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Vegetation Biophysical Variables and Remote Sensing Applications

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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 (21 July 2023) | Viewed by 23531

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Special Issue Editors

Dr. Jia Sun

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Guest Editor

School of Geography and Information Engineering, China University of Geoscience, Wuhan 430079, China
Interests: leaf functional traits; vegetation radiative transfer modelling; vegetation dynamics; hyperspectral lidar

Dr. Ximing Ren

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Guest Editor

Micron School of Materials Science and Engineering, College of Engineering, Boise State University, Boise, ID 83725, USA
Interests: time-correlated single-photon counting; time-resolved fluorescent spectroscopy; full-waveform depth profiling; computational imaging

Dr. Chunbo Huang

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Guest Editor

State Key Laboratory of Biogeology and Environmental Geology, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: environmental geography; ecological restoration; ecosystem services; urban environments; landscape planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing provides high throughput, non-destructive, and cost-efficient measurements and data to estimate vegetation biophysical variables, e.g., leaf area index, leaf inclination angles, biomass, and gross primary production. The estimates of vegetation biophysical variables are fundamental to assess the dynamic response of vegetation to changing environmental conditions and anthropogenic influences. Scientists are not only interested in the land-based vegetation, but also ocean-based. The complete recovery of all structural and physiological parameters of vegetation is ill posed with single spectral or single sensor data. Thus, scientists are using time-series and multi-sensor data acquired by remote sensing observations to improve the accuracy of vegetation structures and biophysical variables retrieval. The rich data also allow us to explore the fusion between multi-sensor in various spectral, spatial, and temporal domains in order to better retrieve vegetation properties in multiple dimensions and boost new application of vegetation trait products.

This Special Issue aims at studies covering retrieval of vegetation biophysical variables based on remote sensing tools and platforms and relevant applications in crop/forest sciences. Topics may cover anything from estimation of vegetation variables at different scales to ecological application using vegetation variables. Hence, the Issue welcomes vegetation biophysical properties remote sensing research, especially, but not limited to:

(1) using multiple-dimensional data (e.g., three spatial dimensions, polarization and spectrum) from one platform;

(2) multisource data integration (e.g., multispectral/hyperspectral, thermal, LiDAR, SAR, and sonar data) between various platforms;

(3) multi-temporal, multiscale systems, approaches, or studies focused on precision agriculture/forest dynamic monitoring and ecosystem services assessment.

Dr. Jia Sun
Dr. Ximing Ren
Dr. Chunbo Huang
Guest Editors

Manuscript Submission Information

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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 biophysical variables
parameter retrieval
data fusion
vegetation dynamics
carbon cycling
LiDAR
SAR
precision agriculture
ecological modelling
machine learning

Published Papers (11 papers)

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Research

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18 pages, 2690 KiB

Open AccessArticle

Novel Hybrid Model to Estimate Leaf Carotenoids Using Multilayer Perceptron and PROSPECT Simulations

by Weilin Hao, Jia Sun, Zichao Zhang, Kan Zhang, Feng Qiu and Jin Xu

Remote Sens. 2023, 15(20), 4997; https://doi.org/10.3390/rs15204997 - 17 Oct 2023

Cited by 1 | Viewed by 906

Abstract

Leaf carotenoids (Cxc) play a crucial role in vegetation as essential pigments responsible for capturing sunlight and protecting leaf tissues. They provide vital insights into a plant physiological status and serve as sensitive indicators of plant stress. However, remote sensing of Cxc at the leaf level has been challenging due to the low Cxc content and weaker absorption features compared to those of chlorophylls in the visible domain. Existing vegetation indices have been widely applied but often lack a solid physical foundation, which limits their applicability and robustness in characterizing Cxc. Yet, physical models can confront this ill-posed problem, though with high operational costs. To address this issue, this study presents a novel hybrid inversion method that combines the multilayer perceptron (MLP) algorithm with PROSPECT model simulations to accurately retrieve Cxc. The effectiveness of the MLP method was investigated through comparisons with the classical PROSPECT model inversion (look-up table [LUT] method), the convolutional neural network (CNN) hybrid model, and the Transformer hybrid model. In the pooled results of six experimental datasets, the MLP method exhibited its robustness and generalization capabilities for leaf Cxc content estimation, with RMSE of 3.12 μg/cm² and R² of 0.52. The Transformer (RMSE = 3.14 μg/cm², R² = 0.46), CNN (RMSE = 3.42 μg/cm², R² = 0.28), and LUT (RMSE = 3.82 μg/cm², R² = 0.24) methods followed in descending order of accuracy. A comparison with previous studies using the same public datasets (ANGERS and LOPEX) also demonstrated the performance of the MLP method from another perspective. These findings underscore the potential of the proposed MLP hybrid method as a powerful tool for accurate Cxc retrieval applications, providing valuable insights into vegetation health and stress response. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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21 pages, 5222 KiB

