Synergy of UAV-LiDAR Data and Multispectral Remote Sensing Images for Allometric Estimation of Phragmites Australis Aboveground Biomass in Coastal Wetland DOI Creative Commons

Chentian Ge,

Chao Zhang, Yuan Zhang

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(16), С. 3073 - 3073

Опубликована: Авг. 21, 2024

Quantifying the vegetation aboveground biomass (AGB) is crucial for evaluating environment quality and estimating blue carbon in coastal wetlands. In this study, a UAV-LiDAR was first employed to quantify canopy height model (CHM) of Phragmites australis (common reed). Statistical correlations were explored between two multispectral remote sensing data (Sentinel-2 JL-1) reed biophysical parameters (CHM, density, AGB) estimated from data. Consequently, AGB separately mapped with UAV-LiDAR, Sentinel-2, JL-1 through allometric equations (AEs). Results show that UAV-LiDAR-derived CHM at pixel size 4 m agrees well observed stem (R2 = 0.69). Reed positively correlates basal diameter negatively plant density. The optimal inversion derived Sentinel-2 R2 0.58, RMSE 216.86 g/m2 0.50, 244.96 g/m2, respectively. This study illustrated synergy images has great potential monitoring.

Язык: Английский

Toward a better understanding of coastal salt marsh mapping: A case from China using dual-temporal images DOI
Chuanpeng Zhao,

Mingming Jia,

Zongming Wang

и другие.

Remote Sensing of Environment, Год журнала: 2023, Номер 295, С. 113664 - 113664

Опубликована: Июнь 15, 2023

Язык: Английский

Процитировано

47
Cross-scale mapping of above-ground biomass and shrub dominance by integrating UAV and satellite data in temperate grassland DOI
Ang Chen, Cong Xu, Min Zhang

и другие.

Remote Sensing of Environment, Год журнала: 2024, Номер 304, С. 114024 - 114024

Опубликована: Фев. 7, 2024

Язык: Английский

Процитировано

26
Development of forest aboveground biomass estimation, its problems and future solutions: A review DOI Creative Commons
Taiyong Ma,

Chao Zhang,

Liping Ji

и другие.

Ecological Indicators, Год журнала: 2024, Номер 159, С. 111653 - 111653

Опубликована: Фев. 1, 2024

Forest aboveground biomass (AGB) is crucial as it serves a fundamental indicator of the productivity, biodiversity, and carbon storage forest ecosystems. This paper presents targeted literature review advancements in AGB estimation methods. We conducted an extensive published using Web Science, ResearchGate, Semantic Scholar, Google Scholar. Our findings highlight importance accurate studies terrestrial cycle, ecosystem management, climate change. Moreover, contributes valuable ecological knowledge supports effective natural resource management. Unfortunately, during data collection process for estimation, we have identified two critical yet often overlooked issues: (1) reliability manual survey accuracy, (2) impact overlap between ground plots remote sensing pixels on estimation. Drawing existing technologies analysis, propose potentially solution to address these challenges. In conclusion, mapping parameters, such AGB, will remain priority forestry research foreseeable future. To ensure practical applicability findings, our future efforts focus understanding accuracy determining optimal pixels.

Язык: Английский

Процитировано

20
Aboveground biomass retrieval of wetland vegetation at the species level using UAV hyperspectral imagery and machine learning DOI Creative Commons

Wei Zhuo,

Wu Nan, Runhe Shi

и другие.

Ecological Indicators, Год журнала: 2024, Номер 166, С. 112365 - 112365

Опубликована: Июль 13, 2024

Язык: Английский

Процитировано

11
Towards carbon neutrality: Enhancing CO2 sequestration by plants to reduce carbon footprint DOI
Dawid Skrzypczak,

Katarzyna Gorazda,

Katarzyna Mikula

и другие.

The Science of The Total Environment, Год журнала: 2025, Номер 966, С. 178763 - 178763

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

2
Monitoring of chlorophyll-a and suspended sediment concentrations in optically complex inland rivers using multisource remote sensing measurements DOI Creative Commons
Yi Xiao, Jiahao Chen,

Yue Xu

и другие.

Ecological Indicators, Год журнала: 2023, Номер 155, С. 111041 - 111041

Опубликована: Окт. 9, 2023

In recent decades, phytoplankton proliferation and sediment input to rivers (especially urban rivers) have become more dramatic under the compound pressure of climate change human activities. Given generally narrow width current high spatial resolution satellites, which are limited by band settings, bandwidth, signal-to-noise ratio, UAVs with their exceptional spatiotemporal can be used as a useful tool for river environmental monitoring inversion uncertainty assessment. this study, UAV-based hyperspectral (X20P) multispectral (P4M) images, along Sentinel-2 MultiSpectral Instrument (MSI), Landsat-8 Operational Land Imager (OLI) Landsat-9 OLI2 data, were assess in retrieving chlorophyll-a (Chla) suspended (SS) concentrations rivers. Chla SS models based on UAV satellite data constructed using stepwise multiple regression typical retrieval algorithms, respectively, performance was focus our research. The results demonstrated that concentration inversion, each sensor performed follows: X20P > P4M Landsat9 MSI Landsat8 OLI, OLI. addition, retrievals analyzed assistance model. Results showed bandwidths finely tuned settings essential inversion. algorithm, NDCI, is only effective certain bands (band 1 from 684 724 nm 2 660 680 nm). It also noted lack some key (e.g., red-edge 700–710 nm), severely limiting practical application relation Chla. However, specific variances different relatively small impact example, correlation between R/B (a algorithm) ranged 0.68 0.77. monitoring, other hand, necessitates higher than monitoring. accuracy decreased markedly when images resampled 10 m 30 resolution. it not crucial original (RMSE<30cm = 6.28 mg/L) (RMSE10m 5.85 (RMSE30m 4.08 while increased. Our highlighted various options future SS, exploiting synergy satellites achieve precise observations at greater temporal scales, will benefit aquatic environment management protection.

