The accuracy of large-area forest canopy cover estimation using Landsat in boreal region

Hadi,; Korhonen, Lauri; Hovi, Aarne; Rönnholm, Petri; Rautiainen, Miina

doi:10.1016/j.jag.2016.08.009

Cited by 29 publications

(12 citation statements)

References 34 publications

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“…Secondly, the global thematic forest products GFCC30TC and GFC30 have been validated as having accuracies of 90% and 90.94% at the global scale. GFCC30TC performs differently in different geographical regions: for example, it underestimates the high-canopy forest (canopy cover > 70%) but overestimates the low-canopy forest (canopy cover < 30%) in the boreal forest in Finland [56] and has a higher accuracy over North America (82% in Maryland) [57] than in South America (where the producer's and user's accuracies are 73:4% ± 0:09% and 94:7% ± 0:11%, respectively) [58]. TCC-2010 has been shown to have a higher accuracy than other forest products: for example, the producer's and user's accuracies were shown to be 88:4% ± 0:07% and 91:3% ± 0:07%, as against 73:4% ± 0:09% and 94:7% ± 0:11% for GFCC30TC, in South America [58].…”

Section: Zhang Et Almentioning

confidence: 99%

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

et al. 2021

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Land-cover mapping is one of the foundations of Earth science. As a result of the combined efforts of many scientists, numerous global land-cover (GLC) products with a resolution of 30 m have so far been generated. However, the increasing number of fine-resolution GLC datasets is imposing additional workloads as it is necessary to confirm the quality of these datasets and check their suitability for user applications. To provide guidelines for users, in this study, the recent developments in currently available 30 m GLC products (including three GLC products and thematic products for four different land-cover types, i.e., impervious surface, forest, cropland, and inland water) were first reviewed. Despite the great efforts toward improving mapping accuracy that there have been in recent decades, the current 30 m GLC products still suffer from having relatively low accuracies of between 46.0% and 88.9% for GlobeLand30-2010, 57.71% and 80.36% for FROM_GLC-2015, and 65.59% and 84.33% for GLC_FCS30-2015. The reported accuracies for the global 30 m thematic maps vary from 67.86% to 95.1% for the eight impervious surface products that were reviewed, 56.72% to 97.36% for the seven forest products, 32.73% to 98.3% for the six cropland products, and 15.67% to 99.7% for the six inland water products. The consistency between the current GLC products was then examined. The GLC maps showed a good overall agreement in terms of spatial patterns but a limited agreement for some vegetation classes (such as shrub, tree, and grassland) in specific areas such as transition zones. Finally, the prospects for fine-resolution GLC mapping were also considered. With the rapid development of cloud computing platforms and big data, the Google Earth Engine (GEE) greatly facilitates the production of global fine-resolution land-cover maps by integrating multisource remote sensing datasets with advanced image processing and classification algorithms and powerful computing capability. The synergy between the spectral, spatial, and temporal features derived from multisource satellite datasets and stored in cloud computing platforms will definitely improve the classification accuracy and spatiotemporal resolution of fine-resolution GLC products. In general, up to now, most land-cover maps have not been able to achieve the maximum (per class or overall) error of 5%–15% required by many applications. Therefore, more efforts are needed toward improving the accuracy of these GLC products, especially for classes for which the accuracy has so far been low (such as shrub, wetland, tundra, and grassland) and in terms of the overall quality of the maps.

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Section: Zhang Et Almentioning

confidence: 99%

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

et al. 2021

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“…Independent assessment was indispensable before the application of these satellite-based maps, which depended on accurate measurements of canopy cover as references. An easy-to-understand assessment method was to directly compare with field measurements [13,14]. However, the collection of field measurements was laborious and timeconsuming for evaluating maps with coarse resolution (e.g., 250 m of MODIS VCF), especially over mountainous areas [13,15,16].…”

Section: Introductionmentioning

confidence: 99%

“…An easy-to-understand assessment method was to directly compare with field measurements [13,14]. However, the collection of field measurements was laborious and timeconsuming for evaluating maps with coarse resolution (e.g., 250 m of MODIS VCF), especially over mountainous areas [13,15,16]. Alternatively, high-resolution satellite images, including QuickBird, WorldView, IKONOS, and GeoEye, were used to produce reference data [8,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Regional Sampling of Forest Canopy Covers Using UAV Visible Stereoscopic Imagery for Assessment of Satellite-Based Products in Northeast China