Open AccessArticle

Multi-Dimensional Spatial and Temporal Variations of Ecosystem Service Values in the Li River Basin, 1990–2020

by Jinlong Hu, Sicheng Qiu, Nan Luo, Guo Qing and Chunbo Huang

Remote Sens. 2023, 15(12), 2996; https://doi.org/10.3390/rs15122996 - 8 Jun 2023

Cited by 3 | Viewed by 1132

Abstract

Changing landscape patterns would alter ecosystem components and functions, affecting the supply of ecosystem services. Understanding the spatial and temporal variations of ecosystem services is an important basis for ecosystem management and planning decisions and is of great significance for the realization of regional sustainable development. Based on Landsat TM/OLI remote sensing images from 1990, 2000, 2010, and 2020 in the Li River Basin, we explored the spatial and temporal variabilities of ecosystem services in the Li River Basin over the past 30 years, from both horizontal and vertical dimensions, using modified equivalence factor method and spatial autocorrelation analysis. The research findings are as follows: (1) Forestland has consistently been the dominant landscape type in the Li River Basin, with its area continuously increasing, while farmland, water bodies, and grassland have decreased, and construction land and bare land have increased. (2) The value of ecosystem services in the Li River Basin exhibited an initial increase followed by a decrease trend, with a net increase of 9.20 × 10⁸ yuan. Forestland contributed the most to the value of ecosystem services. (3) Hydrological regulation and climate regulation are the dominant functions of the Li River Basin’s ecosystems, accounting for over 50% of the total contribution. (4) The value of ecosystem services per unit area increases with increasing slope and elevation. The segments with slopes ranging from 15 to 25 degrees and elevation zones between 200 and 500 m have the highest total value of ecosystem services. (5) The overall level of ecosystem services in the Li River Basin is relatively high and continues to rise, but areas with a low ecosystem service value are gradually concentrated. (6) The Moran’s I values for ecosystem services in all four periods are greater than 0, indicating a significant positive spatial autocorrelation. The overall pattern of ecosystem services is relatively stable, but there are significant spatial variations, which are characterized by lower values in the central area and higher values in the surrounding areas. The research findings provide a scientific basis for watershed ecological environment construction, optimal allocation of land resources, and sustainable landscape management. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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17 pages, 8483 KiB

Open AccessArticle

Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery

by Weibo Shi, Xiaohan Liao, Jia Sun, Zhengjian Zhang, Dongliang Wang, Shaoqiang Wang, Wenqiu Qu, Hongbo He, Huping Ye, Huanyin Yue and Torbern Tagesson

Remote Sens. 2023, 15(8), 2205; https://doi.org/10.3390/rs15082205 - 21 Apr 2023

Cited by 1 | Viewed by 1488

Abstract

Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under different months. For a case study area in Wanglang Nature Reserve, Southwest China, we acquired monthly RGB and multispectral images on six occasions over the growing season. We found that the accuracy of RGB imagery varied considerably (the highest intersection over union (IoU), 83.72%, was in April and the lowest, 76.81%, was in June), while the accuracy of multispectral imagery was consistently high (IoU > 81%). In April and October, the accuracy of the RGB imagery was slightly higher than that of multispectral imagery, but for the other months, multispectral imagery was more accurate (IoU was nearly 6% higher than those of the RGB imagery for June). Adding vegetation indices (VIs) improved the accuracy of the RGB models during summer, but there was still a gap to the multispectral model. Hence, our results indicate that the optimized time of the year for identifying Faxon fir using UAV imagery is during the peak of the growing season when using a multispectral imagery. During the non-growing season, RGB imagery was no worse or even slightly better than multispectral imagery for Faxon fir identification. Our study can provide guidance for optimizing observation plans regarding data collection time and UAV loads and could further help enhance the utility of UAVs in forestry and ecological research. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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20 pages, 2812 KiB

