Forest Canopy Cover Research Articles

Determination of forest canopy cover of different sized stands with diverse structure using ALS data: case study of the Białowieża Forest (Poland)

ABSTRACT The need for objective methods to determine tree canopy cover (CC) across large numbers of stands has led to the development of techniques that utilise airborne laser scanning (ALS) data, which provides a reproducible and detailed representation of canopy geometry. We developed a method for determining CC area and evaluated the estimation accuracy for stands of different sizes, structure and composition. This method is based on tree crown geometries obtained from ALS data, and verified with field measurements using data for 3245 stands of the Białowieża Forest District in Poland. In relatively large stands (3–5 ha), the theoretical error of prediction decreased from 0.13 to 0.10 with increasing stand area. In stands larger than 10 ha, however, the error in estimating CC was less than ±0.10. Although every estimation method comes with its own assumptions and errors, the presented method eliminates the subjectivity in observer bias prevalent in traditional field-based ocular assessments and provides a more transparent and methodologically uniform approach for estimating CC in forests.

Larger and older church forests promote bird species richness, diversity and resilience across northwest Ethiopia's deforested landscapes

Birds are vital bioindicators of biodiversity and ecosystem health, but their persistence and ecological roles are under severe threat from human activities in the Lake Tana Basin, Ethiopia. Rapid land and forest degradation driven by agricultural expansion, urbanization, and the rising demand for fuelwood imperil avian community structure and ecosystem functions in this region. However, amidst this ecological challenge, church forests have emerged as crucial sanctuaries. Despite their small size and isolation from each other by mainly agricultural land-use, these church forests provide indispensable ecosystem services and play a pivotal role in preserving biodiversity. Understanding the significance of church forests for avian biodiversity and the factors influencing bird species richness and community composition is paramount. To this end, a comprehensive survey was conducted in 24 church forests between November and December 2018. Data collection involved both point counts utilizing Audiomoth acoustic loggers and transect surveys. The study revealed a total of 131 bird species in the surveyed church forests, including endemic and threatened species. These birds exhibited diverse foraging habits and functional traits. Notably, church forest size and canopy cover were positively associated with species richness, highlighting the importance of preserving and potentially expanding these forested areas. Furthermore, the research identified key factors driving the similarity of bird communities in church forests, notably forest surface area, canopy cover, and altitude. Importantly, resilience metrics demonstrated significant positive relationships with species richness, emphasizing the conservation value of these unique ecosystems. Our study underscores the critical role of church forests in safeguarding avian biodiversity in the Lake Tana Basin, and our findings advocate for conservation efforts aimed at preserving and enhancing the region's church forests, as their continued degradation could jeopardize both ecological and socio-economic potential in this network of remnant forests.

Forest canopy cover affects microclimate buffering during an extreme heat event

Increasing temperatures and extreme heat episodes have become more common with climate change. While forests are known to buffer increasing temperatures (relative to non-forested areas), whether this buffering is maintained under extreme temperature events is relatively unknown. Here we assess whether forests continue to buffer microclimate (specifically temperatures) during an extreme heat event: the Pacific Northwest (PNW) heat dome in June 2021. We use a combination of ground-based and regional climate data and find that forest understories were 3 °C cooler than a clear-cut area and 4 °C cooler than regional temperatures during the PNW heat dome. By examining forests with different levels of canopy cover we also found that the buffering capacity of forests is greater under denser canopies even under extreme heat events. Additionally, we found vertical variation in thermal buffering, with the greatest amount of buffering at the surface of the forest floor. Overall, our findings suggest that temperate coastal forests, that are known to buffer average temperatures, can also act as microclimate buffers during extreme heat events like the heat dome that occurred in the PNW in 2021. This could be good news for forest dwelling organisms that are sensitive to such extreme heat events.

