Estimate Canopy Cover Research Articles

Estimating canopy cover loss with Landsat dense time series: a Mortality Magnitude Index for the Ecosystem Disturbance and Recovery Tracker (eDaRT)

Estimating canopy cover loss with Landsat dense time series: a Mortality Magnitude Index for the Ecosystem Disturbance and Recovery Tracker (eDaRT)

High-resolution mapping of grassland canopy cover in China through the integration of extensive drone imagery and satellite data

Canopy cover is a crucial indicator for assessing grassland health and ecosystem services. However, achieving accurate high-resolution estimates of grassland canopy cover at a large spatial scale remains challenging due to the limited spatial coverage of field measurements and the scale mismatch between field measurements and satellite imagery. In this study, we addressed these challenges by proposing a regression-based approach to estimate large-scale grassland canopy cover, leveraging the integration of drone imagery and multisource remote sensing data. Specifically, over 90,000 10 × 10 m drone image tiles were collected at 1,255 sites across China. All drone image tiles were classified into grass and non-grass pixels to generate ground-truth canopy cover estimates. These estimates were then temporally aligned with satellite imagery-derived features to build a random forest regression model to map the grassland canopy cover distribution of China. Our results revealed that a single classification model can effectively distinguish between grass and non-grass pixels in drone images collected across diverse grassland types and large spatial scales, with multilayer perceptron demonstrating superior classification accuracy compared to Canopeo, support vector machine, random forest, and pyramid scene parsing network. The integration of extensive drone imagery successfully addressed the scale-mismatch issue between traditional ground measurements and satellite imagery, contributing significantly to enhancing mapping accuracy. The national canopy cover map of China generated for the year 2021 exhibited a spatial pattern of increasing canopy cover from northwest to southeast, with an average value of 56 % and a standard deviation of 26 %. Moreover, it demonstrated high accuracy, with a coefficient of determination of 0.89 and a root-mean-squared error of 12.38 %. The resulting high-resolution canopy cover map of China holds great potential in advancing our comprehension of grassland ecosystem processes and advocating for the sustainable management of grassland resources.

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.

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.

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.

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.

Using field-based, photogrammetric point cloud, orthophoto and LiDAR-derived metrics to assess forest structure–snowpack relationships in the Great Lakes–St. Lawrence Forest region

The efficacy of field-based, photogrammetric point cloud, orthophoto and light detection and ranging datasets to describe forest structure and resolve forest–snowpack relationships in a mixed forest region was evaluated over two years at the point and transect scales. Hemispheric photo-derived canopy metrics correlated well with remotely sensed metrics, but tree bole metrics were not effectively derived from remotely sensed data. Significant differences in melt rate and snow-free date were found across forest type at the transect scale. Field and remotely sensed estimates of canopy cover were highly correlated with melt rate and snow-free date at the point scale, which aligns with previous literature and understanding of snowmelt processes. However, significant correlations were only present during the 2016 study year, which was attributed to canopy-controlled solar radiation-driven melt in 2016 versus more spatially uniform turbulent flux-driven melt in 2017. Peak snow water equivalent metrics were not correlated well with canopy or tree height metrics, contrary to previous research. This was likely due to mid-winter melt events throughout both study years, where a mix of accumulation and melt processes confounded forest–snowpack relationships. This study demonstrates that widely available remotely sensed data with a broad coverage can be used to: (i) describe forest–snowpack relationships in mixed hardwood, coniferous forests and (ii) elucidate the variability of forest–snowpack relationships under different climate conditions in this environment.

Estimation of oil palm’s leaf area index (LAI) using Unmanned Aerial Vehicle (UAV) images

