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.

scholarly journals Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1912 ◽  
Author(s):  
Saeedeh Eskandari ◽  
Mohammad Reza Jaafari ◽  
Patricia Oliva ◽  
Omid Ghorbanzadeh ◽  
Thomas Blaschke
Keyword(s):  
Land Cover ◽  
Forest Canopy ◽  
Pearson Correlation ◽  
Canopy Cover ◽  
Google Earth ◽  
Tree Canopy ◽  
Zagros Forests ◽  
Tree Canopy Cover ◽  
Field Plots ◽  
Sentinel 2

The Zagros forests in Western Iran are valuable ecosystems that have been seriously damaged by human interference (harvesting the wood and forest sub-products, converting the forests to the agricultural lands, and grazing) and natural events (drought events and fire). In this study, we generated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests of Shirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field data for protective management. First, we assessed the accuracy of Google Earth data using 300 random field plots in 10 different land cover types. For land cover mapping, we evaluated the performance of four supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD), neural network (NN), and support vector machine (SVM)). The accuracy of the land cover maps was assessed using a set of 150 stratified random plots in Google Earth. We mapped the forest canopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. We calculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots). The linear regression between the NDVI values and the TCC% was used to obtain the predictive model of TCC% based on the NDVI. The results showed that Google Earth data yielded an overall accuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopy cover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, which is highly significant. The results also showed that the linear regression model is a good predictive model for TCC% estimation based on the NDVI (r2 = 0.864). The results can be used as a baseline for decision-makers to monitor land cover change in the region, whether produced by human activities or natural events and to establish measures for protective management of forests.

Temporal changes in boreal forest tree canopy cover along a gradient of gamma radiation

1985 ◽  
Vol 63 (1) ◽  
pp. 15-20 ◽  
Author(s):  
B. D. Amiro ◽  
J. R. Dugle
Keyword(s):  
Gamma Radiation ◽  
Boreal Forest ◽  
Gamma Rays ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Woody Species ◽  
Tree Canopy ◽  
Forest Site ◽  
Dose Rates ◽  
Tree Canopy Cover

A forest site in southeastern Manitoba has been irradiated by a point source of gamma rays continuously since 1973, and measurements have been made yearly to study the change in boreal forest canopy cover along the radiation gradient. After 10 years of chronic irradiation, a zone of total tree death has resulted from mean dose rates between 25 and 62 mGy h−1. Tree canopy cover was reduced at mean dose rates exceeding ~ 4.5 mGy h−1 and the largest reduction occurred in the first 2 years of irradiation. The temporal responses of seven woody species to gamma radiation are presented. Bebb's willow, trembling aspen, speckled alder, and paper birch were less sensitive to radiation than black spruce, balsam fir, and jack pine. The results confirm that gymnosperms are more sensitive to gamma rays than angiosperms.

scholarly journals Identification of Micro-Scale Landforms of Landslides Using Precise Digital Elevation Models

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 117 ◽  
Author(s):  
František Chudý ◽  
Martina Slámová ◽  
Julián Tomaštík ◽  
Roberta Prokešová ◽  
Martin Mokroš
Keyword(s):  
Large Scale ◽  
Forest Canopy ◽  
Digital Elevation Models ◽  
Canopy Cover ◽  
Point Clouds ◽  
Tree Canopy ◽  
Automatic Identification ◽  
Optical Parameters ◽  
Micro Scale ◽  
Digital Elevation

An active gully-related landslide system is located in a deep valley under forest canopy cover. Generally, point clouds from forested areas have a lack of data connectivity, and optical parameters of scanning cameras lead to different densities of point clouds. Data noise or systematic errors (missing data) make the automatic identification of landforms under tree canopy problematic or impossible. We processed, analyzed, and interpreted data from a large-scale landslide survey, which were acquired by the light detection and ranging (LiDAR) technology, remotely piloted aircraft system (RPAS), and close-range photogrammetry (CRP) using the ‘Structure-from-Motion’ (SfM) method. LAStools is a highly efficient Geographic Information System (GIS) tool for point clouds pre-processing and creating precise digital elevation models (DEMs). The main landslide body and its landforms indicating the landslide activity were detected and delineated in DEM-derivatives. Identification of micro-scale landforms in precise DEMs at large scales allow the monitoring and the assessment of these active parts of landslides that are invisible in digital terrain models at smaller scales (obtained from aerial LiDAR or from RPAS) due to insufficient data density or the presence of many data gaps.

