Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle

Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring cities’ green infrastructure requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (n = 172 trees) to obtain tree growth in the form of effective leaf area index (LAIe) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016–2017. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of LAIe and TWSI to monitor the effects of climate change on the GI of cities.

[1]  J. Carley,et al.  Use of emerging remote sensing technologies for measuring long-term shoreline change and coastal management , 2019 .

[2]  Sasan Haghani,et al.  Design and Implementation of a DASH7-Based Wireless Sensor Network for Green Infrastructure , 2019 .

[3]  Samuel Ortega-Farías,et al.  Digital Cover Photography for Estimating Leaf Area Index (LAI) in Apple Trees Using a Variable Light Extinction Coefficient , 2015, Sensors.

[4]  J. Escalonad,et al.  Night-time responses to water supply in grapevines (Vitis vinifera L.) under deficit irrigation and partial root-zone drying , 2014 .

[5]  B. Ritter.  Use of unmanned aerial vehicles (UAV) for urban tree inventories , 2014 .

[6]  M. Perc,et al.  Evaluating Green Infrastructure via Unmanned Aerial Systems and Optical Imagery Indices , 2020 .

[7]  Wenhui Kuang,et al.  Investigating the Patterns and Dynamics of Urban Green Space in China's 70 Major Cities Using Satellite Remote Sensing , 2020, Remote. Sens..

[8]  W. Fricke.  Night-Time Transpiration - Favouring Growth? , 2019, Trends in plant science.

[9]  Michael A. Forster,et al.  Interactive effects of elevated CO2 and drought on nocturnal water fluxes in Eucalyptus saligna. , 2011, Tree physiology.

[10]  Baofeng Su,et al.  Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications , 2017, J. Sensors.

[11]  Haibin Lv,et al.  Infrastructure Monitoring and Operation for Smart Cities Based on IoT System , 2020, IEEE Transactions on Industrial Informatics.

[12]  R. Valentini,et al.  IoT Monitoring of Urban Tree Ecosystem Services: Possibilities and Challenges , 2020, Forests.

[13]  R. De Bei,et al.  Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methods , 2014 .

[14]  T. Jones.  Advances in Environmental Measurement Systems: Remote Sensing of Urban Methane Emissions and Tree Sap Flow Quantification , 2019 .

[15]  H. Jones.  Thermal Imaging and Infrared Sensing in Plant Ecophysiology , 2018 .

[16]  Michael A. Forster,et al.  Nighttime transpiration of Populus euphratica during different phenophases , 2018, Journal of Forestry Research.

[17]  D. Watson.  Comparative Physiological Studies on the Growth of Field Crops: I. Variation in Net Assimilation Rate and Leaf Area between Species and Varieties, and within and between Years , 1947 .

[18]  M. A. Skewes,et al.  Night-time sap flow is parabolically linked to midday water potential for field-grown almond trees , 2013, Irrigation Science.

[19]  E. Honkavaara,et al.  Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft , 2018 .

[20]  Robert G. Traver,et al.  Performance Optimization of a Green Infrastructure Treatment Train Using Real-Time Controls , 2017 .

[21]  J. Watson,et al.  The impact of urbanization and climate change on urban temperatures: a systematic review , 2017, Landscape Ecology.

[22]  Abbas Mohajerani,et al.  The urban heat island effect, its causes, and mitigation, with reference to the thermal properties of asphalt concrete. , 2017, Journal of environmental management.

[23]  Damir Medak,et al.  Geospatial monitoring of green infrastructure – case study Zagreb, Croatia , 2017 .

[24]  Paul Osmond,et al.  Understanding Land Surface Temperature Differences of Local Climate Zones Based on Airborne Remote Sensing Data , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Stéphane Herbette,et al.  Water stress-induced xylem hydraulic failure is a causal factor of tree mortality in beech and poplar. , 2013, Annals of botany.

[26]  H. Jones.  Plants and Microclimate: Other environmental factors: wind, altitude, climate change and atmospheric pollutants , 2013 .

[27]  Hamlyn G. Jones,et al.  Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling , 1999 .

[28]  P. Willems,et al.  Heat stress increase under climate change twice as large in cities as in rural areas: A study for a densely populated midlatitude maritime region , 2017 .