From simple linear regression to machine learning methods: Canopy cover modelling of a young forest using planet data

Accurate canopy cover estimation is essential for mature and early-stage young forests, as it guides forest management and silvicultural activities necessary for their growth and regeneration. However, obtaining precise measurements of canopy cover in the field is time-consuming and challenging, especially at the regional and landscape levels. Remote sensing techniques offer a promising alternative to traditional field-based measurements for estimating forest canopy cover. In this study, our objective is to estimate forest canopy cover using vegetation indices derived from the multispectral bands of PlanetScope (Planet Lab, Inc., San Francisco, CA, USA). To the best of our knowledge, this is the first study to utilise PlanetScope imagery data for estimating canopy cover in young boreal forests. Based on the analysis of four bands (green, blue, red, and near-infrared) from PlanetScope imagery, 43 vegetation indices, including four spectral bands and 13 salinity indices, were computed to select predictors in canopy cover modelling. Six regression models were employed to model canopy cover: linear, elastic net, support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine. All the models demonstrated good performance for both the training dataset (R2 = 0.58–0.69) and the testing dataset (R2 = 0.59–0.64, RMSE = 0.16–0.18, rRMSE = 22%–23%, and MAE = 0.12–0.14). Based on the fit statistics in the training and testing datasets and the paired t-test, our study identified the light gradient boosting machine as the most suitable model for predicting canopy cover in young boreal forests. For the light gradient boosting machine, the R2 value was 0.69 (training), and for testing data, the R2 = 0.64, RMSE = 0.16, rRMSE = 22%, and MAE = 0.12. Therefore, we recommend that future researchers utilise Planet multispectral data and the light gradient boosting machine regression to estimate forest canopy cover at a higher spatial resolution. However, exploring additional machine learning algorithms and explicitly boosting methods when computing forest canopy cover using satellite remote sensing is strongly advised.

Spatiotemporal Analysis of Urban Forest in Chattanooga, Tennessee from 1984 to 2021 Using Landsat Satellite Imagery

Chattanooga, Tennessee is one of many cities in the Southeastern United States that is experiencing rapid urban growth. As these metropolitan areas continue to grow larger, more and more of Earth’s unique temperate forest, an ecosystem of enormous cultural, ecological, and recreational significance in the Southeastern United States, is destroyed to make way for new urban development. This research takes advantage of the extensive temporal archive of multispectral satellite imagery provided by the Landsat program to conduct a 37-year analysis of urban forest canopy cover across the City of Chattanooga. A time series of seven Landsat 5 scenes and three Landsat 8 scenes were acquired between 1984 and 2021 at an interval of five years or less. Each multispectral image was processed digitally and classified into a four-class thematic raster using a supervised hybrid classification scheme with a support vector machine (SVM) algorithm. The obtained results showed a loss of up to 43% of urban forest canopy and a gain of up to 134% urban land area in the city. Analyzing the multidecade spatiotemporal forest canopy in a rapidly expanding metropolitan center, such as Chattanooga, could help direct sustainable development efforts towards areas urbanizing at an above-average rate.

Long-term annual estimation of forest above ground biomass, canopy cover, and height from airborne and spaceborne sensors synergies in the Iberian Peninsula

The Mediterranean Basin has experienced substantial land use changes as traditional agriculture decreased and population migrated from rural to urban areas, which have resulted in a large forest cover increase. The combination of Landsat time series, providing spectral information, with lidar, offering three-dimensional insights, has emerged as a viable option for the large-scale cartography of forest structural attributes across large time spans. Here we develop and test a comprehensive framework to map forest above ground biomass, canopy cover and forest height in two regions spanning the most representative biomes in the peninsular Spain, Mediterranean (Madrid region) and temperate (Basque Country). As reference, we used lidar-based direct estimates of stand height and forest canopy cover. The reference biomass and volume were predicted from lidar metrics. Landsat time series predictors included annual temporal profiles of band reflectance and vegetation indices for the 1985–2023 period. Additional predictor variables including synthetic aperture radar, disturbance history, topography and forest type were also evaluated to optimize forest structural attributes retrieval. The estimates were independently validated at two temporal scales, i) the year of model calibration and ii) the year of the second lidar survey. The final models used as predictor variables only Landsat based metrics and topographic information, as the available SAR time-series were relatively short (1991–2011) and disturbance information did not decrease the estimation error. Model accuracies were higher in the Mediterranean forests when compared to the temperate forests (R2 = 0.6–0.8 vs. 0.4–0.5). Between the first (1985–1989) and the last (2020–2023) decades of the monitoring period the average forest cover increased from 21 ± 2% to 32 ± 1%, mean height increased from 6.6 ± 0.43 m to 7.9 ± 0.18 m and the mean biomass from 31.9 ± 3.6 t ha−1 to 50.4 ± 1 t ha−1 for the Mediterranean forests. In temperate forests, the average canopy cover increased from 55 ± 4% to 59 ± 3%, mean height increased from 15.8 ± 0.77 m to 17.3 ± 0.21m, while the growing stock volume increased from 137.8 ± 8.2 to 151.5 ± 3.8 m3 ha−1. Our results suggest that multispectral data can be successfully linked with lidar to provide continuous information on forest height, cover, and biomass trends.

