. These offset errors between the same tree on different data that have been geometrically corrected can lead to substantial inaccuracies in applications. In this study, we propose a novel approach to match individual trees between aerial photographs and airborne LiDAR data. To achieve this, we first leveraged the maximum overlap of the tree crowns in a local area to determine the correct and the optimal offset vector, and then used the offset vector to rectify the mismatch on individual tree positions. Finally, we compared our proposed approach with a commonly used automatic image registration method. We used pairing rate (the percentage of correctly paired trees) and matching accuracy (the degree of overlap between the correctly paired trees) to measure the effectiveness of results. We evaluated the performance of our approach across six typical landscapes, including broadleaved forest, coniferous forest, mixed forest, roadside trees, garden trees, and parkland trees. Compared to the conventional method, the average pairing rate of individual trees for all six landscapes increased from 91.13% to 100.00% (p = 0.045, t-test), and the average matching accuracy increased from 0.692 ± 0.175 (standard deviation) to 0.861 ± 0.152 (p = 0.017, t-test). Our study demonstrates that the proposed tree-oriented matching approach significantly improves the registration accuracy of individual trees between aerial photographs and airborne LiDAR data. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Function and Traits)

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Graphical abstract

20 pages, 3995 KiB

Open AccessArticle

Exploring Influences of Leaves on Urban Species Identification Using Handheld Laser Scanning Point Cloud: A Case Study in Hong Kong

by Meilian Wang and Man Sing Wong

Remote Sens. 2023, 15(11), 2826; https://doi.org/10.3390/rs15112826 - 29 May 2023

Viewed by 1135

Abstract

Urban tree species identification performs a significant role in tree management and the sustainable development of cities. Conventionally, multispectral or hyperspectral remote sensing images are applied to identify species. However, spectral profiles of trees on images are easily affected by surroundings and illuminations, resulting in urban trees of different species possibly having similar spectral features. The handheld laser scanning (HLS) technique can capture 3D structural information of trees and be confirmed to be effective in reducing the problem of spectral similarity through tree structural properties (TSP). TSP usually varies in different leaf conditions, especially TSP of tropical tree species. In this study, we investigated the effects of leaves on urban tropical tree species identification using HLS. A total of 89 metrics that characterized the TSP were evaluated, including 19 branches, 12 stems, 45 crowns, and 13 entire tree metrics. All metrics were derived under different leaf conditions. The correlation and importance of these metrics were further evaluated. Our results demonstrated that crown metrics perform the most important role in urban species identification in leaf-on and leaf-off conditions and that the combination of metrics derived in different leaf conditions can improve the identification accuracy. Furthermore, we discovered 9 robust metrics that perform well in all leaf conditions, including 3 crowns, 2 branches, 2 stems, and 2 entire tree metrics. These metrics give a deep understanding of numerous structural properties and provide a significant reference for the relevant structure-based classification of other tropical species. This study also illustrated that HLS could help to overcome the spectrum-related limitations and improve the efficiency of species identification and sustainable forest management. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Function and Traits)

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23 pages, 7364 KiB

Open AccessArticle

Carbon Stock Prediction in Managed Forest Ecosystems Using Bayesian and Frequentist Geostatistical Techniques and New Generation Remote Sensing Metrics

by Tsikai Solomon Chinembiri, Onisimo Mutanga and Timothy Dube

Remote Sens. 2023, 15(6), 1649; https://doi.org/10.3390/rs15061649 - 18 Mar 2023

Cited by 3 | Viewed by 1430

Abstract

The study compares the performance of a hierarchical Bayesian geostatistical methodology with a frequentist geostatistical approach, specifically, Kriging with External Drift (KED), for predicting C stock using prediction aides from the Landsat-8 and Sentinel-2 multispectral remote sensing platforms. The frequentist geostatistical approach’s reliance on the long-run frequency of repeated experiments for constructing confidence intervals is not always practical or feasible, as practitioners typically have access to a single dataset due to cost constraints on surveys and sampling. We evaluated two approaches for C stock prediction using two new generation multispectral remote sensing datasets because of the inherent uncertainty characterizing spatial prediction problems in the unsampled locations, as well as differences in how the Bayesian and frequentist geostatistical paradigms handle uncertainty. Information on C stock spectral prediction in the form of NDVI, SAVI, and EVI derived from multispectral remote sensing platforms, Landsat-8 and Sentinel-2, was used to build Bayesian and frequentist-based C stock predictive models in the sampled plantation forest ecosystem. Sentinel-2-based C stock predictive models outperform their Landsat-8 counterparts using both the Bayesian and frequentist inference approaches. However, the Bayesian-based Sentinel-2 C stock predictive model (

