A new index to differentiate tree and grass based on high resolution image and object-based methods

Abstract Urban trees and grass have different ecological functions and services. Remote sensing provides a feasible way of quantifying urban vegetative cover and distribution at large scale. Most previous studies have used supervised classification based on high resolution images to map urban trees and grass. However, due to the lack of specialized features for distinguishing coarse and fine vegetation, the classification accuracy of urban trees and grass is consistently low. Although adding 3D topographical information can improve accuracy, such data has limited availability. This paper developed a tree-grass differentiation index (TGDI) to facilitate the fast and effective classification of urban trees and grass. We examined the performance of the new index by applying it to different classification methods. We compared the classification of Method 1: supervised classification without TGDI; Method 2: supervised classification with TGDI; and Method 3: rule-based classification with TGDI. The results showed that the overall accuracy of Method 1, Method 2, and Method 3 were, 84 %, 88 %, and 90.5 %, respectively. Using the new index can improve the classification of urban trees and grass regardless if TGDI is used alone for rule-based classification or added as a feature for supervised classification. The main advantage of using TGDI is to reduce the misclassification of sunlit portions of trees into grass. The producer accuracy of tree and the user accuracy of grass can be improved by more than 10 % when TGDI is applied to supervised classification. This study synthesized texture and spectral features, which enhances the traditional approach of index construction based on spectral features alone, and without the requirement of detailed 3D surface data. The results suggest a novel way forward for developing indexes that can yield improved accuracies and expand the utility of remote sensing for illuminating patterns of ecological structure and function in urban environments.

[1]  Jingfeng Xiao,et al.  A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA , 2005 .

[2]  Dongmei Chen,et al.  Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification , 2019, Remote. Sens..

[3]  K. Doick,et al.  The role of one large greenspace in mitigating London's nocturnal urban heat island. , 2014, The Science of the total environment.

[4]  L. Mariani,et al.  Climatological analysis of the mitigating effect of vegetation on the urban heat island of Milan, Italy. , 2016, The Science of the total environment.

[5]  Bo Du,et al.  Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art , 2016, IEEE Geoscience and Remote Sensing Magazine.

[6]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[7]  Anne Puissant,et al.  The utility of texture analysis to improve per‐pixel classification for high to very high spatial resolution imagery , 2005 .

[8]  Emanuele Frontoni,et al.  Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery , 2011, Int. J. Geogr. Inf. Sci..

[9]  Weiqi Zhou,et al.  An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data , 2013, IEEE Geoscience and Remote Sensing Letters.

[10]  James D. Wickham,et al.  Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD). , 2017, Remote sensing of environment.

[11]  Weiqi Zhou,et al.  Understanding the dynamic of greenspace in the urbanized area of Beijing based on high resolution satellite images , 2015 .

[12]  R. Lunetta,et al.  Land-cover change detection using multi-temporal MODIS NDVI data , 2006 .

[13]  S. Pickett,et al.  Shifting concepts of urban spatial heterogeneity and their implications for sustainability , 2016, Landscape Ecology.

[14]  M. Cadenasso,et al.  Effects of the spatial configuration of trees on urban heat mitigation: A comparative study , 2017 .

[15]  Yifang Ban,et al.  WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices , 2019, Remote. Sens..

[16]  A. R. Ennos,et al.  The effect of street trees and amenity grass on urban surface water runoff in Manchester, UK , 2013 .

[17]  A. R. Ennos,et al.  The effect of tree shade and grass on surface and globe temperatures in an urban area , 2012 .

[18]  Xiaoguang Jiang,et al.  Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image , 2012 .

[19]  J. Townshend,et al.  NDVI-derived land cover classifications at a global scale , 1994 .

[20]  A. Troy,et al.  An object‐oriented approach for analysing and characterizing urban landscape at the parcel level , 2008 .

