Hybrid model for estimating forest canopy heights using fused multimodal spaceborne LiDAR data and optical imagery

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 122, P. 103431 - 103431

Published: July 28, 2023

The forest canopy height is a key indicator for measuring global carbon stocks. Spaceborne LiDAR, satellite remote sensing technology, plays an essential role in large-scale estimations. However, there are still some problems with existing methods of the spaceborne LiDAR estimates: retrieval accuracy degraded by topographic relief and vegetation cover, as well uneven spatial distribution mapping uncertainties. In this paper, we investigated possibility fusing multimodal optical images to improve these above problems. We proposed hybrid model full-waveform photon-counting data imagery. Specifically, our approach divided regional extent into multiple fusion patterns based on footprints object-oriented method. then constructed models corresponding each pattern finally integrated results using weighting scheme considering geospatial distances. used GEDI (full-waveform LiDAR), ICESat-2 (photon-counting LiDAR) Sentinel-2 (optical imagery) products input validated four representative biomes ecosystems (i.e., evergreen broadleaf forests, deciduous savannas coniferous forests). experimental demonstrated that multisource can not only enhance estimation (R2 0.65 ∼ 0.90 RMSE 0.57 4.15 m biomes) but also maintain stable under undulating slope large cover. Moreover, uncertainty was low (meanerror −0.20 0.03 m) uniformly distributed space (stdev 0.71 4.45 m). compared performances two other advanced models, products, showed significant advantages test region. Our study demonstrates effectiveness imagery improvement.

Language: Английский

---

Characterising the distribution of mangroves along the southern coast of Vietnam using multi-spectral indices and a deep learning model

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 923, P. 171367 - 171367

Published: March 1, 2024

Mangroves are an ecologically and economically valuable ecosystem that provides a range of ecological services, including habitat for diverse plant animal species, protection coastlines from erosion storms, carbon sequestration, improvement water quality. Despite their significant role, in many areas, Vietnam, large scale losses have occurred, although restoration efforts been underway. Understanding the loss efficacy requires high resolution temporal monitoring mangrove cover on scales. We produced time series 10-m-resolution maps using Multispectral Instrument Sentinel 2 satellites with this tool measured changes distribution Vietnamese Southern Coast (VSC). extracted annual ranging 2016 to 2023 deep learning model U-Net architecture based 17 spectral indices. Additionally, comparison misclassification by global products was conducted, indicating demonstrated superior performance when compared experiments multispectral bands Sentinel-2 time-series Sentinel-1 data, as shown highest performing The generated metrics, overall accuracy, precision, recall, F1-score were above 90 % entire years. Water indices investigated most important variables extraction. Our study revealed some misclassifications such World Cover Global Mangrove Watch highlighted significance our local analysis. While we did observe 34,778 ha (42.2 %) area region, 47,688 (57.8 new appeared, resulting net gain 12,910 (15.65 over eight-year period study. majority areas concentrated Ca Mau peninsulas within estuaries undergoing recovery programs natural processes. occurred regions where industrial development, wind farm projects, reclaimed land, shrimp pond expansion is occurring. theoretical framework well up-to-date data mapping change can be readily applied at other sites.

Language: Английский

---

3U CubeSat-Based Hyperspectral Remote Sensing by Offner Imaging Hyperspectrometer with Radially-Fastened Primary Elements

Sensors, Journal Year: 2024, Volume and Issue: 24(9), P. 2885 - 2885

Published: April 30, 2024

This paper presents findings from a spaceborne Earth observation experiment utilizing novel, ultra-compact hyperspectral imaging camera aboard 3U CubeSat. Leveraging the Offner optical scheme, camera’s hyperspectrometer captures images of terrestrial regions with 200 m spatial resolution and 12 nanometer spectral across 400 to 1000 wavelength range, covering 150 channels in visible near-infrared spectrums. The is specifically designed for deployment on CubeSat nanosatellite platform, featuring robust all-metal cylindrical body hyperspectrometer, coaxial arrangement elements ensures optimal compactness vibration stability. performance was rigorously evaluated through numerical simulations prior construction. Analysis data acquired over year-long orbital operation demonstrates CubeSat’s ability produce various vegetation indices, including normalized difference index (NDVI). A comparative study European Space Agency’s Sentinel-2 L2A shows strong agreement at critical points, confirming suitability Notably, ISOI can generate unique beyond reach L2A, underscoring its potential advancing remote sensing applications.