Leaf Area Index (LAI) is a parameter commonly used to indicate oil palm growth and production. The destructive method is the standard method to estimate LAI. However, this method requires much effort and cost and can only be done at a specific period. Unmanned Aerial Vehicles (UAVs), which have been widely used for mapping and calculating trees in oil palm plantations, have the potential to estimate LAI values quickly, efficiently, and without disturbing the oil palm tree. This research was conducted to develop the model for predicting LAI based on UAV images. Data was collected from six plots of 12-year-old oil palm trees at Adolina Estate, Serdang Bedagai, North Sumatra, Indonesia. The research was conducted on six varieties released by IOPRI, consisting of DxP Yangambi, DxP Langkat, DxP Simalungun, DyP Dumpy, DxP LaMe, and DxP PPKS 540. The estimated canopy cover from the UAV images was employed as an independent factor (x) compared to the calculated LAI from the destructive method, which was used as a dependent factor (y). The results showed that the LAI estimation using UAV imagery followed a linear model with R-squared values ranging from 0.3874-0.9556. In conclusion, despite requiring further research, UAV images could be used as rapid tools to estimate oil palm LAI.

Enhancing global rainfall interception loss estimation through vegetation structure modeling

Accurate estimates of rainfall interception loss are crucial for modeling the water balance of forested areas. However, considerable regional variability exists in the interception process, and much uncertainty remains. This study enhances the estimation of rainfall interception loss at the global scale by integrating remote sensing products into the parameterization of Gash’s analytical model. We refer to this enhanced configuration as the Global Interception Model (GIM). High-resolution satellite imagery was used to derive vegetation indices and spectral reflectance, which were then employed in linear regression models to estimate canopy cover fraction (c) and vegetation water storage capacity (Sv). Their importance in ecological processes, and land and water resource management, makes the modeling of these parameters of particular interest. The other two parameters required to run the Gash model, namely the mean rainfall and evaporation rates under saturated canopy conditions, were obtained via the integration of MWSEP and ERA5-Land meteorological products. Modeling performance was evaluated using in situ measurements and gridded datasets. GIM estimates exhibited a superior performance statistic when compared to PMLv2 and GLEAMv3.7a. Our results demonstrate the high potential of this approach for improving the accuracy of rainfall interception loss estimates from local to global scales.

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.

Monitoring canopy quality and improving equitable outcomes of urban tree planting using LiDAR and machine learning

Urban tree canopies are fundamental to mitigating the impacts of climate change within cities as well as providing a range of other important ecosystem, health, and amenity benefits. However, urban tree planting initiatives do not typically utilize data about both the horizontal and vertical dimensions of the tree canopy, despite height being a critical determinant of the quality and value of urban canopy cover. We present a novel pipeline that uses airborne LiDAR data to train a multi-task machine learning model to generate estimates of both canopy cover and height in urban areas. We apply this to multi-source multi-spectral imagery for the case study of Chicago, USA. Our results indicate that a multi-task UNet convolutional neural network can be used to generate reliable estimates of canopy cover and height from aerial and satellite imagery. We then use these canopy estimates to allocate 75,000 trees from Chicago’s recent green initiative under four scenarios, minimizing the urban heat island effect and then optimizing for an equitable canopy distribution, comparing results when only canopy cover is used, and when both canopy cover and height are considered. Through the introduction of this novel pipeline, we show that including canopy height within decision-making processes allows the distribution of new trees to be optimised to further reduce the urban heat island effect in localities where trees have the highest cooling potential and allows trees to be more equitably distributed to communities with lower quality canopies.

Positive effects of tree diversity on tropical forest restoration in a field-scale experiment.

Experiments under controlled conditions have established that ecosystem functioning is generally positively related to levels of biodiversity, but it is unclear how widespread these effects are in real-world settings and whether they can be harnessed for ecosystem restoration. We used remote-sensing data from the first decade of a long-term, field-scale tropical restoration experiment initiated in 2002 to test how the diversity of planted trees affected recovery of a 500-ha area of selectively logged forest measured using multiple sources of satellite data. Replanting using species-rich mixtures of tree seedlings with higher phylogenetic and functional diversity accelerated restoration of remotely sensed estimates of aboveground biomass, canopy cover, and leaf area index. Our results are consistent with a positive relationship between biodiversity and ecosystem functioning in the lowland dipterocarp rainforests of SE Asia and demonstrate that using diverse mixtures of species can enhance their initial recovery after logging.

