Using the plant height and canopy coverage to estimation maize aboveground biomass with UAV digital images

European Journal of Agronomy, Journal Year: 2023, Volume and Issue: 151, P. 126957 - 126957

Published: Sept. 9, 2023

Language: Английский

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Quantifying how topography impacts vegetation indices at various spatial and temporal scales

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 312, P. 114311 - 114311

Published: Aug. 3, 2024

Satellite-derived vegetation indices (VIs) have been extensively used in monitoring dynamics at local, regional, and global scales. While numerous studies explored various factors influencing VIs, a remarkable knowledge gap persists concerning their applicability mountain areas with complex topographic variations. Here we bridge this by conducting comprehensive evaluation of the effects on ten widely VIs. We three strategies, including: (i) an analytic radiative transfer model; (ii) 3D ray-tracing (iii) Moderate Resolution Imaging Spectroradiometer (MODIS) products. The two models provided theoretical results under specific terrain conditions, aiding first exploration interactions both shadow spatial scale MODIS-based quantified discrepancies VIs between MODIS-Terra MODIS-Aqua over flat rugged terrains, providing new insights into real satellite data across different temporal scales (i.e., from daily to multiple years). Our were consistent these revealing key findings. normalized difference index (NDVI) generally outperformed other yet all did not perform well (e.g., mean relative error (MRE) 14.7% for NDVI non-shadow 26.1% areas). impacts exist spatiotemporal For example, MREs reached 28.5% 11.1% 30 m 3 km resolutions, respectively. quarterly annual deviations also increased slope. found topography-induced interannual variations simulated MODIS data. trend Tibetan Plateau 2003 2020 as slope steepened enhanced (EVI) doubled). Overall, sun-target-sensor geometry changes induced topography, causing shadows mountains along obstructions sensor observations, compromised reliability terrains. study underscores considerable particularly effects, scales, highlighting imperative cautious application VIs-based calculation mountains.

Language: Английский

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Validating GEDI tree canopy cover product across forest types using co-registered aerial LiDAR data

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 207, P. 326 - 337

Published: Jan. 1, 2024

Language: Английский

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Estimation of canopy photon recollision probability from airborne laser scanning

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 311, P. 114264 - 114264

Published: June 12, 2024

Language: Английский

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A comprehensive survey on quantifying non-photosynthetic vegetation cover and biomass from imaging spectroscopy

Ecological Indicators, Journal Year: 2023, Volume and Issue: 155, P. 110911 - 110911

Published: Sept. 26, 2023

Non-photosynthetic vegetation (NPV) is considered a key quantifiable variable in the context of new spaceborne imaging spectroscopy missions. Knowledge NPV essential for all terrestrial ecosystems, and its mapping beneficial agriculture forestry. In agriculture, crop residues (CR) play an important role tillage, erosion control soil health management. forest management, supports understanding dynamics ecological processes such as fire, erosion, land use changes. Whereas fractional cover has been extensively studied across managed natural so far little attention paid to quantification biomass senescent material. this comprehensive survey, we summarize past attempts quantify landscape-scale or CR (in %) g/m2) from optical Earth observation data, with particular focus on hyperspectral data exploitation. Given three decades studies detection, identify following methodological trends: (1) shift unmixing approaches towards regression-based models; (2) two-band indices multi-band equations; (3) linear regression data-driven machine learning models. (4) addition, gradual progress radiative transfer modelling (RTM) describing interaction radiation non-photosynthetic plant material achieved. These trends have enabled merely identifying presence explicit over croplands grasslands. We highlight potential recent upcoming next-generation missions their unprecedented enhanced multispectral streams propose implement efficient workflows operational delivery global products along associated uncertainties. summary, survey emphasizes significance way support management croplands, grasslands, forests, vegetation.