Language: Английский

---

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(3), P. 446 - 446

Published: Jan. 23, 2024

Since 1971, remote sensing techniques have been used to map and monitor phenomena parameters of the coastal zone. However, updated reviews only considered one phenomenon, parameter, data source, platform, or geographic region. No review has offered an overview that can be accurately mapped monitored with data. This systematic was performed achieve this purpose. A total 15,141 papers published from January 2021 June 2023 were identified. The 1475 most cited screened, 502 eligible included. Web Science Scopus databases searched using all possible combinations between two groups keywords: geographical names in areas platforms. demonstrated that, date, many (103) (39) (e.g., coastline land use cover changes, climate change, urban sprawl). Moreover, authors validated 91% retrieved parameters, 39 1158 times (88% combined together other parameters), 75% over time, 69% several compared results each available products. They obtained 48% different methods, their 17% GIS model techniques. In conclusion, addressed requirements needed more effectively analyze employing integrated approaches: they data, merged

Language: Английский

---

Precision in mapping and assessing mangrove Biomass: Insights from the Persian Gulf coasts

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 128, P. 103769 - 103769

Published: March 16, 2024

This groundbreaking research makes a contribution to climate change adaptation studies by filling crucial knowledge gap related the precise evaluation of mangrove biomass—an essential element influencing future trends coastal ecosystems. Specifically, study concentrates on Hara Biosphere Reserve (HBR) coasts Persian Gulf (PG), aiming generate maps biomass. The methodological approach involves comprehensive analysis, including utilization Landsat imagery establish an NDVI map mangroves, application Cumulative Sum (CUSUM) method determine threshold value for distinguishing between tall and dwarf subsequent mapping their distribution in both island zones. Additionally, calculates above-ground biomass (AGB) below-ground (BGB) values sample plots, develops regression relationship values, integrates extent with spatial variations AGB BGB. Noteworthy outcomes include identification (0.63) types revealing distinct mangroves Significantly, positioned seaward edges exhibit higher zones than mangroves. These findings shed light potential exacerbation impacts, such as rising sea levels changing tidal range PG due heightened productivity specific Recognizing these structural characteristics production disparities is developing effective programs. Integrating insights into management strategies emphasized pivotal enhancing efficiency success programs, presenting robust solution protecting diverse areas.

Language: Английский

---

Mangrove mapping and monitoring using remote sensing techniques towards climate change resilience

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 23, 2024

Abstract Mangroves are amongst the richest ecosystems in world providing valuable goods and services to millions of people while enhancing resilience coastal communities against climate change induced hazards, especially island nations. However, these mangroves severely affected by many anthropogenic activities. Therefore, understanding spatial variability nations is highly essential events ongoing climatic change. Thus, this study assessed use remote sensing techniques GIS map monitor mangrove cover at selected sites, namely Le Morne Ferney, on tropical Mauritius. Freely available 2013 SPOT-5 2023 Sentinel 2A images were retrieved processed using ArcGIS Pro tools SNAP; mapped based Google Earth historical imagery ground truthing respective sites. Following application vegetation indices, GLCM PCA analysis, mosaicked classified Random Trees algorithm. Kappa values all 90 s; showed a significant increase over decadal scale with main class from mudflat mangroves. This demonstrates how geo-spatial crucial for monitoring as they provide spatially explicit time sensitive information. Decision makers, researchers, relevant stakeholders can utilize data bolster tailored mitigation adaptation strategies specific thereby

Language: Английский

---

Uncovering mangrove range limits using very high resolution satellite imagery to detect fine‐scale mangrove and saltmarsh habitats in dynamic coastal ecotones

