scholarly journals Estimation of forest canopy cover: a comparison of field measurement techniques

Silva Fennica ◽  
2006 ◽  
Vol 40 (4) ◽  
Author(s):  
Lauri Korhonen ◽  
Kari Korhonen ◽  
Miina Rautiainen ◽  
Pauline Stenberg
Keyword(s):  
Field Measurement ◽  
Forest Canopy ◽  
Measurement Techniques ◽  
Canopy Cover

scholarly journals Enhancing Methods for Under-Canopy Unmanned Aircraft System Based Photogrammetry in Complex Forests for Tree Diameter Measurement

2020 ◽  
Vol 12 (10) ◽  
pp. 1652
Author(s):  
Sean Krisanski ◽  
Mohammad Sadegh Taskhiri ◽  
Paul Turner
Keyword(s):  
Forest Canopy ◽  
Measurement Techniques ◽  
Digital Terrain Model ◽  
Canopy Cover ◽  
Field Measurements ◽  
Point Clouds ◽  
3D Models ◽  
Unmanned Aircraft ◽  
Global Navigation Satellite Systems ◽  
Diameter Measurement

The application of Unmanned Aircraft Systems (UAS) beneath the forest canopy provides a potentially valuable alternative to ground-based measurement techniques in areas of dense canopy cover and undergrowth. This research presents results from a study of a consumer-grade UAS flown under the forest canopy in challenging forest and terrain conditions. This UAS was deployed to assess under-canopy UAS photogrammetry as an alternative to field measurements for obtaining stem diameters as well as ultra-high-resolution (~400,000 points/m2) 3D models of forest study sites. There were 378 tape-based diameter measurements collected from 99 stems in a native, unmanaged eucalyptus pulchella forest with mixed understory conditions and steep terrain. These measurements were used as a baseline to evaluate the accuracy of diameter measurements from under-canopy UAS-based photogrammetric point clouds. The diameter measurement accuracy was evaluated without the influence of a digital terrain model using an innovative tape-based method. A practical and detailed methodology is presented for the creation of these point clouds. Lastly, a metric called the Circumferential Completeness Index (CCI) was defined to address the absence of a clearly defined measure of point coverage when measuring stem diameters from forest point clouds. The measurement of the mean CCI is suggested for use in future studies to enable a consistent comparison of the coverage of forest point clouds using different sensors, point densities, trajectories, and methodologies. It was found that root-mean-squared-errors of diameter measurements were 0.011 m in Site 1 and 0.021 m in the more challenging Site 2. The point clouds in this study had a mean validated CCI of 0.78 for Site 1 and 0.7 for Site 2, with a mean unvalidated CCI of 0.86 for Site 1 and 0.89 for Site 2. The results in this study demonstrate that under-canopy UAS photogrammetry shows promise in becoming a practical alternative to traditional field measurements, however, these results are currently reliant upon the operator’s knowledge of photogrammetry and his/her ability to fly manually in object-rich environments. Future work should pursue solutions to autonomous operation, more complete point clouds, and a method for providing scale to point clouds when global navigation satellite systems are unavailable.

scholarly journals Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 433
Author(s):  
Xiaolan Huang ◽  
Weicheng Wu ◽  
Tingting Shen ◽  
Lifeng Xie ◽  
Yaozu Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Google Earth ◽  
Tree Canopy ◽  
Landsat Data ◽  
High Resolution Data ◽  
Modis Ndvi ◽  
Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

Forest canopy cover analysis using UAS lidar

2017 ◽  
Author(s):  
Qingwang Liu ◽  
Shiming Li ◽  
Kailong Hu ◽  
Yong Pang ◽  
Zengyuan Li
Keyword(s):  
Forest Canopy ◽  
Canopy Cover

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

2016 ◽  
Vol 53 ◽  
pp. 118-127 ◽  
Author(s):  
Hadi ◽  
Lauri Korhonen ◽  
Aarne Hovi ◽  
Petri Rönnholm ◽  
Miina Rautiainen
Keyword(s):  
Forest Canopy ◽  
Canopy Cover ◽  
Large Area ◽  
Boreal Region

scholarly journals Spatial and temporal variability in the ratio of trace gases emitted from biomass burning

2011 ◽  
Vol 11 (8) ◽  
pp. 3611-3629 ◽  
Author(s):  
T. T. van Leeuwen ◽  
G. R. van der Werf
Keyword(s):  
Temporal Variability ◽  
Field Measurement ◽  
Environmental Variables ◽  
Trace Gases ◽  
Measurement Techniques ◽  
Arithmetic Mean ◽  
Fire Season ◽  
Burned Area ◽  
Spatial And Temporal Variability ◽  
Fire Emissions

