Progress and Limitations in Forest Carbon Stock Estimation Using Remote Sensing Technologies: A Comprehensive Review

Forests, Год журнала: 2025, Номер 16(3), С. 449 - 449

Опубликована: Март 2, 2025

Forests play a key role in carbon sequestration and oxygen production. They significantly contribute to peaking neutrality goals. Accurate estimation of forest stocks is essential for precise understanding the capacity ecosystems. Remote sensing technology, with its wide observational coverage, strong timeliness, low cost, stock research. However, challenges data acquisition processing include variability, signal saturation dense forests, environmental limitations. These factors hinder accurate estimation. This review summarizes current state research on from two aspects, namely remote methods, highlighting both advantages limitations various sources models. It also explores technological innovations cutting-edge field, focusing deep learning techniques, optical vegetation thickness impact forest–climate interactions Finally, discusses including issues related quality, model adaptability, stand complexity, uncertainties process. Based these challenges, paper looks ahead future trends, proposing potential breakthroughs pathways. The aim this study provide theoretical support methodological guidance researchers fields.

Язык: Английский

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Seasonal Land Use and Land Cover Mapping in South American Agricultural Watersheds Using Multisource Remote Sensing: The Case of Cuenca Laguna Merín, Uruguay

Sensors, Год журнала: 2025, Номер 25(1), С. 228 - 228

Опубликована: Янв. 3, 2025

Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers extract insights from Multisource Remote Sensing. This study aims use these technologies for mapping summer winter Land Use/Land Cover features Cuenca de la Laguna Merín, Uruguay, while comparing performance Random Forests, Support Vector Machines, Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 Shuttle Radar Topography Mission imagery, Google Engine, training validation datasets quoted methods involve creating a multisource database, conducting feature importance analysis, developing models, supervised classification performing accuracy assessments. Results indicate low significance microwave inputs relative optical features. Short-wave infrared bands transformations such as Normalised Vegetation Index, Surface Water Index Enhanced demonstrate highest importance. Accuracy assessments that various classes is optimal, particularly rice paddies, which play vital role country’s economy highlight significant environmental concerns. However, challenges persist reducing confusion between classes, regarding natural vegetation versus seasonally flooded vegetation, well post-agricultural fields/bare land herbaceous areas. Forests Trees exhibited superior compared Machines. Future research should explore approaches Deep Learning pixel-based object-based integration address identified challenges. These initiatives consider data combinations, including additional indices texture metrics derived Grey-Level Co-Occurrence Matrix.

Язык: Английский

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Assessing Vegetation Canopy Growth Variations in Northeast China

Plants, Год журнала: 2025, Номер 14(1), С. 143 - 143

Опубликована: Янв. 6, 2025

Studying climate change’s impact on vegetation canopy growth and senescence is significant for understanding predicting dynamics. However, there a lack of adequate research changes across the lifecycles different types. Using GLASS LAI (leaf area index) data (2001–2020), we investigated development (April–June), maturity (July–August), (September–October) rates in Northeast China, focusing their responses to preseason climatic factors. We identified that early stages saw acceleration, with over 71% areas experiencing such acceleration April May. As grew, accelerating slowed down, reached its maturation earlier. By analyzing partial correlation between factors, it was were most significantly affected by air temperature. A positive observed stages, which shifted negative during senescence. Notably, transition timing varied among types, grasslands (June) occurring earlier than forests (July) farmlands (August). Additionally, grassland showed stronger response precipitation farmlands, lagged effect 2.50 months. Our findings improve holding importance ecological environmental monitoring, land-use planning, sustainable development.