Forest Canopy Cover Inversion Exploration Using Multi-Source Optical Data and Combined Methods

An accurate estimation of canopy cover can provide an important basis for forest ecological management by understanding the forest status and change patterns. The aim of this paper is to investigate the four methods of the random forest (RF), support vector regression (SVR), k-nearest neighbor (KNN), and k-nearest neighbor with fast iterative features selection (KNN-FIFS) for modeling forest canopy cover, and to evaluate three mainstream optical data sources—Landsat8 OLI, Sentinel-2A, Gaofen-1 (GF-1)—and three types of data combined comparatively by selecting the optimal modeling method. The paper uses the Daxinganling Ecological Station of Genhe City, Inner Mongolia, as the research area, and is based on three types of multispectral remote sensing data, extracting spectral characteristics, textural characteristics, terrain characteristics; the Kauth–Thomas transform (K-T transform); and color transformation characteristics (HIS). The optimal combination of features was selected using three feature screening methods, namely stepwise regression, RF, and KNN-FIFS, and the four methods: RF, SVR KNN, and KNN-FIFS, were combined to carry out the evaluation analysis regarding the accuracy of forest canopy cover modeling: (1) In this study, a variety of remote sensing features were introduced, and the feature variables were selected by different parameter preference methods and then employed in modeling. Based on the four modeling inversion methods, the KNN-FIFS model achieves the best accuracy: the Landsat8 OLI with R2 = 0.60, RMSE = 0.11, and RMSEr = 14.64% in the KNN-FIFS model; the Sentinel-2A with R2 = 0.80, RMSE = 0.08, and RMSEr = 11.63% in the KNN-FIFS model; the GF-1 with R2 = 0.55, RMSE = 0.12, and RMSEr = 15.04% in the KNN-FIFS model; and the federated data with R2 = 0.82, RMSE = 0.08, and RMSEr = 10.40% in the KNN-FIFS model; (2) the three multispectral datasets have the ability to estimate forest canopy cover, and the modeling accuracy superior under the combination of multi-source data features; (3) under different optical data, KNN- FIFS achieves the best accuracy in the established nonparametric model, and its feature optimization method is better than that of the random forest optimization method. For the same model, the estimation result of the joint data is better than the single optical data; thus, the KNN-FIFS model, with specific parameters, can significantly improve the inversion accuracy and efficiency of forest canopy cover evaluation from different data sources.

A national assessment of urban forest carbon storage and sequestration in Canada

During a time of rapid urban growth and development, it is becoming ever more important to monitor the carbon fluxes of our cities. Unlike Canada’s commercially managed forests that have a long history of inventory and modelling tools, there is both a lack of coordinated data and considerable uncertainty on assessment procedures for urban forest carbon. Nonetheless, independent studies have been carried out across Canada. To improve upon Canada’s federal government reporting on carbon storage and sequestration by urban forests, this study builds on existing data to develop an updated assessment of carbon storage and sequestration for Canada’s urban forests. Using canopy cover estimates derived from ortho-imagery and satellite imagery ranging from 2008 to 2012 and field-based urban forest inventory and assessment data from 16 Canadian cities and one US city, this study found that Canadian urban forests store approximately 27,297.8 kt C (− 37%, + 45%) in above and belowground biomass and sequester approximately 1497.7 kt C year−1 (− 26%, + 28%). In comparison with the previous national assessment of urban forest carbon, this study suggested that in urban areas carbon storage has been overestimated and carbon sequestration has been underestimated. Maximizing urban forest carbon sinks will contribute to Canada’s mitigation efforts and, while being a smaller carbon sink compared to commercial forests, will also provide important ecosystem services and co-benefits to approximately 83% of Canadian people.