Struktur Tegakan dan Tutupan Kanopi Ekosistem Hutan dan Parak di Kecamatan Lubuk Kilangan Kota Padang

This study aimed to determine the stand structure and tree canopy cover in forest and parak ecosystems in Lubuk Kilangan District, Padang City. This is a descriptive research with a direct observation throught purposive random sampling method. Data were collected by making six of 20 x 20 m plots in each primary forest and parak ecosystem. Tree diameters ≥10 cm were measured in each plot and canopy cover were collected using the Hemispherical Photography method using a mobile phone front camera equipped with a fish-eye lens. Data were analyzed in terms of stand density, basal area, Stand Basal Area (SBA), and percentage of canopy cover. The average tree density in the primary forest area was 913 individuals/ha, while the tree density in the parak was 467 individuals/ha. The SBA value in the primary forest was higher than in the parak, which was 64.10 m2/ha in the primary forest and 14,72 m2/ha in the parak. The average percentage of canopy cover in both areas shows that the percentage of primary forest canopy cover was greater than that of parak. The average value of the percentage of canopy cover in the forest area was 80.48%, while in the parak area was 71.23%. Correlation between tree density, canopy cover, SBA was only occured in primary forest area where the canopy cover negatively correlated with SBA. This study showed that the primary forest area and parak in Lubuk Kilangan Subdistrict have a good tree density, SBA and tree canopy covers.

An Advanced Terrain Vegetation Signal Detection Approach for Forest Structural Parameters Estimation Using ICESat-2 Data

Accurate forest structural parameters (such as forest height and canopy cover) support forest carbon monitoring, sustainable forest management, and the implementation of silvicultural practices. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is a spaceborne Light Detection and Ranging (LiDAR) satellite, offers significant potential for acquiring precise and extensive information on forest structural parameters. However, the ICESat-2 ATL08 product is significantly influenced by the geographical environment and forest characteristics, maintaining considerable potential for enhancing the accuracy of forest height estimation. Meanwhile, it does not focus on providing canopy cover data. To acquire accurate forest structural parameters, the Terrain Signal Neural Network (TSNN) framework was proposed, integrating Computer Vision (CV), Ordering Points to Identify the Clustering Structure (OPTICS), and deep learning. It encompassed an advanced approach for detecting terrain vegetation signals and constructing deep learning models for estimating forest structural parameters using ICESat-2 ATL03 raw data. First, the ATL03 footprints were visualized as Profile Raster Images of Footprints (PRIF), implementing image binarization through adaptive thresholding and median filtering denoising to detect the terrain. Second, the rough denoising buffers were created based on the terrain, combining with the OPTICS clustering and Gaussian denoising algorithms to recognize the terrain vegetation signal footprints. Finally, deep learning models (convolutional neural network (CNN), ResNet50, and EfficientNetB3) were constructed, training standardized PRIF to estimate forest structural parameters (including forest height and canopy cover). The results indicated that the TSNN achieved high accuracy in terrain detection (coefficient of determination (R2) = 0.97) and terrain vegetation signal recognition (F-score = 0.72). The EfficientNetB3 model achieved the highest accuracy in forest height estimation (R2 = 0.88, relative Root Mean Squared Error (rRMSE) = 13.5%), while the CNN model achieved the highest accuracy in canopy cover estimation (R2 = 0.80, rRMSE = 18.5%). Our results have significantly enhanced the accuracy of acquiring ICESat-2 forest structural parameters, while also proposing an original approach combining CV and deep learning for utilizing spaceborne LiDAR data.

