Estimation of urban woody vegetation cover using multispectral imagery and LiDAR

Ucar, Zennure; Bettinger, Pete; Merry, Krista; Akbulut, Ramazan; Siry, Jacek P.

doi:10.1016/j.ufug.2017.12.001

Cited by 31 publications

(17 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is available at the spatial resolution of 0.6 to 2 meters with very low cloud coverage and consists of repeat images during the growing season with two or three year cycles for more than 15 years [1]. It has been a unique choice for a variety of geospatial mapping applications, such as analysis of land cover and land use change [2][3][4], evaluation of ecosystem services [5][6][7], monitoring of forest health [8][9][10][11], and assessment of urban green infrastructure [12,13]. The NAIP imagery will likely continue to be the one of the best data sources for many research and operational efforts that need high-resolution multispectral imagery for feature extraction, change detection, or collection of ground truth for validate coarse-resolution satellite products.…”

Section: Introductionmentioning

confidence: 99%

“…This is important to the calculations of many multispectral indices, such as the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), that are very sensitive to atmospheric effects. However, many studies directly applied NAIP without correction for spectral analysis such as NDVI calculation, land cover classification, and vegetation cover estimation [3,4,6,8,9,[11][12][13][14]17]. This is mainly due to a lack of easy access to radiometric response data for the sensors used and the lack of methods for retrieving surface reflectance from NAIP DN values [1].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

2019

View full text Add to dashboard Cite

The utilization of high-resolution aerial imagery such as the National Agriculture Imagery Program (NAIP) data is often hampered by a lack of methods for retrieving surface reflectance from digital numbers. This study developed a new relative radiometric correction method to retrieve 1 m surface reflectance from NAIP imagery. The advantage of this method lies in the adaptive identification of pseudoinvariant (PIV) pixels from a time series of Landsat images that can fully characterize the temporally spectral variations of land surface. The identified PIV pixels allow for an effective conversion of digital numbers to surface reflectance, as demonstrated through the validation at 150 sites across the contiguous United States. The results show substantial improvement in the agreement of NAIP-derived normalized difference vegetation index (NDVI) values with Landsat-derived NDVI reference. Across the sites, root mean square error and mean absolute error were reduced from 0.37 ± 0.14 to 0.08 ± 0.07 and from 0.91 ± 0.64 to 0.18 ± 0.52, respectively. Over 70% PIV pixels on average were derived from vegetated areas, while water and developed areas together contributed 27% of the PIV pixels. As the NAIP program is continuing to generate new images across the country, the advantages of its high spatial resolution, national coverage, long time series, and regular revisits will make it an increasingly crucial data source for a variety of research and management applications. The proposed method could benefit many agricultural, hydrological, and urban studies that rely on NAIP imagery to quantify land surface patterns and dynamics. It could also be applied to improve the preprocessing of high-resolution aerial imagery in other countries.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

2019

View full text Add to dashboard Cite

show abstract

“…The PA measures omission error, and represents how well a land cover class can be assigned to the landscape. [45,46]. CM is an n×n matrix, the OA, UA, PA, and kappa coefficient can be defined as:OA=∑k=1npkk/p, UA=pii/pu, PA=pjj/pp, kappa coefficient=p∑i=1npii−∑i=1n(pupu)p2−∑i=1n(pupu) where n is the total of classes, p is the total of samples, row i represents classified samples, column j represents practical samples, p…”

