scholarly journals Estimation of boreal forest canopy cover with ground measurements, statistical models and remote sensing

2011 ◽  
Vol 2011 (115) ◽  
Author(s):  
Lauri Korhonen
Keyword(s):  
Remote Sensing ◽  
Boreal Forest ◽  
Statistical Models ◽  
Forest Canopy ◽  
Canopy Cover

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 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.

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 Assessing Urban Forest Canopy Cover Using Airborne or Satellite Imagery

2008 ◽  
Vol 34 (6) ◽  
pp. 334-340
Author(s):  
Jeffrey Walton ◽  
David Nowak ◽  
Eric Greenfield
Keyword(s):  
Remote Sensing ◽  
New York ◽  
Forest Canopy ◽  
Urban Forest ◽  
Canopy Cover ◽  
Classification Methods ◽  
Remote Sensing Techniques ◽  
Many Sources ◽  
Forest Managers

With the availability of many sources of imagery and various digital classification techniques, assessing urban forest canopy cover is readily accessible to most urban forest managers. Understanding the capability and limitations of various types of imagery and classification methods is essential to interpreting canopy cover values. An overview of several remote sensing techniques used to assess urban forest canopy cover is presented. A case study comparing canopy cover percentages for Syracuse, New York, U.S. interprets the multiple values developed using different methods. Most methods produce relatively similar results, but the estimate based on the National Land Cover Database is much lower.

scholarly journals Improvement of Forest Canopy Density Mapping of Spare Forests Using a Novel RS-GIS Based Classification Method

2021 ◽  
Author(s):  
Mohammad Hassan Naseri ◽  
Shaban Shataee
Keyword(s):  
Remote Sensing ◽  
High Performance ◽  
Forest Canopy ◽  
Direct Method ◽  
Accuracy Assessment ◽  
Canopy Cover ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Density Mapping ◽  
Cover Density

Abstract Background: Accurate mapping and monitoring canopy cover using remote sensing data as an alternative way for field surveys are very important for forest managers, particularly in the spare and low dense forests. Due to being area-based of canopy cover density and mixing spectral responses of tree crowns and soil in the thin and semi-dense forests, finding the high-performance method of classification is a challenge particularly on high-resolution imagery. In this study, we compared produced maps of canopy cover using direct remote sensing and indirect (RS-GIS-based) methods in two forest sites on the Quickbird and WorldView-2 images using the Artificial Neural Network (ANN) algorithm. Also, the optimal plot area was examined by different plot areas.Results: In the direct method and based on the obtained results, in the Dashte Barm using Quickbird image, the best classification was for plots of 7500 m2 with an overall accuracy of 56.57% and kappa coefficient of 0.32. In the Ilam site and on the WorldView-2 image, the best result is obtained by the plots of 5,000 m2 area with an overall accuracy of 45.71% and the kappa coefficient of 0.263. The results of accuracy assessment of maps of indirect method in the Dashte Barm site for grids with different areas showed that the best classifications obtained from sample plot areas of 10000 m2 with overall accuracy of 82.69% and Kappa coefficient of 0.744; but in the Ilam sites the best result was obtained using sample area of 1000 m2 with overall accuracy of 74.27% and the Kappa coefficient of 0.690. Conclusions: The results exposed that use of the RS-GIS based method could considerably improve the results compare to direct classification. Also, the results showed concerning the conditions of canopy cover density of forest stands, plots with different areas can be used to map of forest canopy cover density; however, for direct classification the use of plots with areas of 5000 m2 and more are suitable in sparse forests. For RS-GIS based method, the plot areas of 1000 m2 are optimal due to time and cost saving.

scholarly journals Monitoring Mediterranean Oak Decline in a Peri-Urban Protected Area Using the NDVI and Sentinel-2 Images: The Case Study of Castelporziano State Natural Reserve

2018 ◽  
Vol 10 (9) ◽  
pp. 3308 ◽  
Author(s):  
Fabio Recanatesi ◽  
Chiara Giuliani ◽  
Maria Ripa
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Urban Forest ◽  
Forest Health ◽  
Canopy Cover ◽  
High Rate ◽  
Health Conditions ◽  
The Mediterranean ◽  
Sentinel 2

Climate change and human activities in particular are important causes of the possible variations in Mediterranean basin forest health conditions. Over the last decades, deciduous oak-forest mortality has been a recurrent problem in central and southern Italy. Despite the perception of increasingly visible damage in oak forests in drought sites, the role of various environmental factors in their decline is not completely clear. Among the modern methods of monitoring terrestrial ecosystems, remote sensing is of prime importance thanks to its ability to provide synoptic information on large areas with a high frequency of acquisition. This paper reports the preliminary results regarding a replicable and low cost monitoring tool planned to quantify forest health conditions based on the application of the Normalized Difference Vegetation Index (NDVI), using the diachronic images provided by the Sentinel-2 satellite. The study area is represented by a peri-urban forest of natural Mediterranean deciduous oaks, characterized by a high variability in the composition of the species and in the silvicultural structures. In order to monitor the health conditions of a specific forest canopy cover with remote sensing data, it is necessary to classify the forest canopy cover in advance to separate it from other species and from the Mediterranean scrub. This is due to the spatial distribution of vegetation and the high rate of biodiversity in the Mediterranean natural environment. To achieve this, Light Detection and Ranging (LiDAR) data, forest management data and field sampling data were analyzed. The main results of this research show a widespread decline in oak health conditions over the observed period (2015–2017). Specifically, for the studied area, thanks to the specific localization of the oak canopy cover, we detected a high potential concerning the Sentinel-2 data application in monitoring forest health conditions by NDVI application.

scholarly journals Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta

2019 ◽  
Vol 11 (16) ◽  
pp. 1919 ◽  
Author(s):  
Annette Dietmaier ◽  
Gregory J. McDermid ◽  
Mir Mustafizur Rahman ◽  
Julia Linke ◽  
Ralf Ludwig
Keyword(s):  
Boreal Forest ◽  
Forest Structure ◽  
Forest Canopy ◽  
Canopy Structure ◽  
Three Dimensional ◽  
Canopy Cover ◽  
Aerial Photogrammetry ◽  
Northern Alberta ◽  
Height Model ◽  
Economical Alternative

Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87% overall accuracy), followed by the hybrid and DAP models (47% and 46%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9–53%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP’s sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology’s capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.

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