Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)

Abstract

Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map and monitor urban tree coverage and changes over time as an efficient and low-cost alternative to field-based measurements, which are time consuming and costly. Automatic extraction of urban land cover features with high accuracy is a challenging task, and it demands object based artificial intelligence workflows for efficiency and thematic accuracy. The aim of this research is to effectively map urban tree cover changes and model the relationship of such changes with socioeconomic variables. The object-based convolutional neural network (CNN) method is illustrated by mapping urban tree cover changes between 2005 and 2015/16 using satellite, Google Earth imageries and Light Detection and Ranging (LiDAR) datasets. The training sample for CNN model was generated by Object Based Image Analysis (OBIA) using thresholds in a Canopy Height Model (CHM) and the Normalised Difference Vegetation Index (NDVI). The tree heatmap produced from the CNN model was further refined using OBIA. Tree cover loss, gain and persistence was extracted, and multiple regression analysis was applied to model the relationship with socioeconomic variables. The overall accuracy and kappa coefficient of tree cover extraction was 96% and 0.77 for 2005 images and 98% and 0.93 for 2015/16 images, indicating that the object-based CNN technique can be effectively implemented for urban tree coverage mapping and monitoring. There was a decline in tree coverage in all suburbs. Mean parcel size and median household income were significantly related to tree cover loss (R2 = 58.5%). Tree cover gain and persistence had positive relationship with tertiary education, parcel size and ownership change (gain: R2 = 67.8% and persistence: R2 = 75.3%). The research findings demonstrated that remote sensing data with intelligent processing can contribute to the development of policy input for management of tree coverage in cities.

Deep learning for processing and analysis of remote sensing big data: a technical review

“…(2) object extraction Object extraction can be formed as a biclass classification or instance segmentation, aiming to predict the pixelwise mask of each instance in RS images. These objects can be buildings, roads, waterbodies, and other regions of interest, such as urban canopy (Timilsina, Aryal, & Kirkpatrick, 2020), nutrient deficient areas (Dadsetan, Pichler, & Wilson et al, 2021).…”

Deep Learning-Based Instance Segmentation Method of Litchi Canopy from UAV-Acquired Images

“…For instance, Dong et al [23] designed digital height models (DHMs) by subtracting the DTM from the DSM, which is the key data product for avoiding confusion between the treetop and soil areas. Similarly, Timilsina et al [24] developed a canopy height model (CHM) by subtracting DEM from the DSM using the tool in ENVI for the identification of tree coverage.…”

“…Previous studies on the identification of individual trees have been focused on several species, including citrus [4,5,8,[25][26][27][28], apple [23], palm [10,14,29], cranberry [21], and urban trees [13,24]. However, although there are studies on the semantic segmentation of litchi flowers [30] and branches [31], the studies on litchi canopy segmentation based on remote sensing, as far as we know, have not been proposed.…”

Semi-U-Net: A Lightweight Deep Neural Network for Subject-Sensitive Hashing of HRRS Images

“…The extraction and analysis of earth surface features through high resolution remote sensing (HRRS) images has received extensive research, such as the buildings extraction [1][2][3], vegetation detection [4][5][6], urban expansion analysis [7][8][9] and detection of land cover changes [10]. However, there is a key issue that cannot be ignored: ensuring the security of HRRS image is the basic prerequisite for using HRRS images.…”