Forestry Applications of Space-borne LiDAR Sensors: A Worldwide Bibliometric Analysis

Опубликована: Янв. 8, 2024

The 21st century has seen the launch of new space-borne sensors based on LiDAR (light detection and ranging) technology developed in second half 20th century. was initially to integrate laser-focused imaging with capability determine distances through measurement signal return times, utilizing suitable data acquisition electronics. Nowadays, these have transformed into robust instruments, offering novel opportunities for mapping terrain, canopy heights, estimating above-ground biomass (AGB) across local regional scales. This work aims analyze scientific impact large-scale for-est retrieve 3D information, monitor forest degradation, estimate AGB, model key ecosystem variables such as primary productivity biodiversity. In this way, a worldwide bibliometric analysis topic carried out up 412 publications in-dexed Scopus database during period 2004-2022. results showed that number published documents increased exponentially last five years, coinciding commis-sioning two space missions: Ice, Cloud Land Elevation Satellite (ICESat-2) Global Ecosystem Dynamics Investigation (GEDI). These missions are providing since 2018 2019, respectively. journal demonstrated highest field "Remote Sensing," among leading contributors, top countries terms publica-tions were USA, China, UK, France, Germany. realm prominent research in-stitutions, France boasted six, USA had four, China three, while UK Canada each one. upward trajectory recorded from 2004 2022 catego-rizes subject under investigation highly trending topic, particularly within context enhancing administration resources engaging global climate treaty frameworks mandating surveillance reporting carbon stocks forests. recent August Terrestrial Carbon Monitoring (TECMS; State Administration Forestry Grassland), along planned coming years three sensors, Multi-footprint Observation Im-ager (Japan Aerospace Exploration Agency), BIOMASS P-band Synthetic Aperture Radar (SAR) (European Space Surface Topography (LIST; NASA), will greatly contribute expanding ability map systems at very large context, integration data, including imagery, SAR, LiDAR, is anticipated steer upcoming years.

Язык: Английский

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Mapping Windthrow Risk in Pinus radiata Plantations Using Multi-Temporal LiDAR and Machine Learning: A Case Study of Cyclone Gabrielle, New Zealand

Remote Sensing, Год журнала: 2025, Номер 17(10), С. 1777 - 1777

Опубликована: Май 20, 2025

As the frequency of strong storms and cyclones increases, understanding wind risk in both existing newly established plantation forests is becoming increasingly important. Recent advances quality availability remotely sensed data have significantly improved our capability to make large-scale predictions. This study models loss radiata pine (Pinus D.Don) plantations following a severe cyclone within Gisborne Region New Zealand through leveraging repeat regional LiDAR acquisitions, optical imagery, various surfaces describing key climatic, topographic, storm-specific conditions. A random forest model was trained on 9713 plots classified as windthrow or no-windthrow. Model validation using 50 iterations 80/20 train/test splits achieved robust accuracy (accuracy = 0.835; F1 score 0.841; AUC 0.913). In comparison most European empirical (AUC 0.51–0.90), framework demonstrated superior discrimination, underscoring its value for regions prone cyclones. Among 14 predictor variables, influential were mean windspeed during February, exposition index, site drainage, stand age. predictions closely aligned with estimated 3705 hectares cyclone-induced damage indicated that 20.9% unplanted areas region would be at age 30 if pine. The resulting surface serves valuable decision-support tool managers, helping mitigate guide adaptive afforestation strategies. Although developed Zealand, approach findings broader relevance management cyclone-prone worldwide, particularly where forestry widely practised.

Язык: Английский

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Remote Sensing of Forests in Bavaria: A Review

Remote Sensing, Год журнала: 2024, Номер 16(10), С. 1805 - 1805

Опубликована: Май 20, 2024

In recent decades, climatic pressures have altered the forested landscape of Bavaria. Widespread loss trees has unevenly impacted entire state, which 37% is covered by forests (5% more than national average). 2018 and 2019—due in large part to drought subsequent insect infestations—more tree-covered areas were lost Bavaria any other German state. Moreover, annual crown condition survey revealed a decreasing trend tree vitality since 1998. We conducted systematic literature review regarding remote sensing total, 146 scientific articles published between 2008 2023. While 88 studies took place Bavarian Forest National Park, only five publications whole Outside park, remaining 2.5 million hectares forest are understudied. The most commonly studied topics related bark beetle infestations (24 papers); however, few papers focused on drivers infestations. majority utilized airborne data, while utilizing spaceborne data multispectral; types under-utilized- particularly thermal, lidar, hyperspectral. recommend future both spatially broaden investigations state or scale increase temporal acquisitions together with contemporaneous situ data. Especially understudied response climate, catastrophic disturbances, regrowth species composition, phenological timing, sector management. utilization forestry uptake results among stakeholders remains challenge compared heavily European countries. An integral economy tourism sector, also vital for climate regulation via atmospheric carbon reduction land surface cooling. Therefore, monitoring centrally important attaining resilient productive forests.

