Estimating of chlorophyll fluorescence parameter Fv/Fm for plant stress detection at peatlands under Ramsar Convention with Sentinel-2 satellite imagery

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102603 - 102603

Published: April 17, 2024

Monitoring vegetation is essential in Earth Observation (EO) due to its link with the global carbon cycle, playing a crucial role ecosystem management. The fluorescence of chlorophyll (ChF) reliable indicator plants' photosynthetic activity and growth, especially when they are experiencing unfavourable conditions, particularly terrestrial wetlands. These wetlands integral components landscape, contributing significantly climate mitigation, adaptation, biodiversity, well-being both environment humanity. We conducted research study using XGBoost machine learning algorithm map parameter Fv/Fm Biebrza River Valley, which known for marshes, peatlands, diverse flora fauna. Our highlights benefits ensemble classifiers derived from EO Sentinel-2 satellite imagery accurately mapping across landscapes under Ramsar Convention at Narew Valley (Poland) Čepkeliai Marsh (Lithuania). provides an accurate estimate ChF robust determination coefficient 0.747 minimal bias 0.013, as validated situ data. precision estimation remote sensing sensors depends on growth stage, emphasizing importance identifying optimal overpass time S-2 observations. found that biophysical factors, denoted by spectral indices related greenness leaf pigments, were highly impactful variables among top classifiers. However, incorporating soil, meteorological indicators data could further increase accuracy mapping.

Language: Английский

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Physicochemical Parameters of Water and Its Implications on Avifauna and Habitat Quality

Sustainability, Journal Year: 2023, Volume and Issue: 15(12), P. 9494 - 9494

Published: June 13, 2023

Wetland ecosystems are essential for maintaining biological diversity and significant elements of the global landscape. However, biodiversity wetlands has been significantly reduced by more than 50% worldwide due to rapid expansion urban areas other human activities. The aforementioned factors have resulted in drastic antagonistic effects on species composition, particularly aquatic avifauna. decline wetland avifauna, which can be attributed changes water quality that impact habitats, is a major concern. In this study, we evaluated physicochemical parameters avifauna India’s first Conservation Reserve, Ramsar site an Important Bird Area. Water samples were collected monthly basis across nine different sites various parameters, such as temperature, electrical conductivity, pH, oxygen demand, dissolved oxygen, total solids salinity, analyzed pre-monsoon post-monsoon seasons, while point count surveys conducted assess richness density waterbirds. Our findings show positive correlation with temperature (r = 0.57), 0.56) 0.6) season negative −0.62) demand −0.69) season. We suggest synergistic effect may affect bird populations Asan Reserve. Poor was observed few sampling sites, negatively number waterbirds present. study emphasize importance conservation,

Language: Английский

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Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(4), P. 647 - 647

Published: Feb. 9, 2024

In this study, we employ in situ, meteorological, and remote sensing data to estimate chlorophyll-a concentration at different depths a South American freshwater ecosystem, focusing specifically on lake southern Chile known as Lake Maihue. For our analysis, explored four scenarios using three deep learning traditional statistical models. These involved field (Scenario 1), meteorological variables 2), satellite (Scenarios 3.1 3.2) predict levels Maihue (0, 15, 30 m). Our choice of models included SARIMAX, DGLM, LSTM, all which showed promising performance predicting concentrations lake. Validation metrics for these indicated their effectiveness chlorophyll levels, serve valuable indicators the presence algae water body. The coefficient determination values ranged from 0.30 0.98, with DGLM model showing most favorable statistics tested. It is worth noting that LSTM yielded comparatively lower metrics, mainly due limitations available training data. employed, use machine data, have great potential application lakes rest world similar characteristics. addition, results constitute fundamental resource decision-makers protection conservation quality.

Language: Английский

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Monitoring tropical forest change using tree canopy cover time series obtained from Sentinel-1 and Sentinel-2 data

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Feb. 5, 2024

The most practical method for monitoring forest change over large areas is using remotely sensed data. However, given that current techniques are somewhat weak small-scale disturbances, achieving accurate remains challenging, especially in tropical where selective and illegal logging occurs frequently. To further improve the ability to monitor changes, we estimated tree canopy cover (TCC) Sentinel-1 Sentinel-2 We developed an approach on obtained TCC time series. This was applied Bago Mountains of Myanmar from 2017 2021. then completed accuracy assessments area estimation reference data stratified random sampling unbiased estimators. final results indicated that: (1) estimation, played a limited role; red-edge bands achieved slightly different other bands, superior were by all bands; (2) our successfully mapped with overall 93%. Furthermore, compared widely used recent approaches, better at capturing disturbances.