[29]  Rui Pitarma,et al.  Contribution to Trees Health Assessment Using Infrared Thermography , 2019, Agriculture.

[31]  A. Vergés,et al.  Seagrass on the brink: Decline of threatened seagrass Posidonia australis continues following protection , 2018, PloS one.

[32]  Eija Honkavaara,et al.  A Novel Deep Learning Method to Identify Single Tree Species in UAV-Based Hyperspectral Images , 2020, Remote. Sens..

[33]  Joseph O. Sexton,et al.  Urban Warming Drives Insect Pest Abundance on Street Trees , 2013, PloS one.

[34]  Marco Mora,et al.  Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies , 2016, Comput. Electron. Agric..

[35]  Sugie Lee,et al.  Analyzing Thermal Characteristics of Urban Streets Using a Thermal Imaging Camera: A Case Study on Commercial Streets in Seoul, Korea , 2018 .

[36]  Manfred Stoll,et al.  Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. , 2002, Journal of experimental botany.

[37]  S. Tyerman,et al.  Computational water stress indices obtained from thermal image analysis of grapevine canopies , 2012, Irrigation science.

[38]  Sigfredo Fuentes,et al.  An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI. , 2008, Functional plant biology : FPB.

[39]  Christopher Gomez,et al.  3D modelling of individual trees using a handheld camera: Accuracy of height, diameter and volume estimates , 2015 .

[40]  Marco Mora,et al.  Performance Assessment of Thermal Infrared Cameras of Different Resolutions to Estimate Tree Water Status from Two Cherry Cultivars: An Alternative to Midday Stem Water Potential and Stomatal Conductance , 2020, Sensors.

[41]  Sigfredo Fuentes,et al.  Assessment of canopy vigor information from kiwifruit plants based on a digital surface model from unmanned aerial vehicle imagery , 2019 .

[42]  Claudia Gonzalez Viejo,et al.  Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach , 2019, Sensors.

[43]  Zhou Wang,et al.  Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery , 2019, Remote. Sens..

[44]  Sigfredo Fuentes,et al.  Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality , 2020 .

[45]  J. Koricheva,et al.  Drought effects on damage by forest insects and pathogens: a meta‐analysis , 2012 .

[46]  Marcos Carrasco-Benavides,et al.  Assessment of an automated digital method to estimate leaf area index (LAI) in cherry trees , 2016 .

[47]  M. McGeoch,et al.  Temperature variation across Marion Island associated with a keystone plant species (Azorella selago Hook. (Apiaceae)) , 2007, Polar Biology.

[48]  Jason Smith,et al.  VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine , 2016, Sensors.

[49]  Craig Macfarlane,et al.  Measurement of Crown Cover and Leaf Area Index Using Digital Cover Photography and Its Application to Remote Sensing , 2009, Remote. Sens..

[50]  Margarita Georgieva,et al.  Integrated assessment of urban green infrastructure condition in Karlovo urban area by in-situ observations and remote sensing , 2018 .

[51]  Kathy Steppe,et al.  Responses of tree species to heat waves and extreme heat events. , 2015, Plant, cell & environment.

[52]  Rui Pitarma,et al.  Infrared Thermography Applied to Tree Health Assessment: A Review , 2019, Agriculture.

[53]  J. Peñuelas,et al.  Early Diagnosis of Vegetation Health From High-Resolution Hyperspectral and Thermal Imagery: Lessons Learned From Empirical Relationships and Radiative Transfer Modelling , 2019, Current Forestry Reports.

[54]  Yifang Ban,et al.  Monitoring Urban Green Infrastructure Changes and Impact on Habitat Connectivity Using High-Resolution Satellite Data , 2020, Remote. Sens..

[55]  J. Atchison,et al.  Seeing the trees for the (urban) forest: more-than-human geographies and urban greening , 2018, Australian Geographer.

[56]  Sigfredo Fuentes,et al.  Digital surface model applied to unmanned aerial vehicle based photogrammetry to assess potential biotic or abiotic effects on grapevine canopies , 2016 .

[57]  Paul Osmond,et al.  Mapping and classifying green infrastructure typologies for climate-related studies based on remote sensing data , 2019, Urban Forestry & Urban Greening.