Язык: Английский

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Vegetation canopy height shapes bats’ occupancy: a remote sensing approach

GIScience & Remote Sensing, Год журнала: 2024, Номер 61(1)

Опубликована: Июль 17, 2024

Anthropogenic activities have significantly altered land cover on a global scale. These changes often negative effect biodiversity limiting the distribution of species. The extent species' depends landscape composition and configuration at local level. To better understand this large scale, we evaluated how vegetation structure shape bat occurrence while considering imperfect detection. We hypothesize that intensification anthropogenic in agriculture, for example, reduces heterogeneity structure, thereby, limits occurrence. investigate this, conducted acoustic sampling across 59 locations southern Portugal, each with three spatial replicates. derived fine-scale structural metrics by combining spaceborne LiDAR (GEDI) synthetic aperture radar data (Sentinel-1 ALOS/PALSAR-2). Additionally, included high-resolution climate from CHELSA. Our findings revealed an important relationship between occupancy particularly canopy height. Moreover, forest shrubland proportions were main types influencing species responses. All best-ranking models least one climatic variable (temperature, humidity, or potential evapotranspiration), demonstrating importance when predicting distribution. surveys had detection probability varying 0.19 to 0.86, it was influenced night conditions. underscore modeling detection, especially highly vagile elusive organisms like bats. results demonstrate effectiveness using remote sensing model context monitoring conservation.

Язык: Английский

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Understory Terrain Estimation by Synergizing Ice, Cloud, and Land Elevation Satellite-2 and Multi-Source Remote Sensing Data

Remote Sensing, Год журнала: 2024, Номер 16(24), С. 4770 - 4770

Опубликована: Дек. 21, 2024

Forest ecosystems are incredibly valuable, and understory terrain is crucial for estimating various forest structure parameters. As the demand monitoring increases, quickly accurately understanding spatial distribution patterns of has become a new challenge. This study used ICESat-2 data as reference validation basis, integrating multi-source remote sensing (including Landsat 8, ICESat-2, SRTM) applying machine learning methods to estimate sub-canopy topography area. The results from random model show significant improvement in accuracy compared traditional SRTM products, with an R2 0.99, ME 0.22 m, RMSE 3.59 STD m. In addition, we assessed estimates different landforms, canopy heights, cover types, coverage. demonstrate that estimation minimally impacted by ground elevation, type, coverage, indicating good stability. approach holds promise at regional global scales, providing support protecting ecosystems.

Язык: Английский

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Advancing forest aboveground biomass mapping by integrating GEDI with other Earth Observation data using a cloud computing platform: A case study of Alabama, United States

EarthArXiv (California Digital Library), Год журнала: 2024, Номер unknown

Опубликована: Апрель 6, 2024

Forest aboveground biomass (AGB) is a crucial indicator for monitoring carbon and requires accurate quantification. This study aimed to advance AGB estimation using open access Earth observation (EO) data cloud computing, focusing on Alabama, USA. The specific objectives were to: (1) develop workflow creating 30 m forest AGBD map with GEDI, GEE, (2) evaluate compare GEDI-derived maps from ecoregion-specific models estimates derived generalized modeling approach, (3) existing field inventory global product. Utilizing GEDI footprint-level (~25 diameter) was extrapolated EO ancillary by employing random machine learning. Two approaches assessed: statewide Alabama's six ecoregions. Ecoregion showed superior accuracy (R²: 0.34–0.73; RMSE: 49.09–53.78 Mg/ha) compared the model 0.32; 70.48 Mg/ha). Validation Inventory Analysis European Space Agency Climate Change Initiative yielded R² of 0.50 0.81, RMSE 33.95 Mg/ha 83.12 Mg/ha, respectively. underscores importance demonstrates potential open-access platforms in advancing estimation.

Язык: Английский

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