How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass?

Varvia, Petri; Korhonen, Lauri; Bruguière, A.; Toivonen, Janne; Packalén, Petteri; Maltamo, Matti; Saarela, Svetlana; Popescu, Sorin C.

doi:10.1016/j.rse.2022.113174

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…The study's findings have drawn attention to the intriguing variation in FCC estimations between day and night segment acquisitions. The higher accuracy rates associated with night acquisitions resonate with the results from biomass estimation studies conducted by Narine et al (2019b) and Varvia et al (2022). In another study, Neuenschwander et al (2020) validated canopy and terrain heights obtained from ICESat-2 in Finland using airborne lidar data.…”

Section: Discussionsupporting

confidence: 55%

“…These noteworthy advantages could potentially escalate the prediction precision of specific forest stand parameters. As an illustration, several academic studies employing ICESat-2 data have discerned that data amassed during night hours can augment the predictive success in assessing aboveground biomass (Narine et al, 2019b;Varvia et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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ICESat-2 ATL08 Verileri Kullanılarak Veri Toplama Zamanının Orman Kanopi Örtüsü Tahmini Üzerindeki Etkisinin Değerlendirilmesi

AKTÜRK

2023

Kastamonu Üniversitesi Orman Fakültesi Dergisi

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Aim of study: This study investigates the estimation success of using day and night segments in producing Forest Canopy Cover (FCC) maps with the Canopy Cover Estimation Model (CCEM) for the years 2020 and 2022. Area of study: The study area covers 17 interconnected counties situated in the southeastern part of Texas state, adjacent to the state of Louisiana, and near the southern coastlines, known for their extensive forested areas. Material and methods: This study incorporated both day and night acquisition segments from Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data for a comprehensive comparison of their effectiveness in mapping the forest canopy cover using the CCEM. Main results: The study’s findings reveal that night segment-derived FCC maps outperform those derived from day segments, showing higher kappa coefficients of 0.77 and 0.83 for the years 2020 and 2022, respectively. In addition, notable differences were observed among classes of FCC estimations successes for day and night segment-derived maps. Research highlights: This study introduces a significant finding that the FCC maps derived from night segments yield more accurate results than those derived from day segments. The study further discovers a notable difference in the forest canopy cover classification success, particularly with a lower accuracy observed in the Moderate Forest Canopy Cover (MFCC) category.

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Section: Discussionsupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

ICESat-2 ATL08 Verileri Kullanılarak Veri Toplama Zamanının Orman Kanopi Örtüsü Tahmini Üzerindeki Etkisinin Değerlendirilmesi

AKTÜRK

2023

Kastamonu Üniversitesi Orman Fakültesi Dergisi

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“…In recent years, ICESat-2/ATLAS, a satellite-borne lidar system of the latest iteration, has played an increasingly important role in a number of disciplines, including measurements of ice-cover thickness and surface elevation [26,27], the dynamic monitoring of lake water levels [28], and the imaging of forest physical parameters. In the estimation and mapping of forest physical parameters, ICESat-2 data are frequently used in synergy with satellite optical imagery for forest aboveground biomass inversion [29][30][31] and tree height mapping at the regional scale [32][33][34], and have become the crucial data source for large-scale forest aboveground biomass and tree height mapping. However, no studies on forest biodiversity monitoring using the novel satellite-based LIDAR ICESat-2/ATLAS in conjunction with satellite optical remote sensing have been reported to date, and there has been little exploration of the potential of combining horizontal spectral variation and vertical structural variation features of satellite remote sensing data for application in forest tree species diversity modeling [4].…”

Section: Introductionmentioning

confidence: 99%

Forest Tree Species Diversity Mapping Using ICESat-2/ATLAS with GF-1/PMS Imagery

Yang

Shu

Zhang

et al. 2023

Forests

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Forest ecosystems depend on species of tree variety. Remote sensing for obtaining large-scale spatial distribution information of tree species diversity is a geoscience research hotspot to overcome the limitations of conventional tree species diversity survey approaches. Airborne LiDAR or synergy with airborne optical imagery has been used to model and estimate tree species diversity for specific forest communities, with many revealing results. However, the data collection for such research is costly, the breadth of monitoring findings is limited, and obtaining information on the geographical pattern is challenging. To this end, we propose a method for mapping forest tree species diversity by synergy satellite optical remote sensing and satellite-based LiDAR based on the spectral heterogeneity hypothesis and structural variation hypothesis to improve the accuracy of the remote sensing monitoring of forest tree species diversity while considering data cost. The method integrates horizontal spectral variation from GF-1/PMS image data with vertical structural variation from ICESat-2 spot data to estimate the species diversity of trees. The findings reveal that synergistic horizontal spectral variation and vertical structural variation overall increase tree species diversity prediction accuracy compared to a single remote sensing variation model. The synergistic approach improved Shannon and Simpson indices prediction accuracy by 0.06 and 0.04, respectively, compared to the single horizontal spectral variation model. The synergistic model, single vertical structural variation model, and single horizontal spectral variation model were the best prediction models for Shannon, Simpson, and richness indices, with R2 of 0.58, 0.62, and 0.64, respectively. This research indicates the potential of synergistic satellite-based LiDAR and optical remote sensing in large-scale forest tree species diversity mapping.

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“…However, laser measurements are expensive, and thus in many cases they cannot be conducted wall-to-wall but only through sample coverage [8] . Important examples include the strip samples of laser measurements conducted in Alaska [9] , and the samples of laser footprints from NASA's Global Ecosystem Dynamics Investigation (GEDI; [1 , 10] and the Ice, Cloud, and land Elevation Satellites 1 and 2 (ICESat-1 and −2; [11] , [12] , [13] , [14] , [15] , [16] , [17] . For many of these applications, existing methods for assessing uncertainties using frameworks such as model-assisted estimation (e.g., [9] ), hybrid inference (e.g., [1 , 11] ), and hierarchical model-based inference (e.g., [10] ) have been sufficient.…”

Section: Introductionmentioning

confidence: 99%

Three-phase hierarchical model-based and hybrid inference

Saarela¹,

Varvia²,

Korhonen³

et al. 2023

MethodsX

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How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass?

Cited by 8 publications

References 27 publications

ICESat-2 ATL08 Verileri Kullanılarak Veri Toplama Zamanının Orman Kanopi Örtüsü Tahmini Üzerindeki Etkisinin Değerlendirilmesi

ICESat-2 ATL08 Verileri Kullanılarak Veri Toplama Zamanının Orman Kanopi Örtüsü Tahmini Üzerindeki Etkisinin Değerlendirilmesi

Forest Tree Species Diversity Mapping Using ICESat-2/ATLAS with GF-1/PMS Imagery

Three-phase hierarchical model-based and hybrid inference

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