Remote Sensing in Ecology and Conservation, Journal Year: 2024, Volume and Issue: unknown

Published: May 23, 2024

Abstract Mangroves are important ecosystems for coastal biodiversity, resilience and carbon dynamics that being threatened globally by human pressures the impacts of climate change. Yet, at several geographic range limits in tropical–temperate transition zones, mangrove expanding poleward response to changing macroclimatic drivers. near often grow smaller statures form dynamic, patchy distributions with other habitats, which difficult map using moderate‐resolution (30‐m) satellite imagery. As a result, many these areas missing global distribution maps. To better small, scrub mangroves, we tested Landsat Sentinel (10‐m) against very high resolution (VHR) Planet (3‐m) WorldView (1.8‐m) imagery assessed accuracy machine learning classification approaches discerning current (2022) saltmarsh from habitats rapidly ecotone along east coast Florida, USA. Our aim is (1) quantify mappable differences landscape composition complexity, class dominance spatial properties patches due image resolution; (2) resolve mapping uncertainties region. We found ability leading edge was hampered size extent stands too small detection (50% accuracy). most successful mangroves wetland (84% accuracy), closely followed (82%) (81%). With WorldView, detected 800 ha within Florida range‐limit study area, 35% more than were Planet, 114% 537% Landsat. Higher‐resolution helped reveal additional variability metrics quantifying diversity, configuration connectedness among landscape, patch scales. Overall, VHR improved our can help supplement outdated regional

Language: Английский

---

Earth Observation Data for Mangrove Monitoring and Management at the Red Sea Coastline, Egypt

Springer remote sensing/photogrammetry, Journal Year: 2025, Volume and Issue: unknown, P. 145 - 175

Published: Jan. 1, 2025

Language: Английский

---

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 14

Published: June 2, 2023

Forests are suffering water stress due to climate change; in some parts of the globe, forests being exposed highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used provide remote monitoring health forest, including moisture content, chlorophyll, nitrogen estimation, forest canopy, degradation, among others. However, intelligence evolve fast associated computational resources; data acquisition, processing change accordingly. This article is aimed at gathering latest developments forests, special emphasis on most important vegetation parameters (structural morphological), using machine techniques. The analysis presented here gathered 108 articles from last 5 years, we conclude by showing newest AI tools that might be near future.

Language: Английский

---

Mapping Mangrove Above-Ground Carbon Using Multi-Source Remote Sensing Data and Machine Learning Approach in Loh Buaya, Komodo National Park, Indonesia

Forests, Journal Year: 2023, Volume and Issue: 14(1), P. 94 - 94

Published: Jan. 4, 2023

Mangrove forests provide numerous valuable ecosystem services and can sequester a large volume of carbon that help mitigate climate change impacts. Modeling mangrove with robust valid approaches is crucial to better understanding existing conditions. The study aims estimate Above-Ground Carbon (AGC) at Loh Buaya located in the Komodo National Park (Indonesia) using novel Extreme Gradient Boosting (XGB) Genetic Algorithm (GA) analyses integrating multiple sources remote sensing (optical, Synthetic Aperture Radar (SAR), Digital Elevation Model (DEM)) data. Several steps were conducted assess model’s accuracy, starting field survey 50 sampling plots, processing images, selecting variables, examining appropriate machine learning (ML) models. effectiveness proposed XGB-GA was assessed via comparison other well-known ML techniques, i.e., Random Forest (RF) Support Vector Machine (SVM) Our results show hybrid model yielded best (R2 = 0.857 training R2 0.758 testing phase). optimized by GA consisted six spectral bands five vegetation indices generated from Sentinel 2B together national DEM had an RMSE 15.40 Mg C ha−1 outperformed models for quantifying AGC. estimated AGC ranging 2.52 123.89 (with average 57.16 ha−1). findings contribute innovative method, which fast reliable open-source data software. Multisource remotely sensed combined advanced techniques potentially be used tropical ecosystems worldwide.

Language: Английский

---