Patterns of regional site index across a North American boreal forest gradient

Forest structure—the height, cover, vertical complexity, and spatial patterns of trees—is a key indicator of productivity variation across forested extents. During the 2017 and 2019 growing seasons, NASA’s Arctic-Boreal Vulnerability Experiment collected full-waveform airborne LiDAR using the land, vegetation and imaging sensor, sampling boreal and tundra landscapes across a variety of ecological regions from central Canada westward through Alaska. Here, we compile and archive a geo-referenced gridded suite of these data that include vertical structure estimates and novel horizontal cover estimates of vegetation canopy cover derived from vegetation’s vertical LiDAR profile. We validate these gridded estimates with small footprint airborne LiDAR, and link >36 million of them with stand age estimates from a Landsat time-series of tree-canopy cover that we confirm with plot-level disturbance year data. We quantify the regional magnitude and variability in site index, the age-dependent rates of forest growth, across 15 boreal ecoregions in North America. With this open archive suite of forest structure data linked to stand age, we bound current forest productivity estimates across a boreal structure gradient whose response to key bioclimatic drivers may change with stand age. These results, derived from a reduction of a large archive of airborne LiDAR and a Landsat time series, quantify forest productivity bounds for input into forest and ecosystem growth models, to update forecasts of changes in North America’s boreal forests by improving the regional parametrization of forest growth rates.

Differences in Canopy Cover Estimations from ALS Data and Their Effect on Fire Prediction

Canopy cover is a primary attribute used in empirical wildfire models for certain fuel types. Accurate estimation of canopy cover is a key to ensuring accurate prediction of fire spread and behaviour in these fuels. Airborne Laser Scanning (ALS) is a promising active remote sensing technology for estimating canopy cover in natural ecosystems since it can penetrate and measure the vegetation canopy. Various methods have been developed to estimate canopy cover from ALS data. However, little attention has been given to the evaluation of algorithms used to calculate canopy cover and the subsequent influence these algorithms can have on wildfire behaviour models. In this study we evaluate the effect of using different algorithms to calculate canopy cover on the performance of the Australian Mallee-heath fire spread model. ALS data was used to derive five canopy cover models. Fire spread metrics including burned area, unburned area within the fire extent, and extent of fire were compared for different model run times. The results show that these metrics are strongly influenced by choice of algorithm used to calculate canopy cover. The results from this study may provide practical guidance for the optimal selection of estimation methods in canopy cover mapping.

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

Forest canopy cover is an essential biophysical parameter of ecological significance, especially for characterizing woodlands and forests. This research focused on using data from the ICESat-2/ATLAS spaceborne lidar sensor, a photon-counting altimetry system, to map the forest canopy cover over a large country extent. The study proposed a novel approach to compute categorized canopy cover using photon-counting data and available ancillary Landsat images to build the canopy cover model. In addition, this research tested a cloud-mapping platform, the Google Earth Engine (GEE), as an example of a large-scale study. The canopy cover map of the Republic of Türkiye produced from this study has an average accuracy of over 70%. Even though the results were promising, it has been determined that the issues caused by the auxiliary data negatively affect the overall success. Moreover, while GEE offered many benefits, such as user-friendliness and convenience, it had processing limits that posed challenges for large-scale studies. Using weak or strong beams' segments separately did not show a significant difference in estimating canopy cover. Briefly, this study demonstrates the potential of using photon-counting data and GEE for mapping forest canopy cover at a large scale.

Canopy cover mediates the effects of a decadal increase in time since fire on arboreal birds

Wildfires impact animal populations directly, and indirectly through alteration of forest habitats. Recovery of populations and habitat structure occurs over time since fire, but knowledge is lacking about the relative importance of these processes as drivers of the occurrence of birds in fire-prone forests. We aimed to determine the extent to which canopy cover mediates the effects of a decadal increase in time since fire on the richness and occurrence of canopy bird species. We established sites at either short (5 years) or mid-range (16 years) time since fire in montane dry sclerophyll forests of south-eastern Australia. Canopy cover estimates were derived from airborne LiDAR data. Birds were surveyed using acoustic recorders, with the resulting data analyzed using Bayesian mediation models to partition direct (population processes) and indirect (canopy cover) effects of time since fire on canopy birds. The predictive accuracy of models representing partial mediation (direct and indirect effects) and complete mediation (indirect effects only) were then compared. The direct effects of wildfire on birds were minimal between five and 16 years since fire. Instead, indirect effects prevailed, with species richness and the occurrence of most canopy species increasing as canopy cover regenerated over time since fire. As these forests transition from short to mid-range time since fire, ongoing increases in canopy cover are of primary importance for birds. We recommend an approach to managing avian diversity that incorporates canopy cover in fire planning to optimize the retention of dwindling amounts of older forest under climate change.