Open AccessArticle

Exploring the Best-Matching Plant Traits and Environmental Factors for Vegetation Indices in Estimates of Global Gross Primary Productivity

by Weiqing Zhao and Zaichun Zhu

Remote Sens. 2022, 14(24), 6316; https://doi.org/10.3390/rs14246316 - 13 Dec 2022

Cited by 5 | Viewed by 1822

Abstract

As the largest source of uncertainty in carbon cycle studies, accurate quantification of gross primary productivity (GPP) is critical for the global carbon budget in the context of global climate change. Numerous vegetation indices (VIs) based on satellite data have participated in the construction of GPP models. However, the relative performance of various VIs in predicting GPP and what additional factors should be combined with them to reveal the photosynthetic capacity of vegetation mechanistically better are still poorly understood. We constructed two types of models (universal and plant functional type [PFT]-specific) for solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation (NIR_v), and Leaf Area Index (LAI) based on two widely used machine learning algorithms, i.e., the random forest (RF) and back propagation neural network (BPNN) algorithms. A total of thirty plant traits and environmental factors with legacy effects are considered in the model. We then systematically investigated the ancillary variables that best match each vegetation index in estimating global GPP. Four types of models (universal and PFT-specific, RF and BPNN) consistently show that SIF performs best when modeled using a single vegetation index (R² = 0.67, RMSE = 2.24 g C·m⁻²·d⁻¹); however, NIR_v combined with CO₂, plant traits, and climatic factors can achieve the highest prediction accuracy (R² = 0.87, RMSE = 1.40 g C·m⁻²·d⁻¹). Plant traits effectively enhance all prediction models’ accuracy, and climatic variables are essential factors in improving the accuracy of NIRv- or LAI-based GPP models, but not the accuracy of SIF-based models. Our findings provide valuable information for the configuration of the data-driven models to improve the accuracy of predicting GPP and provide insights into the physiological and ecological mechanisms underpinning GPP prediction. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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19 pages, 4460 KiB

Open AccessArticle

An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data

by Ting Gao, Zhihai Gao, Bin Sun, Pengyao Qin, Yifu Li and Ziyu Yan

Remote Sens. 2022, 14(17), 4317; https://doi.org/10.3390/rs14174317 - 1 Sep 2022

Cited by 12 | Viewed by 1976

Abstract

Forest-canopy closure (FCC) reflects the coverage of the forest tree canopy, which is one of the most important indicators of forest structure and a core parameter in forest resources investigation. In recent years, the rapid development of UAV LiDAR and photogrammetry technology has provided effective support for FCC estimation. However, affected by factors such as different tree species and different stand densities, it is difficult to estimate FCC accurately based on the single-tree canopy-contour method in complex forest regions. Thus, this study proposes a method for estimating FCC accurately using algorithm integration with an optimal window size for treetop detection and an optimal algorithm for crown-boundary extraction using UAV LiDAR data in various scenes. The research results show that: (1) The FCC estimation accuracy was improved using the method proposed in this study. The accuracy of FCC in a camphor pine forest (Pinus sylvestris var. mongolica Litv.) was 89.11%, with an improvement of 6.77–11.25% compared to the results obtained from other combined conditions. The FCC accuracy for white birch (White birch platyphylla Suk) was about 87.53%, with an increase of 3.25–8.42%. (2) The size of the window used for treetop detection is closely related to tree species and stand density. With the same forest-stand density, the treetop-detection window size of camphor pine was larger than that of white birch. The optimal window size of camphor pine was between 5 × 5~11 × 11 (corresponding 2.5~5.5 m), while that of white birch was between 3 × 3~7 × 7 (corresponding 1.5~3.5 m). (3) There are significant differences in the optimal-canopy-outline extraction algorithms for different scenarios. With a medium forest-stand density, the marker-controlled watershed (MCW) algorithm has the best tree-crown extraction effect. The region-growing (RG) method has better extraction results in the sparse areas of camphor pine and the dense areas of white birch. The Voronoi tessellation (VT) algorithm is more suitable for the dense areas of camphor pine and the sparse regions of white birch. The method proposed in this study provides a reference for FCC estimation using high-resolution remote-sensing images in complex forest areas containing various scenes. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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21 pages, 5868 KiB