[21]  Zhiming Zhang,et al.  Integration of Satellite Imagery, Topography and Human Disturbance Factors Based on Canonical Correspondence Analysis Ordination for Mountain Vegetation Mapping: A Case Study in Yunnan, China , 2014, Remote. Sens..

[22]  Xiaoma Li,et al.  Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China , 2012, Landscape Ecology.

[23]  Hukum Singh,et al.  A comparative study of NDBI, NDISI and NDII for extraction of urban impervious surface of Dehradun [Uttarakhand, India] using Landsat 8 imagery , 2016, 2016 International Conference on Emerging Trends in Communication Technologies (ETCT).

[24]  Xueming Li,et al.  Spatiotemporal variation characteristics of green space ecosystem service value at urban fringes: A case study on Ganjingzi District in Dalian, China. , 2018, The Science of the total environment.

[25]  Y. Ouma,et al.  On the optimization and selection of wavelet texture for feature extraction from high‐resolution satellite imagery with application towards urban‐tree delineation , 2006 .

[26]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[27]  C. Steele,et al.  Mapping land cover in urban residential landscapes using very high spatial resolution aerial photographs , 2012 .

[28]  Weiqi Zhou,et al.  Quantifying spatiotemporal pattern of urban greenspace: new insights from high resolution data , 2015, Landscape Ecology.

[29]  Paul F. Hopkins,et al.  Urban cover mapping using digital, high-spatial resolution aerial imagery , 2001, Urban Ecosystems.

[30]  Weiqi Zhou,et al.  Mapping vegetation functional types in urban areas with WorldView-2 imagery: integrating object-based classification with phenology. , 2018 .

[31]  Roland J. Leigh,et al.  Modelling the effectiveness of urban trees and grass on PM 2.5 reduction via dispersion and deposition at a city scale , 2016 .

[32]  Ruiliang Pu,et al.  Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery , 2011 .

[33]  A. Rango,et al.  Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico , 2004 .

[34]  Zhaoyu Li,et al.  Urban land cover classification using aerial LiDAR and CCD images , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[35]  Xiaoma Li,et al.  Relationship between land surface temperature and spatial pattern of greenspace: What are the effects of spatial resolution? , 2013 .

[36]  Bo-Cai Gao,et al.  Normalized difference water index for remote sensing of vegetation liquid water from space , 1995, Defense, Security, and Sensing.

[37]  Jing Wang,et al.  Quantifying and characterizing the dynamics of urban greenspace at the patch level: A new approach using object-based image analysis , 2018 .

[38]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[39]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[40]  Guihua Zhao,et al.  OBJECT BASED IMAGE ANALYSIS COMBINING HIGH SPATIAL RESOLUTION IMAGERY AND LASER POINT CLOUDS FOR URBAN LAND COVER , 2016 .

[41]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

[42]  S. Pickett,et al.  The rapid but "invisible" changes in urban greenspace: A comparative study of nine Chinese cities. , 2018, The Science of the total environment.

[43]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[44]  L. Kubzansky,et al.  Is All Urban Green Space the Same? A Comparison of the Health Benefits of Trees and Grass in New York City , 2017, International journal of environmental research and public health.

[45]  Curt H. Davis,et al.  Urban land cover classification from high resolution multi-spectral IKONOS imagery , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[46]  Helmi Zulhaidi Mohd Shafri,et al.  Detailed intra-urban mapping through transferable OBIA rule sets using WorldView-2 very-high-resolution satellite images , 2015 .

[47]  Sabine Vanhuysse,et al.  Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data , 2017 .

[48]  Weifeng Li,et al.  Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery , 2014, Remote. Sens..

[49]  Massimiliano Pittore,et al.  Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images , 2014, Remote. Sens..

[50]  D. Armson.  The effect of trees and grass on the thermal and hydrological performance of an urban area , 2012 .

[51]  Arzu Erener,et al.  Classification method, spectral diversity, band combination and accuracy assessment evaluation for urban feature detection , 2013, Int. J. Appl. Earth Obs. Geoinformation.