R M S E = 0.17 M g C {h a}^{- 1}

) is more accurate than its frequentist-based Sentinel-2 (

R M S E

= 1.19

M g C {h a}^{- 1}

) C stock equivalent. The Sentinel-2 frequentist-based C stock predictive model gave the C stock prediction range of

1 \leq M g C {h a}^{- 1} \leq 290

, whilst the Sentinel-2 Bayesian-based C stock predictive model resulted in the prediction range of

1 \leq M g C {h a}^{- 1} \leq 285

. However, both the Bayesian and frequentist C stock predictive models built with the Landsat-8 sensor overpredicted the sampled C stock because the range of predicted values fell outside the range of the observed C stock values. As a result, we recommend and conclude that the Bayesian-based C stock prediction method, when it is combined with high-quality remote sensing data such as that of Sentinel-2, is an effective inferential statistical methodology for reporting C stock in managed plantation forest ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Function and Traits)

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23 pages, 17368 KiB

Open AccessArticle

Mapping Seasonal Leaf Nutrients of Mangrove with Sentinel-2 Images and XGBoost Method

by Jing Miao, Jianing Zhen, Junjie Wang, Demei Zhao, Xiapeng Jiang, Zhen Shen, Changjun Gao and Guofeng Wu

Remote Sens. 2022, 14(15), 3679; https://doi.org/10.3390/rs14153679 - 1 Aug 2022

Cited by 12 | Viewed by 2000

Abstract

Monitoring the seasonal leaf nutrients of mangrove forests helps one to understand the dynamics of carbon (C) sequestration and to diagnose the availability and limitation of nitrogen (N) and phosphorus (P). To date, very little attention has been paid to mapping the seasonal leaf C, N, and P of mangrove forests with remote sensing techniques. Based on Sentinel-2 images taken in spring, summer, and winter, this study aimed to compare three machine learning models (XGBoost, extreme gradient boosting; RF, random forest; LightGBM, light gradient boosting machine) in estimating the three leaf nutrients and further to apply the best-performing model to map the leaf nutrients of 15 seasons from 2017 to 2021. The results showed that there were significant differences in leaf nutrients (p < 0.05) across the three seasons. Among the three machine learning models, XGBoost with sensitive spectral features of Sentinel-2 images was optimal for estimating the leaf C (R² = 0.655, 0.799, and 0.829 in spring, summer, and winter, respectively), N (R² = 0.668, 0.743, and 0.704) and P (R² = 0.539, 0.622, and 0.596) over the three seasons. Moreover, the red-edge (especially B6) and near-infrared bands (B8 and B8a) of Sentinel-2 images were efficient estimators of mangrove leaf nutrients. The information of species, elevation, and canopy structure (leaf area index [LAI] and canopy height) would be incorporated into the present model to improve the model accuracy and transferability in future studies. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Function and Traits)

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Graphical abstract

18 pages, 3968 KiB

Open AccessArticle

Integrating Semi-Supervised Learning with an Expert System for Vegetation Cover Classification Using Sentinel-2 and RapidEye Data

by Nasir Farsad Layegh, Roshanak Darvishzadeh, Andrew K. Skidmore, Claudio Persello and Nina Krüger

Remote Sens. 2022, 14(15), 3605; https://doi.org/10.3390/rs14153605 - 27 Jul 2022

Cited by 1 | Viewed by 1590

Abstract

In complex classification tasks, such as the classification of heterogeneous vegetation covers, the high similarity between classes can confuse the classification algorithm when assigning the correct class labels to unlabelled samples. To overcome this problem, this study aimed to develop a classification method by integrating graph-based semi-supervised learning (SSL) and an expert system (ES). The proposed method was applied to vegetation cover classification in a wetland in the Netherlands using Sentinel-2 and RapidEye imagery. Our method consisted of three main steps: object-based image analysis (OBIA), integration of SSL and an ES (SSLES), and finally, random forest classification. The generated image objects and the related features were used to construct the graph in SSL. Then, an independently developed and trained ES was used in the labelling stage of SSL to reduce the uncertainty of the process, before the final classification. Different spectral band combinations of Sentinel-2 were then considered to improve the vegetation classification. Our results show that integrating SSL and an ES can result in significantly higher classification accuracy (83.6%) compared to a supervised classifier (64.9%), SSL alone (71.8%), and ES alone (69.5%). Moreover, utilisation of all Sentinel-2 red-edge spectral band combinations yielded the highest classification accuracy (overall accuracy of 83.6% with SSLES) compared to the inclusion of other band combinations. The results of this study indicate that the utilisation of an ES in the labelling process of SSL improves the reliability of the process and provides robust performance for the classification of vegetation cover. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Function and Traits)