Section: Methodsmentioning

confidence: 99%

Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods

Zhong

Sun

et al. 2019

Sensors

View full text Add to dashboard Cite

It is valuable to study the land use/land cover (LULC) classification for suburbs. The fusion of Light Detection and Ranging (LiDAR) data and aerial imagery is often regarded as an effective method for the LULC classification; however, more in-depth analysis would be required to explore effective information for enhancing the suburban LULC classification. In this study, first, both aerial imageries and point clouds were simultaneously collected. Then, LiDAR-derived models, i.e., normalized digital surface model (nDSM) and surface intensity model (IM), were generated from the elevation and intensity of point clouds. Further, considering the surface characteristics of ground objects in suburb, we proposed a new LiDAR-derived model, namely surface roughness model (RM), to reveal the degree of surface fluctuations. Additionally, various combinations of aerial imageries and the LiDAR-derived data were used to analyze the effects of multi-variable fusion under different scenarios and optimize the multi-variable integration for suburban LULC classification. The mean decrease impurity method was used to identify the importance of variables; three machine learning classifiers, i.e., random forest (RF), k-nearest neighbor (KNN) and artificial neural network (ANN) were adopted in various scenarios. The results were as follows. The fusion of aerial imagery and all the LiDAR-derived models, i.e., nDSM, RM and IM, with RF classifier performs best in the suburban LULC classification (overall accuracy = 84.75%, kappa coefficient = 0.80). Variable importance analysis shows that nDSM has the highest variable importance proportion (VIP) value, followed by RM, IM, and spectral information, indicating the feasibility of this proposed LiDAR-derived model-RM. This research presents effective methods relating to the application of aerial imagery and LiDAR-derived model for the complex suburban surface scenarios.

show abstract

“…Urban forestry and urban greening areas offers essential biophysical and socioeconomic benefits to both human and the cities, including but not limited to reduce energy use, facilitate cooling effects, improve water and air quality, and improve biodiversity and wildlife habitat. Urban vegetation also provides recreational opportunities and aesthetic values that improve health and the overall enjoyment and increase the value of neighborhoods (Nowak et al, 2010;Richardson and Moskal, 2014;Anguluri and Narayanan, 2017;Mundoli et al, 2017;Ucar et al, 2018). There are extensive research and documents on the importance of the urban forestry and greening area and its benefits to human wellbeing, and contribution to the economic value of the city (Anguluri and Narayanan 2017).…”

Section: Importance Of the Urban Forestry And Urban Greening In Smartmentioning

confidence: 99%

Towards Green Smart Cities: Importance of Urban Forestry and Urban Vegetation

Ucar

Akay

Bilici

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. More than half of the total world’s population lives in urban areas, and it is expected that 66% of all them will live in urban areas by 2050. The population growth and continuing urbanization in the world cause many social, economic, technical, and organizational problems related to transportation, businesses, communication networks, services, and utilities that can risk the cities’ economic and environmental sustainability. Recently, a smart city concept has been developed to provide a solution to improve citizens’ quality of life in urban areas with the adoption of smart and digital technologies and infrastructure for energy, water, mobility, buildings, and government. The smart city concept considers “zero vision” that refers to the use of smart city technologies, Information and Communication Technologies (ICTs) and Internet of Things (IoT) tools, to minimize negative impacts (i.e., zero traffic accident, zero CO2 emission, zero waste, zero crime) in the cities. However, the research in this zero-vision approach mainly focused on transportation and energy. Urban forestry and urban vegetated areas in the cities inherently provide benefits such as reducing air pollution, urban heat island effects, and flood risk and increasing the water quality, aesthetic value, and value of the property that improve citizens’ quality of life. The smart city concept switched towards to sustainable smart city concept that takes into account the services provided by urban forestry and urban vegetation. In this study, the shifts in the smart city concept towards the sustainable smart city, the role of the urban forestry and urban vegetation in this shift was presented. Also, ICTs and IoT tools specifically designed for monitoring, assessing, and managing urban forestry and urban vegetation was reviewed.

show abstract

Estimation of urban woody vegetation cover using multispectral imagery and LiDAR

Cited by 31 publications

References 33 publications

A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

Integrated Airborne LiDAR Data and Imagery for Suburban Land Cover Classification Using Machine Learning Methods

Towards Green Smart Cities: Importance of Urban Forestry and Urban Vegetation

Contact Info

Product

Resources

About