scholarly journals ASSESSING URBAN FOREST CANOPY COVER IN GREAT PLAIN CONSERVATION AREA (DÜZCE CITY, TURKEY) BETWEEN 1984 AND 2015

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 539-544
Author(s):  
Z. Uçar ◽  
R. Eker ◽  
A. Aydin
Keyword(s):  
Ecosystem Services ◽  
Forest Canopy ◽  
Urban Forest ◽  
Canopy Cover ◽  
Tree Canopy ◽  
Urban Trees ◽  
Conservation Area ◽  
Urban Tree ◽  
Tree Canopy Cover ◽  
Urban Tree Canopy

Abstract. Urban trees and forests are essential components of the urban environment. They can provide numerous ecosystem services and goods, including but not limited to recreational opportunities and aesthetic values, removal of air pollutants, improving air and water quality, providing shade and cooling effect, reducing energy use, and storage of atmospheric CO2. However, urban trees and forests have been in danger of being lost by dense housing resulting from population growth in the cities since the 1950s, leading to increased local temperature, pollution level, and flooding risk. Thus, determining the status of urban trees and forests is necessary for comprehensive understanding and quantifying the ecosystem services and goods. Tree canopy cover is a relatively quick, easy to obtain, and cost-effective urban forestry metric broadly used to estimate ecosystem services and goods of the urban forest. This study aimed to determine urban forest canopy cover areas and monitor the changes between 1984–2015 for the Great Plain Conservation area (GPCA) that has been declared as a conservation Area (GPCA) in 2017, located on the border of Düzce City (Western Black Sea Region of Turkey). Although GPCA is a conservation area for agricultural purposes, it consists of the city center with 250,000 population and most settlement areas. A random point sampling approach, the most common sampling approach, was applied to estimate urban tree canopy cover and their changes over time from historical aerial imageries. Tree canopy cover ranged from 16.0% to 27.4% within the study period. The changes in urban canopy cover between 1984–1999 and 1999–2015 were statistically significant, while there was no statistical difference compared to the changes in tree canopy cover between 1984–2015. The result of the study suggested that an accurate estimate of urban tree canopy cover and monitoring long-term canopy cover changes are essential to determine the current situation and the trends for the future. It will help city planners and policymakers in decision-making processes for the future of urban areas.

scholarly journals Estimating tree canopy cover using harmonic regression coefficients derived from multitemporal Landsat data

2020 ◽  
Vol 86 ◽  
pp. 101985 ◽  
Author(s):  
Jill M. Derwin ◽  
Valerie A. Thomas ◽  
Randolph H. Wynne ◽  
John W. Coulston ◽  
Greg C. Liknes ◽  
...  
Keyword(s):  
Canopy Cover ◽  
Tree Canopy ◽  
Regression Coefficients ◽  
Landsat Data ◽  
Tree Canopy Cover ◽  
Harmonic Regression

Laboratory experiments to estimate interception of infrared radiation by tree canopies

2016 ◽  
Vol 25 (9) ◽  
pp. 1009 ◽  
Author(s):  
Bill J. Mathews ◽  
Eva K. Strand ◽  
Alistair M. S. Smith ◽  
Andrew T. Hudak ◽  
B. Dickinson ◽  
...  
Keyword(s):  
Laboratory Experiments ◽  
Forest Canopy ◽  
Detection Threshold ◽  
Canopy Cover ◽  
Tree Canopy ◽  
Radiative Energy ◽  
Prescribed Fires ◽  
Total Biomass ◽  
Tree Canopy Cover ◽  
Radiative Power

Estimates of biomass-burning in wildfires or prescribed fires are needed to account for the production of trace gases and aerosols that enter the atmosphere during combustion. Research has demonstrated that the biomass consumption rate is linearly related to fire radiative power (FRP), and that total biomass consumed is linearly related to fire radiative energy (FRE). Measurement of these is biased by certain characteristics of a forest canopy, such as foliar moisture content and tree canopy cover. Laboratory experiments were conducted to assess the influence of canopy cover on the FRP observed from an overhead sensor (e.g. an aircraft or satellite). A range of canopy cover from 0 to 90% and two classes of canopy (non-transpiring living and desiccated branches) were used in the experiments. Experiments suggest that in cases of complete or nearly complete canopy closure, fires obscured by the canopy may be below the detection threshold of above-canopy FRP sensors. Results from this research will reduce uncertainties in estimates of biomass consumption in surface fires burning under forest canopies.