Different Approaches of Forest Type Classifications for Argentina Based on Functional Forests and Canopy Cover Composition by Tree Species

Modern forestry systems rely on typologies of forest types (FTs). In Argentina, several proposals have been developed, but they lack unified criteria. The objective was to compare different approaches, specifically focusing on (i) phenoclusters (functional forests based on vegetation phenology variations and climate variables) and (ii) forest canopy cover composition by tree species. We conducted comparative uni-variate analyses using data from national forest inventories, forest models (biodiversity, carbon, structure), and regional climate. We assessed the performance of phenoclusters in differentiating the variability of native forests (proxy: forest structure), biodiversity (proxy: indicator species), and environmental factors (proxies: soil carbon stock, elevation, climate). Additionally, we proposed a simple FT classification methodology based on species composition, considering the basal area of tree species. Finally, we compared the performance of both proposals. Our findings showed that classifications based on forest canopy cover composition are feasible to implement in regions dominated by mono-specific forests. However, phenoclusters allowed for the increased complexity of categories at the landscape level. Conversely, in regions where multi-specific stands prevailed, classifications based on forest canopy cover composition proved ineffective; however, phenoclusters facilitated a reduction in complexity at the landscape level. These results offer a pathway to harmonize national FT classifications by employing criteria and indicators to achieve sustainable forest management and conservation initiatives.

Winter soil temperature varies with canopy cover in Siberian larch forests

In the Arctic, winter soil temperatures exert strong control over mean annual soil temperature and winter CO2 emissions. In tundra ecosystems there is evidence that plant canopy influences on snow accumulation alter winter soil temperatures. By comparison, there has been relatively little research examining the impacts of heterogeneity in boreal forest cover on soil temperatures. Using seven years of data from six sites in northeastern Siberia that vary in stem density we show that snow-depth and forest canopy cover exert equally strong control on cumulative soil freezing degrees days (FDDsoil). Together snow depth and canopy cover explain approximately 75% of the variance in linear models of FDDsoil and freezing n-factors (n f; calculated as the quotient of FDDsoil and FDDair), across sites and years. Including variables related to air temperature, or antecedent soil temperatures does not substantially improve models. The observed increase in FDDsoil with canopy cover suggests that canopy interception of snow or thermal conduction through trees may be important for winter soil temperature dynamics in forested ecosystems underlain by continuous permafrost. Our results imply that changes in Siberian larch forest cover that arise from climate warming or fire regime changes may have important impacts on winter soil temperature dynamics.

Combining visual interpretation and image segmentation to derive canopy cover index from high resolution satellite imagery in functionally diverse coniferous forests

Forest canopy cover is one of the most significant structural parameters of the forest stand that can be estimated using of aerial and satellite remote sensing. Even though sub-pixel analysis can be used to estimate the index on low-resolution imagery, high-resolution imagery provides more accurate details on forest canopy variability for ecological and forestry applications. However, the high variability of the images demands a more advanced approach to canopy cover measurement than the visual interpretation of single images or stereo pairs. These traditional methods are inefficient and limited in providing a comprehensive and accurate canopy cover assessment. An improvement of the method could involve classifying high spatial resolution images, separating and extracting the areas corresponding to canopy gaps, and generating canopy cover maps. This study offers valuable insights and reveals key differences between three methods for estimating canopy cover: ground measurements, visual photo interpretation and automatic extraction from classified images using pixel and object-based methods. The texture analysis approach was used for separating the “shadow” objects corresponding to gaps in the canopy from the shades cast on the lower trees. The sample plot-based visual interpretation of the images revealed comparable results between ground and satellite image canopy cover values (correlation coefficient of 0.74 for all plots), with lower correlations (r = 0.39) for uneven-aged stands. The results encourage the use of the texture analysis method, with satisfying accuracy (forest canopy cover differences of maximum 0.06 between ground, photo interpreted and extracted datasets). The method could be further integrated with complementary data like LIDAR or hyperspectral images.

