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.

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 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 The Eye of the Storm: Extreme Weather Events and Sustainable Urban Forest Management

2018 ◽  
Vol 14 ◽  
Author(s):  
Kendra Marshman
Keyword(s):  
Forest Canopy ◽  
Urban Forest ◽  
Global Climate ◽  
Urban Canopy ◽  
Canopy Cover ◽  
Extreme Weather ◽  
Urban Trees ◽  
Extreme Weather Events ◽  
Ice Storms ◽  
Weather Events

More people live in cities today than ever before. One indicator of a sustainable urban environment is a full canopy cover. Urban residents value trees for the benefits of improved air quality, provision of shade, and aesthetic purposes, among others. Although urban trees are greatly valued, they are up against environmental challenges. Global climate change threatens urban forests because of the accompanying increase in frequency and intensity of extreme weather events. Hurricanes, intense precipitation, windstorms, and ice storms, are included. In Halifax (2003) Hurricane Juan negatively affected the urban forest canopy and some areas have not fully recovered. Similarly, in Vancouver’s Stanley Park (2006 & 2007) an extreme windstorm hit the urban canopy. How can urban forest planners adapt the urban forest to become more resilient in the face of such events?

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 Tree Species Classification of Forest Stands Using Multisource Remote Sensing Data

2021 ◽  
Vol 13 (1) ◽  
pp. 144
Author(s):  
Haoming Wan ◽  
Yunwei Tang ◽  
Linhai Jing ◽  
Hui Li ◽  
Fang Qiu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Spatial Resolution ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Aerial Images ◽  
Lidar Data ◽  
Forest Stands ◽  
Time Series Images

The spatial distribution of forest stands is one of the fundamental properties of forests. Timely and accurately obtained stand distribution can help people better understand, manage, and utilize forests. The development of remote sensing technology has made it possible to map the distribution of tree species in a timely and accurate manner. At present, a large amount of remote sensing data have been accumulated, including high-spatial-resolution images, time-series images, light detection and ranging (LiDAR) data, etc. However, these data have not been fully utilized. To accurately identify the tree species of forest stands, various and complementary data need to be synthesized for classification. A curve matching based method called the fusion of spectral image and point data (FSP) algorithm was developed to fuse high-spatial-resolution images, time-series images, and LiDAR data for forest stand classification. In this method, the multispectral Sentinel-2 image and high-spatial-resolution aerial images were first fused. Then, the fused images were segmented to derive forest stands, which are the basic unit for classification. To extract features from forest stands, the gray histogram of each band was extracted from the aerial images. The average reflectance in each stand was calculated and stacked for the time-series images. The profile curve of forest structure was generated from the LiDAR data. Finally, the features of forest stands were compared with training samples using curve matching methods to derive the tree species. The developed method was tested in a forest farm to classify 11 tree species. The average accuracy of the FSP method for ten performances was between 0.900 and 0.913, and the maximum accuracy was 0.945. The experiments demonstrate that the FSP method is more accurate and stable than traditional machine learning classification methods.

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.

Urban Trees and Water Use in Arid Climates: Insights from an Integrated Bioeconomic-Health Model

2018 ◽  
Vol 04 (04) ◽  
pp. 1850022 ◽  
Author(s):  
Benjamin A. Jones ◽  
John Fleck
Keyword(s):  
Ecosystem Services ◽  
Water Use ◽  
Forest Canopy ◽  
Urban Forest ◽  
Economic Value ◽  
Urban Trees ◽  
Tree Cover ◽  
Arid Environments ◽  
Value Of Water ◽  
Health Model

Managing outdoor water use while maintaining urban tree cover is a key challenge for water managers in arid climates. Urban trees generate flows of ecosystem services in arid areas, but also require significant amounts of irrigation. In this paper, a bioeconomic-health model of trees and water use is developed to investigate management of an urban forest canopy when irrigation is costly, water has economic value, and trees provide ecosystem services. The optimal tree irrigation decision is illustrated for Albuquerque, New Mexico, an arid Southwest US city. Using a range of monetary values for water, we find that the tree irrigation decision is sensitive to the value selected. Urban deforestation is optimal when the value of water is sufficiently high, or alternatively starts low, but grows to cross a specific threshold. If, however, the value of water is sufficiently low or if the value of tree cover rises over time, then deforestation is not optimal. The threshold value of water where the switch is made between zero and partial deforestation is well within previously identified ranges on actual water values. This model can be applied generally to study the tradeoffs between urban trees and water use in arid environments.

scholarly journals Riparian Bird Community Structure in Portland, Oregon: Habitat, Urbanization, and Spatial Scale Patterns