Open AccessArticle

Extraction of Citrus Trees from UAV Remote Sensing Imagery Using YOLOv5s and Coordinate Transformation

by Haoxin Tian, Xipeng Fang, Yubin Lan, Chenyang Ma, Huasheng Huang, Xiaoyang Lu, Dehua Zhao, Hanchao Liu and Yali Zhang

Remote Sens. 2022, 14(17), 4208; https://doi.org/10.3390/rs14174208 - 26 Aug 2022

Cited by 11 | Viewed by 2485

Abstract

Obtaining the geographic coordinates of single fruit trees enables the variable rate application of agricultural production materials according to the growth differences of trees, which is of great significance to the precision management of citrus orchards. The traditional method of detecting and positioning fruit trees manually is time-consuming, labor-intensive, and inefficient. In order to obtain high-precision geographic coordinates of trees in a citrus orchard, this study proposes a method for citrus tree identification and coordinate extraction based on UAV remote sensing imagery and coordinate transformation. A high-precision orthophoto map of a citrus orchard was drawn from UAV remote sensing images. The YOLOv5 model was subsequently used to train the remote sensing dataset to efficiently identify the fruit trees and extract tree pixel coordinates from the orchard orthophoto map. According to the geographic information contained in the orthophoto map, the pixel coordinates were converted to UTM coordinates and the WGS84 coordinates of citrus trees were obtained using Gauss–Krüger inverse calculation. To simplify the coordinate conversion process and to improve the coordinate conversion efficiency, a coordinate conversion app was also developed to automatically implement the batch conversion of pixel coordinates to UTM coordinates and WGS84 coordinates. Results show that the Precision, Recall, and F1 Score for Scene 1 (after weeding) reach 0.89, 0.97, and 0.92, respectively; the Precision, Recall, and F1 Score for Scene 2 (before weeding) reach 0.91, 0.90 and 0.91, respectively. The accuracy of the orthophoto map generated using UAV remote sensing images is 0.15 m. The accuracy of converting pixel coordinates to UTM coordinates by the coordinate conversion app is reliable, and the accuracy of converting UTM coordinates to WGS84 coordinates is 0.01 m. The proposed method is capable of automatically obtaining the WGS84 coordinates of citrus trees with high precision. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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24 pages, 6003 KiB

Open AccessArticle

Quantifying the Influences of Driving Factors on Vegetation EVI Changes Using Structural Equation Model: A Case Study in Anhui Province, China

by Zhengnan Gu, Zhen Zhang, Junhua Yang and Leilei Wang

Remote Sens. 2022, 14(17), 4203; https://doi.org/10.3390/rs14174203 - 26 Aug 2022