Язык: Английский

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Multi-Scale Analysis of Urban Greenspace Exposure and Equality: Insights from a Population-Enhanced Vegetation Index (EVI)-Weighted Model in the West Side Straits Urban Agglomeration

Land, Год журнала: 2025, Номер 14(1), С. 132 - 132

Опубликована: Янв. 10, 2025

Urban greenspaces (UGSs) are pivotal for ecological enhancement and the well-being of urban residents. The accurate quantification greenspace exposure (GE) its distributional equality is essential equitable planning mitigating inequalities in access. This study introduces a novel population-EVI-weighted model that integrates Enhanced Vegetation Index (EVI), land cover, demographic data to evaluate GE across various spatial scales buffer distances (300 m, 500 1 km). provides more nuanced representation realistic UGSs utilization by residents than traditional metrics coverage or simple population-weighted exposure. Our comprehensive analysis reveals refining scale improves understanding GE’s variation equality. Furthermore, increasing distance substantially enhances 20 cities over 93% counties within Agglomeration on West Side Straits (WSS). Notably, county level shows superior performance greater sensitivity adjustments compared city WSS. These findings underscore importance achieve equal access greenspaces.

Язык: Английский

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Comparative analysis of global urban land surface phenology between the MODIS and VIIRS products and extraction methods

Journal of Environmental Management, Год журнала: 2025, Номер 375, С. 124326 - 124326

Опубликована: Янв. 29, 2025

Язык: Английский

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Detecting the Phenological Threshold to Assess the Grassland Restoration in the Nanling Mountain Area of China

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 451 - 451

Опубликована: Янв. 28, 2025

The dynamics of vegetation changes and phenology serve as key indicators interannual in productivity. Monitoring the Nanling grassland ecosystem using remote sensing index is crucial for rational development, utilization, protection these resources. Grasslands hilly areas southern China’s middle low mountains have a high restoration efficiency due to favorable combination water temperature conditions. However, dynamic adaptation process under combined effects climate change human activities remains unclear. aim this study was conduct continuous phenological monitoring ecosystem, evaluate its seasonal characteristics, trends, thresholds changes. Normalized Difference Phenology Index (NDPI) values Mountains’ grasslands from 2000 2021 calculated MOD09A1 images Google Earth Engine (GEE) platform. Savitzky–Golay filter Mann–Kendall test were applied time series smoothing trend analysis, growing seasons extracted annually Seasonal Trend Decomposition LOESS. A segmented regression method then employed detect based on cover percentage. results showed that (1) NDPI increased significantly (p < 0.01) across all patches, particularly southeast, with notable rise 2010 2014, following an eastern western central mutation sequence. (2) annual lower upper 0.005~0.167 0.572~0.727, which mainly occurred January–March June–September, respectively. (3) Most same periods increasing season length varying 188 247 days. (4) overall potential productivity improved. (5) mountain associated coverage mean values, threshold identified at value 0.5 2.1% coverage. This indicates ensure sustainable development conservation ecosystems, targeted management strategies should be implemented, regions where factors influence fluctuations.

Язык: Английский

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Monitoring Mangrove Phenology Based on Gap Filling and Spatiotemporal Fusion: An Optimized Mangrove Phenology Extraction Approach (OMPEA)

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 549 - 549

Опубликована: Фев. 6, 2025

Monitoring mangrove phenology requires frequent, high-resolution remote sensing data, yet satellite imagery often suffers from coarse resolution and cloud interference. Traditional methods, such as denoising spatiotemporal fusion, faced limitations: algorithms usually enhance temporal without improving spatial quality, while fusion models struggle with prolonged data gaps heavy noise. This study proposes an optimized extraction approach (OMPEA), which integrates Landsat MODIS a algorithm (e.g., Gap Filling Savitzky–Golay filtering, GF–SG) model Enhanced Spatial Temporal Adaptive Reflectance Fusion Model, ESTARFM). The key of OMPEA is that GF–SG filled cover transit gaps, providing high-quality input to ESTARFM its accuracy NDVI reconstruction in extraction. By conducting experiments on the GEE platform, generates 1-day, 30 m imagery, phenological parameters (i.e., start (SoS), end (EoS), length (LoS), peak (PoS) growing season) are derived using maximum separation (MS) method. Validation four areas along coastal China shows significantly improves potential capture presence incomplete data. increased usable adding 7–33 images 318–415 per region. generated series exhibits strong consistency original (R2: 0.788–0.998, RMSE: 0.007–0.253) revealed earlier SoS longer LoS at lower latitudes. Cross-correlation analysis showed 2–3 month lagged effects temperature mangroves’ growth, precipitation having minimal impact. proposed possibility capturing under non-continuous low-resolution valuable insights for large-scale long-term conservation management.