The Condor ◽  
2003 ◽  
Vol 105 (2) ◽  
pp. 288-302 ◽  
Author(s):  
Lori A. Hennings ◽  
W. Daniel Edge
Keyword(s):  
Native Species ◽  
Forest Canopy ◽  
Urban Canopy ◽  
Canopy Cover ◽  
Bird Community ◽  
Metropolitan Region ◽  
Road Density ◽  
Breeding Bird ◽  
Bird Abundance ◽  
Neotropical Migrant

Abstract In 1999, we surveyed breeding bird and plant communities along 54 streams in the Portland, Oregon, metropolitan region to link bird community metrics with structural and spatial characteristics of urban riparian areas. Canonical correspondence analysis produced two explanatory axes relating to vegetation and road density. Total and non-native bird abundance was higher in narrow forests. Native bird abundance was greater in narrow forests surrounded by undeveloped lands; native species richness and diversity were greater in less-developed areas. Native resident and short-distance-migrant abundance was higher in narrow forests, and diversity was positively associated with developed lands. Neotropical migrant abundance, richness, and diversity were greater in open-canopied areas with fewer roads. We examined spatial relationships by regressing bird variables on satellite-derived forest canopy cover, area of undeveloped lands, and street density in a series of 50-m buffers within a 500-m radius around study sites. Non-native bird abundance decreased with increasing canopy cover within 450 m, but most other relationships were strongest at smaller scales (50–100 m). Our results suggest that increasing urban canopy cover is the most valuable land management action for conserving native breeding birds. A hierarchical scheme for Neotropical migrant conservation might include increasing forest canopy within 450 m of streams to control non-native species and cowbirds; reducing street density within a 100-m radius of streams; and conserving or planting onsite native trees and shrubs. Estructura de Comunidades Riparias de Aves en Portland, Oregon: Hábitat, Urbanización y Patrones de Escala Espacial Resumen. Censamos las comunidades de aves reproductivas y plantas a lo largo de 54 arroyos en el área metropolitana de Portland, Oregon en 1999 para conectar medidas de comunidades de aves con características estructurales y espaciales de zonas riparias urbanas. Análisis de correspondencia canónica produjeron dos ejes explicativos relacionados con la vegetación y la densidad de carreteras. La abundancia total de aves y la de aves no nativas fueron mayores en bosques estrechos. La abundancia de aves nativas fue mayor en bosques estrechos rodeados por terrenos rurales y la riqueza y diversidad de especies fueron mayores en áreas menos desarrolladas. La abundancia de residentes nativas y migratorias de corta distancia fue mayor en bosques estrechos y su diversidad estuvo asociada positivamente con terrenos desarrollados. La abundancia, riqueza y diversidad de las migratorias neotropicales fueron mayores en áreas de dosel abierto y con pocas carreteras. Examinamos las relaciones espaciales mediante regresiones entre variables de aves y la cobertura del dosel derivada de imágenes satelitales, el área de terrenos sin desarrollar y la densidad de calles en una serie de áreas de 50 m de ancho en un radio de 500 m alrededor de los sitios de estudio. La abundancia de aves no nativas disminuyó con aumentos en la cobertura del dosel hasta 450 m, pero la mayoría de las demás relaciones fueron más fuertes a escalas menores (50–100 m). Nuestros resultados sugieren que el incremento de la cobertura del dosel en áreas urbanas es la estrategia de manejo más valiosa para conservar las aves nativas que se reproducen en el área. Un esquema jerárquico para la conservación de las migratorias neotropicales podría incluir aumentar la cobertura de bosque a menos de 450 m de los arroyos para controlar a las especies no nativas y a los Molothrus, reducir la densidad de calles dentro de un radio de 100 m alrededor de los arroyos y conservar o plantar árboles y arbustos nativos.

scholarly journals Pattern recognition of forest canopy using the airborne hyperspectral data and multi-bands high spatial resolution satellite sensor worldview-2 data. A results comparison and accuracy estimation

2019 ◽  
pp. 89-102
Author(s):  
V. V. Kozoderov ◽  
V. D. Egorov
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Spatial Resolution ◽  
Forest Canopy ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Automatic Data ◽  
Sensing Data ◽  
Satellite Sensor

Pattern recognition of forest surface from remote sensing data: using the airborne hyperspectral data and using multi-bands high spatial resolution satellite sensor WorldView‑2 data are investigated. The early proposed method and standard QDA method for calculations were used. A comparison of calculations results were conducted. A recognition calculation accuracy range for airborne and satellite remote sensing data for three forest surface fragments for different created data bases for recognition system has been assessed. Some opportunities of automatic data preparing of created system were displayed. Some special features of pattern recognition of forest surfaces from hyperspectral airborne data and from multi-bands high spatial resolution satellite data were discussed.

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