Cited by 18 | Viewed by 1992

Abstract

Vegetation cover is important to the stability of regional ecosystems and is a focus of research on the relationship between natural and human environments. Although some studies have investigated the association between changes in vegetation cover and various influencing factors, these have shortcomings in quantifying direct and indirect effects. In this study, MOD13Q1 enhanced vegetation index (EVI) data for Anhui Province, China, were acquired between 2000 and 2020. The univariate linear regression, coefficient of variation and Hurst index methods were used to analyze spatial and temporal trends and fluctuations in the EVI between 2000 and 2020 and predict future trends. The impact of land-use change on EVI change was explored using 2000 and 2020 land-use data. Finally, a structural equation model (SEM) was used to quantify the effects of topography, annual average temperature, annual precipitation and human activity changes on EVI variation in Anhui Province. The results show that (1) from 2000 to 2020, the overall EVI in Anhui Province showed a fluctuating trend that increased at a rate of 0.0181·10a⁻¹, and 67.1% of the study area showed a greening trend. The EVI was relatively stable in most regions, with regions of fluctuating EVI being mostly affected by urbanization. For a period after 2020, the overall EVI change will exhibit anti-sustainability and will likely decrease. (2) Among the regions of EVI increase, 72.2% had no change in land-use type, while 10.8% and 6.6% changed to farmland and woodland land uses, respectively. Among the regions where EVI decreased, 69.9% had no change in land-use type, while 13.7% changed from farmland to construction land. (3) Overall, human activity change was the main influence on EVI change, which was mainly reflected in the negative impacts of accelerated urbanization. Topography had direct and indirect effects on EVI variations in Central and Southern Anhui. Annual precipitation change had a stronger impact on EVI variation in Northern and Central Anhui than in Southern Anhui, while annual average temperature change had a small impact in the entire province. Compared with other study methods, SEM provides a new approach to quantifying the influences of vegetation cover dynamics. In addition, the results of this study have important implications for ecological environmental protection and sustainable development in Anhui Province. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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17 pages, 7641 KiB

Open AccessArticle

Attribution of NDVI Dynamics over the Globe from 1982 to 2015

by Cuiyan Liu, Jianyu Liu, Qiang Zhang, Hui Ci, Xihui Gu and Aminjon Gulakhmadov

Remote Sens. 2022, 14(11), 2706; https://doi.org/10.3390/rs14112706 - 4 Jun 2022

Cited by 12 | Viewed by 2164

Abstract

Satellite remote sensing has witnessed a global widespread vegetation greening since the 1980s. However, reliable observation-based quantitative knowledge on global greening remains obscure due to uncertainties in model simulations and the contribution of natural variability is largely unknown. Here, we revisit the attribution of global vegetation changes using the Time Series Segment and Residual Trend (TSS-RESTREND) method. Results showed global vegetation significantly greening over 40.6% of the vegetated grids, whereas vegetation significantly browning over 11.6% of the vegetated grids. The attribution results based on the TSS-RESTREND method show that CO₂ fertilization (CO₂) plays an influential role in vegetation changes over 61.4% of the global vegetated areas, followed by land use (LU, 23.5%), climate change (CC, 7.3%), and climate variability (CV, 1.5%). The vegetation greening can be largely attributed to CO₂ fertilization while the vegetation browning is mainly caused by LU. Meanwhile, we also identify positive impacts of LU and CC on vegetation change in arid regions but negative impacts in humid regions. Our findings indicate spatial heterogeneity in causes behind global vegetation changes, providing more detailed references for global vegetation modeling. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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23 pages, 7190 KiB

Open AccessArticle

Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China

by Liang Liang, Di Geng, Juan Yan, Siyi Qiu, Yanyan Shi, Shuguo Wang, Lijuan Wang, Lianpeng Zhang and Jianrong Kang

Remote Sens. 2022, 14(8), 1902; https://doi.org/10.3390/rs14081902 - 14 Apr 2022

Cited by 26 | Viewed by 2684

Abstract

Net ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this paper, the key parameters of the Carnegie Ames Stanford Approach (CASA) model, maximum light use efficiency (ε_max), was optimized by using vegetation classification data. Then, the NEP was estimated by coupling the optimized CASA model, geostatistical model of soil respiration (GSMSR) and the soil respiration–soil heterotrophic respiration (R_s-R_h) relationship model. The ground observations from ChinaFLUX were used to verify the NEP estimation accuracy. The results showed that the R² of the optimized CASA model increased from 0.411 to 0.774, and RMSE decreased from 21.425 gC·m⁻²·month⁻¹ to 12.045 gC·m⁻²·month⁻¹, indicating that optimizing CASA model by vegetation classification data was an effective method to improve the estimation accuracy of NEP. On this basis, the spatial and temporal distribution of NEP in China was analyzed. The research indicated that the monthly variation of NEP in China was a single peak curve with summer as the peak, which generally presented the pattern of southern region > northern region > Qinghai–Tibet region > northwest region. Furthermore, from 2001 to 2016, most regions of China showed a non-significant level upward trend, but main cropland (e.g., North China Plain and Northeast Plain) and some grassland (e.g., Ngari in Qinghai–Tibet Plateau and Xilin Gol League in Inner Mongolia) showed a non-significant-level downward trend. The study can deepen the understanding of the distribution of carbon sources/sinks in China, and provide a reference for regional carbon cycle research. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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Review