Language: Английский

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Investigating the Response of Vegetation to Flash Droughts by Using Cross-Spectral Analysis and an Evapotranspiration-Based Drought Index

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(9), P. 1564 - 1564

Published: April 28, 2024

Flash droughts tend to cause severe damage agriculture due their characteristics of sudden onset and rapid intensification. Early detection the response vegetation flash is utmost importance in mitigating effects droughts, as it can provide a scientific basis for establishing an early warning system. The commonly used method determining time drought, based on index or correlation between precipitation anomaly growth anomaly, leads late irreversible drought vegetation, which may not be sufficient use analyzing earning. evapotranspiration-based (ET-based) indices are effective indicator identifying monitoring drought. This study proposes novel approach that applies cross-spectral analysis ET-based index, i.e., Evaporative Stress Anomaly Index (ESAI), forcing vegetation-based Normalized Vegetation (NVAI), response, both from medium-resolution remote sensing data, estimate lag vitality status An experiment was carried out North China during March–September period 2001–2020 using products at 1 km spatial resolution. results show average water availability estimated by over 5.9 days, shorter than measured widely (26.5 days). main difference phase lies fundamental processes behind definitions two methods, subtle dynamic fluctuation signature signal (vegetation-based index) correlates with (ET-based versus impact indicated negative NDVI anomaly. varied types irrigation conditions. rainfed cropland, irrigated grassland, forest 5.4, 5.8, 6.1, 6.9 respectively. Forests have longer grasses crops deeper root systems, mitigate impacts droughts. Our method, innovative earlier impending impacts, rather waiting occur. information detected stage help decision makers developing more timely strategies ecosystems.

Language: Английский

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Wetland vegetation mapping improved by phenological leveraging of multitemporal nanosatellite images

Geocarto International, Journal Year: 2025, Volume and Issue: 40(1)

Published: Jan. 21, 2025

Language: Английский

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High-resolution mapping of peatland CO2 fluxes using drone multispectral images

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103060 - 103060

Published: Jan. 1, 2025

Language: Английский

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Predicting Olive Tree Chlorophyll Fluorescence Using Explainable AI with Sentinel-2 Imagery in Mediterranean Environment

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2746 - 2746

Published: March 4, 2025

Chlorophyll fluorescence is a useful indicator of plant’s physiological status, particularly under stress conditions. Remote sensing an increasingly adopted technology in modern agriculture, allowing the acquisition crop information (e.g., chlorophyll fluorescence) without direct contact, reducing fieldwork. The objective this study to improve monitoring olive tree (Fv′/Fm′) via remote Mediterranean environment, where frequency factors, such as drought, increasing. An advanced approach combining explainable artificial intelligence and multispectral Sentinel-2 satellite data was developed predict fluorescence. Field measurements were conducted southeastern Italy on two groves: one irrigated other rainfed reflectance bands vegetation indices used predictors different machine learning algorithms tested compared. Random Forest showed highest predictive accuracy, when predictors. Using spectral preserves more per observation, enabling models detect variations that VIs might miss. Additionally, raw minimizes potential bias could arise from selecting specific indices. SHapley Additive exPlanations (SHAP) analysis performed explain model. using Key regions associated with Fv′/Fm′, red-edge NIR, identified. results highlight integrating grove management, providing tool for early detection targeted interventions.

Language: Английский

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Tropical Forest Carbon Accounting Through Deep Learning-Based Species Mapping and Tree Crown Delineation

Geomatics, Journal Year: 2025, Volume and Issue: 5(1), P. 15 - 15

Published: March 19, 2025

Tropical forests are essential ecosystems recognized for their carbon sequestration and biodiversity benefits. As the world undergoes a simultaneous data revolution climate crisis, accurate on world’s increasingly important. Completely novel in approach, this study proposes methodology encompassing two bespoke deep learning models: (1) single encoder, double decoder (SEDD) model to generate species segmentation map, regularized by distance map training, (2) an XGBoost that estimates diameter at breast height (DBH) based tree crown measurements. These models operate sequentially: RGB images from ReforesTree dataset undergo preprocessing before identification, followed detection using fine-tuned DeepForest model. Post-processing applies custom allometric equations alongside standard accounting formulas final estimates. Unlike previous approaches treat individual identification as isolated task, directly integrates species-level into accounting. Moreover, unlike traditional estimation methods rely regional estimations via satellite imagery, leverages high-resolution, drone-captured offering improved accuracy without sacrificing accessibility resource-constrained regions. The correctly identifies 67% of trees dataset, with rising 84% most common species. In terms accounting, achieves relative error just 2% compared ground-truth potential across test set.

Language: Английский

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Value Realization of Grassland Ecosystem Products in the Karst Desertification Control Area: Spatial Variability, Drivers, and Decision‐Making

Ecology and Evolution, Journal Year: 2025, Volume and Issue: 15(4)

Published: March 27, 2025

ABSTRACT Transforming the ecological advantages of grassland ecosystems into economic benefits while ensuring their long‐term health is an urgent but challenging question, particularly in karst areas characterized by significant spatial heterogeneity. This study selected three representative desertification control (KDC) within South China Karst (SCK) as research focus. Utilizing quantified values ecosystem products and realization rates, we applied a random forest model to analyze influencing factors. We found that: (1) The gross (GEP) per unit area increase with severity desertification. Conversely, value rate decreases grade increases, contradicting theoretical assumption that higher GEP correlates high rate. (2) Water, soil, climate, bare rock coupled human activities (e.g., engineering) affect structure GEP, which, turn, affects KDC area. Based on our findings, suggest leapfrogging can be achieved through artificial engineering ecologically disadvantaged areas, conventional belief more fragile environment results poorer advantages. However, it important note plant species diversity severe low, trade‐off equity between ecology economy must carefully considered future planning. Our findings serve reference for subsequent phases restoration sustainability regions.

Language: Английский

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