ICESat-2 for Canopy Cover Estimation at Large-Scale on a Cloud-Based Platform

Akturk, Emre; Popescu, Sorin C.; Malambo, Lonesome

doi:10.3390/s23073394

Cited by 6 publications

(11 citation statements)

References 78 publications

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“…Generating at least two FCC maps corresponding to different dates is necessary to monitor temporal FCC changes. In this study, the CCEM algorithm developed by Akturk et al (2023) was employed, with some modifications, to generate FCC maps for different dates for Antalya province. The study began with the initial phase of the case study, which involved collecting and filtering appropriate segments.…”

Section: Data Acquisition Filtering and Ccemmentioning

confidence: 99%

“…The study highlights the potential of using ICESat-2 data to improve the estimation of FCC and underscores the importance of selecting the appropriate variables to maximize estimation accuracy. A recent study conducted by Akturk et al (2023) aimed to assess the efficacy of cloud-based platforms for largescale categorized FCC mapping using ICESat-2's ATL08 variables. Google Earth Engine (GEE)-based Canopy Cover Estimation Model (CCEM) was created to generate an FCC map for Türkiye with an accuracy of 71.9% for the year 2021 in the study.…”

Section: Introductionmentioning

confidence: 99%

“…This study aims to generate FCC maps for the Antalya province of Türkiye for 2019 and 2022 using ICESat-2 ATL08 data and Landsat satellite imagery to address the gap in the literature. The CCEM (Akturk et al 2023), which was previously introduced in the literature, was utilized with some modifications within the GEE to generate the FCC maps. Specifically, this study aims to achieve the following objectives:…”

Section: Introductionmentioning

confidence: 99%

“…• Evaluate the impact of using the ESRI 10m Land Use/Land Cover dataset for the years 2017-2022 (Karra et al 2021) instead of the 2019 100m Copernicus Land Cover dataset (Buchhorn et al 2020), which has been identified as a significant source of error in the CCEM (Akturk et al 2023), on the accuracy of the FCC maps generated using the modified CCEM within the GEE platform;…”

Section: Introductionmentioning

confidence: 99%

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Monitoring forest canopy cover change with ICESat-2 Data in fire-prone areas: A case study in Antalya, Türkiye

Akturk

2023

AFR

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This study presents an innovative approach for monitoring forest canopy cover changes in Antalya, Turkey, a region characterized by rich biodiversity, recurrent forest fires, and rapid rehabilitation efforts. Employing advanced remote sensing techniques, the research integrates ICESat-2 ATL08 segment data, Landsat satellite imagery, and Esri Sentinel-2 Land Cover datasets to generate forest canopy cover (FCC) maps for the years 2019 and 2022. The study area was chosen due to its high population, significant tourism industry, and frequent forest fires, making it a critical region for biodiversity and forest resources in Turkey. The Canopy Cover Estimation Method (CCEM) was employed, achieving accuracy percentages ranging from 77% to 86%, demonstrating its efficacy in estimating FCC without the need for fieldwork. The integration of annual updates from the Esri Sentinel-2 Land Cover dataset as auxiliary data greatly enhanced the reliability and accuracy of the analyses. Despite challenges posed by the study area and differences in spatial resolution among the datasets, the study successfully demonstrated the utility of integrating ICESat-2 segment data and Landsat imagery for understanding the dynamics of FCC in a complex environment. The results contribute to more informed decision-making in forest management and conservation efforts. Future research should focus on refining the methodologies to minimize spectral misclassification, improving the spatial resolution of the FCC maps, and leveraging advancements in remote sensing technology to obtain more accurate and reliable results.

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Section: Data Acquisition Filtering and Ccemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring forest canopy cover change with ICESat-2 Data in fire-prone areas: A case study in Antalya, Türkiye

Akturk

2023

AFR

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“…Spaceborne LiDAR has been widely used in global forest canopy height estimation [ 1 , 2 , 3 ] due to its rapid and cost-effective detection of surface typography and vertical forest structure [ 2 , 4 , 5 , 6 ]. The accuracy of global forest height data evaluated by the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat) has been proven over the past few decades [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Density-Based Multilevel Terrain-Adaptive Noise Removal Method for ICESat-2 Photon-Counting Data

Wang,

Zhang,

Zhang

et al. 2023

Sensors

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The photon point clouds collected by the high-sensitivity single-photon detector on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) are utilized in various applications. However, the discretely distributed noise among the signal photons greatly increases the difficulty of signal extraction, especially the edge noise adjacent to signals. To detect signal photons from vegetation coverage areas at different slopes, this paper proposes a density-based multilevel terrain-adaptive noise removal method (MTANR) that identifies noise in a coarse-to-fine strategy based on the distribution of noise photons and is evaluated with high-precision airborne LiDAR data. First, the histogram-based successive denoising method was used as a coarse denoising process to remove distant noise and part of the sparse noise, thereby increasing the fault tolerance of the subsequent steps. Second, a rotatable ellipse that adaptively corrects the direction and shape based on the slope was utilized to search for the optimal filtering direction (OFD). Based on the direction, sparse noise removal was accomplished robustly using the Otsu’s method in conjunction with the ordering points to identify the clustering structure (OPTICS) and provide a nearly noise-free environment for edge searching. Finally, the edge noise was removed by near-ground edge searching, and the signal photons were better preserved by the surface lines. The proposed MTANR was validated in four typical experimental areas: two in Baishan, China, and two in Taranaki, New Zealand. A comparison was made with three other representative methods, namely differential, regressive, and Gaussian adaptive nearest neighbor (DRAGANN), used in ATL08 products, local distance statistics (LDS), and horizontal ellipse-based OPTICS. The results demonstrated that the values of the F1 score for the signal photon identification achieved by the proposed MTANR were 0.9762, 0.9857, 0.9839, and 0.9534, respectively, which were higher than those of the other methods mentioned above. In addition, the qualitative and quantitative results demonstrated that MTANR outperformed in scenes with steep slopes, abrupt terrain changes, and uneven vegetation coverage.

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Mapping percent canopy cover using individual tree- and area-based procedures that are based on airborne LiDAR data: Case study from an oak-hickory-pine forest in the USA

Vatandaslar,

Lee,

Bettinger

et al. 2024

Ecological Indicators

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ICESat-2 for Canopy Cover Estimation at Large-Scale on a Cloud-Based Platform

Cited by 6 publications

References 78 publications

Monitoring forest canopy cover change with ICESat-2 Data in fire-prone areas: A case study in Antalya, Türkiye

Monitoring forest canopy cover change with ICESat-2 Data in fire-prone areas: A case study in Antalya, Türkiye

A Density-Based Multilevel Terrain-Adaptive Noise Removal Method for ICESat-2 Photon-Counting Data

Mapping percent canopy cover using individual tree- and area-based procedures that are based on airborne LiDAR data: Case study from an oak-hickory-pine forest in the USA

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