Language: Английский

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Vegetation coverage precisely extracting and driving factors analysis in drylands

Ecological Informatics, Journal Year: 2023, Volume and Issue: 79, P. 102409 - 102409

Published: Dec. 7, 2023

Fractional Vegetation Coverage (FVC) is an essential indicator that captures variations in vegetation and documents the impacts of climate change human activity for environmental assessment. However, conventional methods encounter challenges accurately extracting fine-scale FVC drylands due to distribution being very heterogeneous space with patches inter-patches. Using lower Tarim River Basin as a typical study case, we investigated three deep convolutional neural networks—Unet, Pspnet, Deeplabv3 + —to generate high-precision high-resolution (0.8 m) remote sensing images. Among these models, Unet model performed better, accuracy 93.38%, while Pspnet Deeplabv3+ was 88.14% 88.91%, respectively. Comparison derived from normalized difference index (NDVI), land use/land cover data ESRI ESA indicated map produced by more consistent on-site field observations. Delving into drivers influencing dryland FVC, found groundwater depth plays pivotal role compared topographical climatic variables. Specifically, when exceeds −3 m, probability occurring high reduced 50%. This innovatively extracted spatial heterogeneity, which better solves insufficient existing dataset, serves valuable reference monitoring change, facilitates precise quantification carbon storage.

Language: Английский

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Validation of the vertical canopy cover profile products derived from the GEDI over selected forest sites

Science of Remote Sensing, Journal Year: 2024, Volume and Issue: 10, P. 100158 - 100158

Published: Sept. 1, 2024

Canopy cover (CC) quantifies the proportion of canopy materials projected vertically onto ground surface. CC is a crucial structural variable and commonly used in many ecological climatic models. The vertical profile product currently available from Global Ecosystem Dynamics Investigation (GEDI). However, detailed information about accuracy uncertainty GEDI remains limited. objective this study to validate over selected forest sites using reference values derived digital hemispherical photography (DHP), airborne laser scanning (ALS) point clouds, simulated waveforms. was quantified analyzed regarding observation conditions, waveform processing, estimation methods. results show that total correlates well with those estimated DHP, ALS, data (r2 = 0.65, 0.71, respectively) but systematically underestimated (bias −0.05, −0.11, −0.07, based on data. Compared ALS-estimated CC, needleleaf shows highest correlation for ≥ 0.65) shrubland lowest bias −0.13). mean absolute error (MAE) decreases 0.15 0.09 35 m. CCs interpretation algorithms A2 A6 display r2 (≥ 0.6) smallest RMSE (≤ 0.23) compared other algorithms. improved at moderate canopy-to-background backscattering coefficient ratio () determined regression method. increases beam sensitivity increasing cover. partial difference between ALS attributed definition differences. Further improvement algorithm can be made by vegetation-specific processing realistic values.

Language: Английский

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A Three-Dimensional Conceptual Model for Estimating the Above-Ground Biomass of Winter Wheat Using Digital and Multispectral Unmanned Aerial Vehicle Images at Various Growth Stages

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(13), P. 3332 - 3332

Published: June 29, 2023

The timely and accurate estimation of above-ground biomass (AGB) is crucial for indicating crop growth status, assisting management decisions, predicting grain yield. Unmanned aerial vehicle (UAV) remote sensing technology a promising approach monitoring biomass. However, the determination winter wheat AGB based on canopy reflectance affected by spectral saturation effects. Thus, constructing generic model accurately estimating using UAV data significant. In this study, three-dimensional conceptual (3DCM) was constructed plant height (PH) fractional vegetation cover (FVC). Compared with both traditional index multi-feature combination model, 3DCM yielded best accuracy jointing stage (based RGB data: coefficient (R2) = 0.82, normalized root mean square error (nRMSE) 0.2; multispectral (MS) R2 0.84, nRMSE 0.16), but decreased significantly when spike organ appeared. Therefore, number (SN) added to create new (n3DCM). Under different stages platforms, n3DCM (RGB: 0.73–0.85, 0.17–0.23; MS: 0.77–0.84, 0.17–0.23) remarkably outperformed 0.67–0.88, 0.15–0.25; 0.60–0.77, 0.19–0.26) AGB. This study suggests that has great potential in resolving errors parameters, which could be extended other crops regions field-based high-throughput phenotyping.

Language: Английский

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Stratified burn severity assessment by integrating spaceborne spectral and waveform attributes in Great Xing'an Mountain

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 307, P. 114152 - 114152

Published: April 15, 2024

Language: Английский

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Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 135, P. 104285 - 104285

Published: Nov. 27, 2024

Language: Английский

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