Estimation of urban tree canopy cover using random point sampling and remote sensing methods

2016 ◽  
Vol 20 ◽  
pp. 160-171 ◽  
Author(s):  
Ebadat G. Parmehr ◽  
Marco Amati ◽  
Elizabeth J. Taylor ◽  
Stephen J. Livesley
Keyword(s):  
Remote Sensing ◽  
Canopy Cover ◽  
Tree Canopy ◽  
Random Point ◽  
Point Sampling ◽  
Urban Tree ◽  
Tree Canopy Cover ◽  
Urban Tree Canopy ◽  
Remote Sensing Methods

scholarly journals Assessing the Accuracy of Remotely Sensed Forest Maps for Nepal

2020 ◽  
Vol 20 (1) ◽  
pp. 19-29
Author(s):  
Menaka Hamal ◽  
Rajesh Bahadur Thapa
Keyword(s):  
Land Cover ◽  
Accuracy Assessment ◽  
Canopy Cover ◽  
Remotely Sensed ◽  
Google Earth ◽  
Tree Canopy ◽  
Kappa Statistics ◽  
Efficiency Coefficient ◽  
Tree Canopy Cover ◽  
Level Of Agreement

The accuracy assessment is vital to validate the remotely sensed thematic output before being front to the users. The statistical accuracy measures and modeling have been using widely for the accuracy assessment of the remote sensing product. This study uses six open-access land cover products - Land Cover of Nepal 2010, GlobeLand30, Treecover2010, Global PALSAR-2 forest/Non-Forest, Tree Canopy Cover (TCC), and ESACCI Land Cover 2010, to find out the most reliable forest product for Nepal. The forest/non-forest data were extracted from each product. The stratified random sampling was used to create test points and verified ground truth in Google Earth (GE) by visual interpretation. The overall accuracy (OA), producer’s accuracy (PA), user’s accuracy (UA), Kappa statistics, and the Nash-Sutcliffe model efficiency coefficient (NSE) were measured for each forest/non-forest map. The OA and UA were found to be highest by 94%; the Kappa statistics showed an 89% level of agreement and NSE showed 77 % performance level for Nepal Land Cover 2010 which is the highest among six datasets. Whereas ESACCI land Cover 2010 was found to be the least performer - OA and UA are 53% and 66% respectively, Kappa shows a 53% level of agreement and NSE shows 4%.. The ESACCI land Cover 2010 was found to be the highest coverage whereas Tree Canopy Cover (TCC) has the least one for each province. This study gives the methodological insight to compare remotely sensed datasets and help the user in the selection of the most reliable open-source forest map for Nepal.

scholarly journals Modified Soil-Adjusted Vegetation Index In Multispectral Remote Sensing Data for Estimating Tree Canopy Cover Density at Rubber Plantation

2018 ◽  
Vol 3 (1) ◽  
pp. 15 ◽  
Author(s):  
Wenang Anurogo ◽  
Muhammad Zainuddin Lubis ◽  
Mir'atul Khusna Mufida
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Canopy Cover ◽  
Remote Sensing Data ◽  
Research Area ◽  
Tree Canopy ◽  
Vegetation Canopy ◽  
Sensing Data ◽  
Tree Canopy Cover ◽  
Cover Density

Forest inventories such as tree canopy density information require a long time and high costs, especially on extensive forest coverage. Remote sensing technology that directly captures the surface vegetation character with extensive recording coverage can be used as an alternative to carrying out such inventory activities. This research aims to determine the level of vegetation canopy cover density on rubber plants that became the location of the research and know the accuracy of the resulting data. The method used in this research is a combination of remote sensing image interpretation, geographic information system, and field measurement. Information retrieval from remote sensing data is done by using ASTER data imagery. This stage includes three parts, namely: pre-field stage, field stage, and post-field stage. The pre-field stage includes the collection of data to be used (including literature studies related to the theme of the study), image processing (geometric and radiometric correction), cropping, masking, land cover classification, vegetation index transformation, and sample determination. The final result of data processing showed that the density of the vegetation canopy in the research area ranged between 7.31 – 12.952 cm / m2 in each grade of vegetation density. These values indicate the range of low-class vegetation canopy cover density to high-class vegetation canopy cover density in the research area. In this research error rate or root mean square error obtained from the calculation of canopy cover density is equal to 1.89.

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