Zhang

et al. 2022

J Remote Sens

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Canopy cover is an important parameter affecting forest succession, carbon fluxes, and wildlife habitats. Several global maps with different spatial resolutions have been produced based on satellite images, but facing the deficiency of reliable references for accuracy assessments. The rapid development of unmanned aerial vehicle (UAV) equipped with consumer-grade camera enables the acquisition of high-resolution images at low cost, which provides the research community a promising tool to collect reference data. However, it is still a challenge to distinguish tree crowns and understory green vegetation based on the UAV-based true color images (RGB) due to the limited spectral information. In addition, the canopy height model (CHM) derived from photogrammetric point clouds has also been used to identify tree crowns but limited by the unavailability of understory terrain elevations. This study proposed a simple method to distinguish tree crowns and understories based on UAV visible images, which was referred to as BAMOS for convenience. The central idea of the BAMOS was the synergy of spectral information from digital orthophoto map (DOM) and structural information from digital surface model (DSM). Samples of canopy covers were produced by applying the BAMOS method on the UAV images collected at 77 sites with a size of about 1.0 km2 across Daxing’anling forested area in northeast of China. Results showed that canopy cover extracted by the BAMOS method was highly correlated to visually interpreted ones with correlation coefficient (r) of 0.96 and root mean square error (RMSE) of 5.7%. Then, the UAV-based canopy covers served as references for assessment of satellite-based maps, including MOD44B Version 6 Vegetation Continuous Fields (MODIS VCF), maps developed by the Global Land Cover Facility (GLCF) and by the Global Land Analysis and Discovery laboratory (GLAD). Results showed that both GLAD and GLCF canopy covers could capture the dominant spatial patterns, but GLAD canopy cover tended to miss scattered trees in highly heterogeneous areas, and GLCF failed to capture non-tree areas. Most important of all, obvious underestimations with RMSE about 20% were easily observed in all satellite-based maps, although the temporal inconsistency with references might have some contributions.

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“…According to the definition of forest given by the Food and Agriculture Organization of the United Nations [7], FCC is the key indicator to define forest, which refers to an area with a tree canopy cover of more than 10% and with an extent of more than 0.5 hectare. This indicates that the accurate estimation of FCC in low coverage areas, where the distinguishment between understory and overstory should be considered, is substantially important [8]. However, traditional field measurements tend to be too laborious or inaccurate for large areas.…”

Section: Introductionmentioning

confidence: 99%

Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images

Jin¹,

Qi²,

Huang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Forest canopy cover (FCC) plays an important role in many ecological, hydrological and forestry applications. For large-scale applications, FCC is usually estimated from remotely sensed data by inverting radiative transfer models (RTMs) or using data-driven regressions. In this study, we proposed a hybrid model, which combines a 3D RTM and transfer learning-based convolutional neural network (T-CNN), to estimate FCC from very high-resolution satellite images (e.g., Chinese GaoFen-2, 1 m resolution with 4 bands). Unlike common hybrid models that are purely trained with simulation data, T-CNN combines simulation data-based pre-training and actual data-based transfer learning, which is a widely used technique in artificial intelligence for finetuning models. The performance of T-CNN was compared with a random forest (RF) model and two general CNN models, including CNN trained with actual dataset only (Data-CNN) and CNN trained with RTM simulation data only (RTM-CNN). Results on the independent validation dataset (not used in training stage) showed that T-CNN had higher accuracy (RMSE = 0.121, R 2 = 0.83), compared with RF (RMSE = 0.26, R 2 = 0.61), Data-CNN (RMSE = 0.142, R 2 = 0.81), and RTM-CNN (RMSE = 0.144, R 2 = 0.73), which indicates that T-CNN has a strong transferability. Tests on different training sizes showed that T-CNN (0.084 < RMSE < 0.108) provided constantly better performances than RF (0.116 < RMSE < 0.122) and Data-CNN (0.103 < RMSE < 0.128), which demonstrates the potential of T-CNN as an alternative to RTM-based inversion and data-driven regressions to estimate FCC, especially when training data is imbalanced and inadequate.

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The accuracy of large-area forest canopy cover estimation using Landsat in boreal region

Cited by 29 publications

References 34 publications

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

Regional Sampling of Forest Canopy Covers Using UAV Visible Stereoscopic Imagery for Assessment of Satellite-Based Products in Northeast China

Combining 3D Radiative Transfer Model and Convolutional Neural Network to Accurately Estimate Forest Canopy Cover From Very High-Resolution Satellite Images

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