Jump to: Research

38 pages, 7384 KiB

Open AccessReview

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

by Aliasghar Bazrafkan, Nadia Delavarpour, Peter G. Oduor, Nonoy Bandillo and Paulo Flores

Remote Sens. 2023, 15(14), 3543; https://doi.org/10.3390/rs15143543 - 14 Jul 2023

Cited by 4 | Viewed by 1824

Abstract

Conventional measurement methods for above-ground biomass (AGB) are time-consuming, inaccurate, and labor-intensive. Unmanned aerial systems (UASs) have emerged as a promising solution, but a standardized procedure for UAS-based AGB estimation is lacking. This study reviews recent findings (2018–2022) on UAS applications for AGB estimation and develops a vegetation type-specific standard protocol. Analysis of 211 papers reveals the prevalence of rotary-wing UASs, especially quadcopters, in agricultural fields. Sensor selection varies by vegetation type, with LIDAR and RGB sensors in forests, and RGB, multispectral, and hyperspectral sensors in agricultural and grass fields. Flight altitudes and speeds depend on vegetation characteristics and sensor types, varying among crop groups. Ground control points (GCPs) needed for accurate AGB estimation differ based on vegetation type and topographic complexity. Optimal data collection during solar noon enhances accuracy, considering image quality, solar energy availability, and reduced atmospheric effects. Vegetation indices significantly affect AGB estimation in vertically growing crops, while their influence is comparatively less in forests, grasses, and horizontally growing crops. Plant height metrics differ across vegetation groups, with maximum height in forests and vertically growing crops, and central tendency metrics in grasses and horizontally growing crops. Linear regression and machine learning models perform similarly in forests, with machine learning outperforming in grasses; both yield comparable results for horizontally and vertically growing crops. Challenges include sensor limitations, environmental conditions, reflectance mixture, canopy complexity, water, cloud cover, dew, phenology, image artifacts, legal restrictions, computing power, battery capacity, optical saturation, and GPS errors. Addressing these requires careful sensor selection, timing, image processing, compliance with regulations, and overcoming technical limitations. Insights and guidelines provided enhance the precision and efficiency of UAS-based AGB estimation. Understanding vegetation requirements aids informed decisions on platform selection, sensor choice, flight parameters, and modeling approaches across different ecosystems. This study bridges the gap by providing a standardized protocol, facilitating widespread adoption of UAS technology for AGB estimation. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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15 pages, 1971 KiB

Open AccessReview

Determination of the Leaf Inclination Angle (LIA) through Field and Remote Sensing Methods: Current Status and Future Prospects

by Sijia Li, Hongliang Fang and Yinghui Zhang

Remote Sens. 2023, 15(4), 946; https://doi.org/10.3390/rs15040946 - 9 Feb 2023

Cited by 4 | Viewed by 2905

Abstract

The leaf inclination angle (LIA), defined as the leaf or needle inclination angle to the horizontal plane, is vital in radiative transfer, precipitation interception, evapotranspiration, photosynthesis, and hydrological processes. This paper reviews the field and remote sensing methods to determine LIA. In the field, LIA is determined using direct and indirect methods. The direct methods include direct contact, photographic, and light detection and ranging (LiDAR) methods, while the indirect methods are composed of the gap fraction, four-component, and polarization measurement methods. The direct methods can obtain LIA accurately at individual leaves, crown, and plot scales, whereas the indirect methods work well for crops at the plot level. The remote sensing methods to estimate LIA are mainly based on the empirical, radiative transfer model, and gap fraction methods. More advanced inversion strategies and validation studies are necessary to improve the robustness of LIA remote sensing estimation. In future studies, automated observation systems can be developed and the LIA measurement can be incorporated into existing ground observation networks to enhance spatial coverage. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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