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Graphical abstract

14 pages, 20499 KiB

Open AccessArticle

Estimating Community-Level Plant Functional Traits in a Species-Rich Alpine Meadow Using UAV Image Spectroscopy

by Yi-Wei Zhang, Tiejun Wang, Yanpei Guo, Andrew Skidmore, Zhenhua Zhang, Rong Tang, Shanshan Song and Zhiyao Tang

Remote Sens. 2022, 14(14), 3399; https://doi.org/10.3390/rs14143399 - 15 Jul 2022

Cited by 7 | Viewed by 1989

Abstract

Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R² values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R² varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Function and Traits)

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17 pages, 2526 KiB

Open AccessArticle

Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit

by Josip Spišić, Domagoj Šimić, Josip Balen, Antun Jambrović and Vlatko Galić

Remote Sens. 2022, 14(11), 2596; https://doi.org/10.3390/rs14112596 - 28 May 2022

Cited by 5 | Viewed by 2204

Abstract

Real-time monitoring of crop responses to environmental deviations represents a new avenue for applications of remote and proximal sensing. Combining the high-throughput devices with novel machine learning (ML) approaches shows promise in the monitoring of agricultural production. The 3 × 2 multispectral arrays with responses at 610 and 680 nm (red), 730 and 760 nm (red-edge) and 810 and 860 nm (infrared) spectra were used to assess the occurrence of leaf rolling (LR) in 545 experimental maize plots measured four times for calibration dataset (n = 2180) and 145 plots measured once for external validation. Multispectral reads were used to calculate 15 simple normalized vegetation indices. Four ML algorithms were assessed: single and multilayer perceptron (SLP and MLP), convolutional neural network (CNN) and support vector machines (SVM) in three validation procedures, which were stratified cross-validation, random subset validation and validation with external dataset. Leaf rolling occurrence caused visible changes in spectral responses and calculated vegetation indexes. All algorithms showed good performance metrics in stratified cross-validation (accuracy >80%). SLP was the least efficient in predictions with external datasets, while MLP, CNN and SVM showed comparable performance. Combining ML with multispectral sensing shows promise in transition towards agriculture based on data-driven decisions especially considering the novel Internet of Things (IoT) avenues. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Function and Traits)

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Review

Jump to: Research

29 pages, 1507 KiB

Open AccessReview

A Systematic Review on Advancements in Remote Sensing for Assessing and Monitoring Land Use and Land Cover Changes Impacts on Surface Water Resources in Semi-Arid Tropical Environments

by Makgabo Johanna Mashala, Timothy Dube, Bester Tawona Mudereri, Kingsley Kwabena Ayisi and Marubini Reuben Ramudzuli

Remote Sens. 2023, 15(16), 3926; https://doi.org/10.3390/rs15163926 - 8 Aug 2023

Cited by 12 | Viewed by 4702

Abstract

This study aimed to provide a systematic overview of the progress made in utilizing remote sensing for assessing the impacts of land use and land cover (LULC) changes on water resources (quality and quantity). This review also addresses research gaps, challenges, and opportunities associated with the use of remotely sensed data in assessment and monitoring. The progress of remote sensing applications in the assessment and monitoring of LULC, along with their impacts on water quality and quantity, has advanced significantly. The availability of high-resolution satellite imagery, the integration of multiple sensors, and advanced classification techniques have improved the accuracy of land cover mapping and change detection. Furthermore, the study highlights the vast potential for providing detailed information on the monitoring and assessment of the relationship between LULC and water resources through advancements in data science analytics, drones, web-based platforms, and balloons. It emphasizes the importance of promoting research efforts, and the integration of remote sensing data with spatial patterns, ecosystem services, and hydrological models enables a more comprehensive evaluation of water quantity and quality changes. Continued advancements in remote sensing technology and methodologies will further improve our ability to assess and monitor the impacts of LULC changes on water quality and quantity, ultimately leading to more informed decision making and effective water resource management. Such research endeavors are crucial for achieving the effective and sustainable management of water quality and quantity. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Function and Traits)

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