Forest fire, thinning, and flood in wildland-urban interface: UAV and lidar-based estimate of natural disaster impacts

ContextWildland-urban interface (WUI) areas are facing increased forest fire risks and extreme precipitation events due to climate change, which can lead to post-fire flood events. The city of Flagstaff in northern Arizona, USA experienced WUI forest thinning, fire, and record rainfall events, which collectively contributed to large floods and damages to the urban neighborhoods and city infrastructure.ObjectivesWe demonstrate multi-temporal, high resolution image applications from an unoccupied aerial vehicle (UAV) and terrestrial lidar in estimating landscape disturbance impacts within the WUI. Changes in forest vegetation and bare ground cover in WUIs are particularly challenging to estimate with coarse-resolution satellite images due to fine-scale landscape processes and changes that often result in mixed pixels.MethodsUsing Sentinel-2 satellite images, we document forest fire impacts and burn severity. Using 2016 and 2021 UAV multispectral images and Structure-from-Motion data, we estimate post-thinning changes in forest canopy cover, patch sizes, canopy height distribution, and bare ground cover. Using repeat lidar data within a smaller area of the watershed, we quantify geomorphic effects in the WUI associated with the fire and subsequent flooding.ResultsWe document that thinning significantly reduced forest canopy cover, patch size, tree density, and mean canopy height resulting in substantially reduced active crown fire risks in the future. However, the thinning equipment ignited a forest fire, which burned the WUI at varying severity at the top of the watershed that drains into the city. Moderate-high severity burns occurred within 3 km of downtown Flagstaff threatening the WUI neighborhoods and the city. The upstream burned area then experienced 100-year and 200–500-year rainfall events, which resulted in large runoff-driven floods and sedimentation in the city.ConclusionWe demonstrate that UAV high resolution images and photogrammetry combined with terrestrial lidar data provide detailed and accurate estimates of forest thinning and post-fire flood impacts, which could not be estimated from coarser-resolution satellite images. Communities around the world may need to prepare their WUIs for catastrophic fires and increase capacity to manage sediment-laden stormwater since both fires and extreme weather events are projected to increase.

Forest Canopy Density Monitoring by Using Geospatial Techniques: A Study of Gadchiroli District, Maharashtra, India

Forest cover is a crucial part of the environment. It makes an essential contribution to the socio-economic and environmental welfare of the Nation. However, these forests are seriously threatened by deforestation, increased mining activity, population growth, uncontrolled urbanisation, a developing tendency of industrialisation, agricultural land purpose, shifting cultivation, effects on soil, water, and biodiversity, unsustainable forms of human activities and others. As a result, developing strategies to promote sustainable forest management, prevent desertification, prevent soil erosion, and halt environmental degradation is essential. Remote Sensing has enabled humans to observe and obtain information about the earth's surface with spatiotemporal changes. The Indian state of Maharashtra's Gadchiroli district is used as a study region. This study investigates forest canopy density and the spatiotemporal changes in forests. The geographical pattern of forest canopy density is displayed by several indices using data from Landsat 5 and Landsat 8 at 30 m spatial resolution. Try to make the study more relevant in the contemporary world. The research area's forest cover has changed through time, as shown by several multi-temporal data sets (1989 and 2019). The results revealed that between 1989 and 2019, forest canopy cover and forest density decreased. It indicated that over 30 years, 1045.51 sq. km of land had degraded. The amount of highly dense forest has decreased significantly over the research period, whereas the non-forest area has been gradually growing for the past 30 years.

The Minimum Target Diameter and the Harvest Age of Oak Natural Secondary Forests in Different Sites Conditions: Case Study in Hunan Province, China

Maintaining permanent forest canopy cover and eventually harvesting timber by predetermined target diameter are often considered as a prototype for future management of the oak natural forest. However, target diameters and harvest age based on average forest growth rates from wide geographical areas often hamper improved management of oak forests. In this study, based on the sampling of 129 target trees from 51 oak natural secondary forest plots in Hunan Province, China, an individual-tree DBH (diameter at breast height) growth model of oak target trees was developed, and the site type (41 levels) was related to the model as random effects by a nonlinear mixed-effects approach. Moreover, the 41 site types were clustered into four site type groups (STG1, STG2, STG3, and STG4) by the K-means clustering algorithm to improve the model performance and practicality. With the help of the model, the five target diameters (including 24, 30, 40, 50, and 60 cm) were simulated in each of the four STGs, and the minimum target diameter was determined for each STG based on the theory of quantitative maturity. In the four STGs, the harvest age of the 24 cm diameter target ranged from 30 to 51 years; the harvest age of the 60 cm target diameter ranged from 131 to 220 years, with the oaks failing to reach 60 cm in the lowest-quality STG4; the minimum target diameter ranged from 21 cm to 29 cm. Results showed that lower-quality sites exclude higher target diameters from optimal harvesting strategies, in contrast to the higher target diameter as a more reasonable strategy in higher quality sites, and that the minimum target diameter is significantly influenced by site conditions. Therefore, it is necessary to develop a diverse target-diameter-harvesting strategy adapted for the complex site conditions of oak forests in Hunan Province towards site-specific timber management to improve the sustainability of timber production in oak forests.