Язык: Английский

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Temporal Relationships of Breeding Landbirds and Productivity on a Working Landscape

Wild, Год журнала: 2025, Номер 2(1), С. 4 - 4

Опубликована: Фев. 17, 2025

The Normalized Difference Vegetation Index (NDVI) is a measurement of landscape “greenness” and used as proxy for productivity to assess species distributions habitats. Seasonal levels have been strongly related avian population dynamics, suggesting dependence upon biomass production completing annual life cycle events. breeding season critical component the that involves higher nutritional requirements feed young, avoiding predators, attracting mates. Our objective was determine how NDVI affects abundance richness across seasons with varied rainfall in South Texas, USA. Breeding bird point-count surveys were conducted, MODIS Terra data collected. We observed both positive negative effects between May June abundance, richness, depending year (i.e., wet or average rainfall) values months prior April) during peak (May), no significant effect June, may be most influential. This information can aid land management recommendations better predict environmental changes like affect dynamics on wildlife domestic animals.

Язык: Английский

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Enhancing Pear Tree Yield Estimation Accuracy by Assimilating LAI and SM into the WOFOST Model Based on Satellite Remote Sensing Data

Agriculture, Год журнала: 2025, Номер 15(5), С. 464 - 464

Опубликована: Фев. 21, 2025

In modern agriculture, timely and accurate crop yield information is crucial for optimising agricultural production management resource allocation. This study focused on improving the prediction accuracy of pear yields. Taking Alar City, Xinjiang, China as research area, a variety data including leaf area index (LAI), soil moisture (SM) remote sensing were collected, covering four key periods growth. Three advanced algorithms, Partial Least Squares Regression (PLSR), Support Vector (SVR) Random Forest (RF), used to construct regression models LAI vegetation in using Sentinel-2 satellite data. The results showed that RF algorithm provided best when inverting LAI. coefficients determination (R2) 0.73, 0.72, 0.76, 0.77 periods, respectively, root-mean-square errors (RMSE) 0.21 m2/m2, 0.24 0.18 0.16 respectively. Therefore, was selected preferred method inversion this study. Subsequently, further explored potential assimilation techniques enhancing simulation. SM incorporated into World Food Studies (WOFOST) growth model by namely, Four-Dimensional Variational Approach (4D-Var), Particle Swarm Optimisation (PSO) algorithm, Ensemble Kalman Filter (EnKF), (PF) separate joint assimilation, experimental assimilated significantly improved compared unassimilated model. particular, EnKF highest estimation with R2 0.82, 0.79 RMSE 1056 kg/ha 1385 alone assimilated, whereas 4D-Var performed jointly high 0.88, reduced 923 kg/ha. addition, it found assimilating outperformed one variable, enhanced predictive performance beyond variable alone. summary, present demonstrated great provide strong support effectively integrating through assimilation.

Язык: Английский

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Using Vegetation Indices Developed for Sentinel-2 Multispectral Data to Track Spatiotemporal Changes in the Leaf Area Index of Temperate Deciduous Forests

Geomatics, Год журнала: 2025, Номер 5(1), С. 11 - 11

Опубликована: Фев. 28, 2025

The leaf area index (LAI) in temperate forests is highly dynamic throughout the season, and lacking such information has limited our understanding of carbon water flux patterns these ecosystems. This study aims to explore potential using vegetation indices based on Sentinel-2 data, which includes three additional spectral bands red-edge region its multispectral imager (MSI) sensor compared previous satellite-borne imagery, effectively track seasonal variations LAI within typical cold–temperate deciduous originating rugged terrain Japan. We evaluated reported developed an specific data monitor spatiotemporal changes mountainous forests, providing more accurate for ecological monitoring. Results showed that (SRB12,B7) was able at both spatial scales (R2 = 0.576). Further analyses revealed nevertheless performed relatively poorly during leaf-maturing season when peaks, suggesting it still suffers from a “saturation” problem. For high-resolution tracking temporal scales, future research needed incorporate information.

Язык: Английский

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