Язык: Английский

Процитировано

19
A Novel Vegetation Index Approach Using Sentinel-2 Data and Random Forest Algorithm for Estimating Forest Stock Volume in the Helan Mountains, Ningxia, China DOI Creative Commons
Taiyong Ma,

Yang Hu,

Jie Wang

и другие.

Remote Sensing, Год журнала: 2023, Номер 15(7), С. 1853 - 1853

Опубликована: Март 30, 2023

Forest stock volume (FSV) is a major indicator of forest ecosystem health and it also plays an important part in understanding the worldwide carbon cycle. A precise comprehension distribution patterns variations FSV crucial assessment sequestration potential optimization management programs sink. In this study, novel vegetation index based on Sentinel-2 data for modeling with random (RF) algorithm Helan Mountains, China has been developed. Among all other variables correlation coefficient r = 0.778, (NDVIRE) developed red-edge bands was most significant. Meanwhile, model that combined indices (bands + VIs-based model, BVBM) performed best training phase (R2 0.93, RMSE 10.82 m3ha−1) testing 0.60, 27.05 m3ha−1). Using Mountains first mapped accuracy 80.46% obtained. The RF thus effective method to assess FSV. addition, can provide new estimate areas, especially sequestration.

Язык: Английский

Процитировано

18
UAV and Satellite Synergies for Mapping Grassland Aboveground Biomass in Hulunbuir Meadow Steppe DOI Creative Commons
Zhu Xiao-hua, Xinyu Chen, Lingling Ma

и другие.

Plants, Год журнала: 2024, Номер 13(7), С. 1006 - 1006

Опубликована: Март 31, 2024

Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate productivity and carbon stock. Satellite remote sensing technology useful for monitoring dynamic changes in AGB across a wide range grasslands. However, due scale mismatch between satellite observations ground surveys, significant uncertainties biases exist mapping from data. This also common problem low- medium-resolution modeling that has not been effectively solved. The rapid development uncrewed aerial vehicle (UAV) offers way solve this problem. In study, we developed method with UAV synergies estimating filled gap observation surveys successfully mapped Hulunbuir meadow steppe northeast Inner Mongolia, China. First, based on hyperspectral data survey data, UAV-based was estimated using combination typical vegetation indices (VIs) leaf area index (LAI), structural parameter. Then, aggregated as satellite-scale sample set model satellite-based estimation. At same time, spatial information incorporated into LAI inversion process minimize bias Finally, entire experimental analyzed. results show following: (1) random forest (RF) had best performance compared simple regression (SR), partial least squares (PLSR) back-propagation neural network (BPNN) estimation, R2 0.80 RMSE 76.03 g/m2. (2) Grassland estimation through introducing achieved higher accuracy. For improved by average 10% reduced 9%. increased 0.70 0.75 decreased 78.24 g/m2 72.36 (3) Based map, accuracy significantly improved. 0.57 0.75, 99.38 suggests UAVs bridge field measurements providing sufficient training dataset

Язык: Английский

Процитировано

8
The impact of environmental variables on reed stands of the intermittent Lake Cerknica, Slovenia: 40 years of change DOI Creative Commons
Nik Ojdanič, Alenka Gaberščik, Igor Zelnik

и другие.

Ecological Indicators, Год журнала: 2025, Номер 170, С. 113101 - 113101

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
From Air to Space: A Comprehensive Approach to Optimizing Aboveground Biomass Estimation on UAV-Based Datasets DOI Open Access
Muhammad Nouman Khan, Yumin Tan, Lingfeng He

и другие.

Forests, Год журнала: 2025, Номер 16(2), С. 214 - 214

Опубликована: Янв. 23, 2025

Estimating aboveground biomass (AGB) is vital for sustainable forest management and helps to understand the contributions of forests carbon storage emission goals. In this study, effectiveness plot-level AGB estimation using height crown diameter derived from UAV-LiDAR, calibration GEDI-L4A GEDI-L2A rh98 heights, spectral variables UAV-multispectral RGB data were assessed. These calibrated values UAV-derived used fit estimations a random (RF) regression model in Fuling District, China. Using Pearson correlation analysis, we identified 10 most important predictor prediction model, including GEDI height, Visible Atmospherically Resistant Index green (VARIg), Red Blue Ratio (RBRI), Difference Vegetation (DVI), canopy cover (CC), (ARVI), Red-Edge Normalized (NDVIre), Color (CIVI), elevation, slope. The results showed that, general, second based on Sentinel-2 indices, slope datasets with evaluation metric (for training: R2 = 0.941 Mg/ha, RMSE 13.514 MAE 8.136 Mg/ha) performed better than first prediction. result was between 23.45 Mg/ha 301.81 standard error 0.14 10.18 Mg/ha. This hybrid approach significantly improves accuracy addresses uncertainties modeling. findings provide robust framework enhancing stock assessment contribute global-scale monitoring, advancing methodologies ecological research.

Язык: Английский

Процитировано

1