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics.

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR's footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran's I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran's I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C = 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis.

Estimation of Forest Canopy Cover by Combining ICESat-2/ATLAS Data and Geostatistical Method/Co-Kriging

Estimation of Forest Canopy Cover by Combining ICESat-2/ATLAS Data and Geostatistical Method/Co-Kriging

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

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.

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

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.

High-resolution forest canopy cover estimation in ecodiverse landscape using machine learning and Google Earth Engine: Validity and reliability assessment

The Forest Canopy Cover (FCC) is an important factor in the health and functioning of forests, as it plays a role in ecosystem services such as carbon sequestration, wildlife habitat, and water regulation. Techniques for accurately and efficiently mapping and extracting FCC information are evolving quickly, and it is of interest to evaluate their validity and reliability. The primary objectives of this study are to: 1) develop a large-scale FCC dataset with a spatial resolution of 1-m, 2) assess the spatial distribution of the FCC at a regional scale, and 3) examine the discrepancies in the FCC areas within the existing data from the Tree Canopy Cover (TCC) percent data by Hansen et al. (2013) and U.S. Forest Service TCC products at different spatial scales within the state of Arkansas (i.e., county and city levels). The FCC dataset was generated using high-resolution aerial imagery and a random forest (RF) machine learning algorithm, which were processed and analyzed using the Google Earth Engine cloud computing platform. The produced FCC dataset was validated using one-third of the observations (i.e., reference locations) obtained from the TCC percent data by Hansen et al. (2013) dataset and the 0.6-m spatial resolution National Agriculture Imagery Program (NAIP) aerial imagery. The results showed that the dataset successfully identified the FCC at a 1-m resolution in the study area, with an overall accuracy ranging between 83.31% and 94.35% per county. The spatial comparison results between the produced FCC dataset and both the Hansen et al. (2013) and USFS products also indicate a strong positive correlation, with the R2 values ranging between 0.94 and 0.98 for the county and the city levels. This dataset provides valuable information for monitoring, forecasting, and managing forest resources in Arkansas and other regions. Furthermore, the results of this study provide evidence that the use of machine learning and cloud computing technologies can produce high-resolution forest cover datasets and can be applied to other areas around the world.

Machine learning for modeling forest canopy height and cover from multi-sensor data in Northwestern Ethiopia.

Continuous mapping of the height and canopy cover of forests is vital for measuring forest biomass, monitoring forest degradation and restoration. In this regard, the contribution of Light Detection and Ranging (LiDAR) sensors, which were developed to obtain detailed data on forest composition across large geographical areas, is immense. Accordingly, this study aims to predict forest canopy cover and height in tropical forest areas utilizing Global Ecosystem Dynamics Investigation (GEDI) LIDAR, multisensor images, and random forest regression. To achieve this, we gathered predictor variables from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Sentinel-2 multispectral datasets, and Sentinel-1 synthetic aperture radar (SAR) backscatters. The model's accuracy was evaluated based on a validation dataset of GEDI Level 2A and Level 2B. The random forest method was used the combination of data layers from Sentinel-1, Sentinel-2, and topographic measurements to model forest canopy cover and height. The produced canopy height and cover maps had a resolution of 30m with R2 = 0.86 and an RMSE of 3.65m for forest canopy height and R2 = 0.87 and an RMSE of 0.15 for canopy cover for the year 2022. These results suggest that combining multiple variables and data sources improves canopy cover and height prediction accuracy compared to relying on a single data source. The output of this study could be helpful in creating forest management plans that support sustainable utilization of the forest resources.