Abstract. Fires are a major source of trace gases and aerosols to the atmosphere. The amount of biomass burned is becoming better known, most importantly due to improved burned area datasets and a better representation of fuel consumption. The spatial and temporal variability in the partitioning of biomass burned into emitted trace gases and aerosols, however, has received relatively little attention. To convert estimates of biomass burned to trace gas and aerosol emissions, most studies have used emission ratios (or emission factors (EFs)) based on the arithmetic mean of field measurement outcomes, stratified by biome. However, EFs vary substantially in time and space, even within a single biome. In addition, it is unknown whether the available field measurement locations provide a representative sample for the various biomes. Here we used the available body of EF literature in combination with satellite-derived information on vegetation characteristics and climatic conditions to better understand the spatio-temporal variability in EFs. While focusing on CO, CH4, and CO2, our findings are also applicable to other trace gases and aerosols. We explored relations between EFs and different measurements of environmental variables that may correlate with part of the variability in EFs (tree cover density, vegetation greenness, temperature, precipitation, and the length of the dry season). Although reasonable correlations were found for specific case studies, correlations based on the full suite of available measurements were lower and explained about 33%, 38%, 19%, and 34% of the variability for respectively CO, CH4, CO2, and the Modified Combustion Efficiency (MCE). This may be partly due to uncertainties in the environmental variables, differences in measurement techniques for EFs, assumptions on the ratio between flaming and smoldering combustion, and incomplete information on the location and timing of EF measurements. We derived new mean EFs, using the relative importance of each measurement location with regard to fire emissions. These weighted averages were relatively similar to the arithmetic mean. When using relations between the environmental variables and EFs to extrapolate to regional and global scales, we found substantial differences, with for savannas 13% and 22% higher CO and CH4 EFs than the arithmetic mean of the field studies, possibly linked to an underrepresentation of woodland fires in EF measurement locations. We argue that from a global modeling perspective, future measurement campaigns could be more beneficial if measurements are made over the full fire season, and if relations between ambient conditions and EFs receive more attention.

scholarly journals Detecting Long-Term Urban Forest Cover Change and Impacts of Natural Disasters Using High-Resolution Aerial Images and LiDAR Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1820
Author(s):  
Raoul Blackman ◽  
Fei Yuan
Keyword(s):  
Remote Sensing ◽  
Ecosystem Services ◽  
Forest Canopy ◽  
High Spatial Resolution ◽  
Urban Forest ◽  
Urban Canopy ◽  
Canopy Cover ◽  
Random Point ◽  
Aerial Images ◽  
Lidar Data

Urban forests provide ecosystem services; tree canopy cover is the basic quantification of ecosystem services. Ground assessment of the urban forest is limited; with continued refinement, remote sensing can become an essential tool for analyzing the urban forest. This study addresses three research questions that are essential for urban forest management using remote sensing: (1) Can object-based image analysis (OBIA) and non-image classification methods (such as random point-based evaluation) accurately determine urban canopy coverage using high-spatial-resolution aerial images? (2) Is it possible to assess the impact of natural disturbances in addition to other factors (such as urban development) on urban canopy changes in the classification map created by OBIA? (3) How can we use Light Detection and Ranging (LiDAR) data and technology to extract urban canopy metrics accurately and effectively? The urban forest canopy area and location within the City of St Peter, Minnesota (MN) boundary between 1938 and 2019 were defined using both OBIA and random-point-based methods with high-spatial-resolution aerial images. Impacts of natural disasters, such as the 1998 tornado and tree diseases, on the urban canopy cover area, were examined. Finally, LiDAR data was used to determine the height, density, crown area, diameter, and volume of the urban forest canopy. Both OBIA and random-point methods gave accurate results of canopy coverages. The OBIA is relatively more time-consuming and requires specialist knowledge, whereas the random-point-based method only shows the total coverage of the classes without locational information. Canopy change caused by tornado was discernible in the canopy OBIA-based classification maps while the change due to diseases was undetectable. To accurately exact urban canopy metrics besides tree locations, dense LiDAR point cloud data collected at the leaf-on season as well as algorithms or software developed specifically for urban forest analysis using LiDAR data are needed.

Improving Estimation of Forest Canopy Cover by Introducing Loss Ratio of Laser Pulses Using Airborne LiDAR

2020 ◽  
Vol 58 (1) ◽  
pp. 567-585 ◽  
Author(s):  
Qingwang Liu ◽  
Liyong Fu ◽  
Guangxing Wang ◽  
Shiming Li ◽  
Zengyuan Li ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Canopy Cover ◽  
Laser Pulses ◽  
Airborne Lidar ◽  
Loss Ratio
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