scholarly journals Urban Tree Species Identification and Carbon Stock Mapping for Urban Green Planning and Management

Forests ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1226
Author(s):  
Md Abdul Mueed Choudhury ◽  
Ernesto Marcheggiani ◽  
Francesca Despini ◽  
Sofia Costanzini ◽  
Paolo Rossi ◽  
...  
Keyword(s):  
Tree Species ◽  
Carbon Stock ◽  
Urban Trees ◽  
Species Classification ◽  
Urban Green ◽  
Urban Tree ◽  
Object Based Image Analysis ◽  
Planning And Management ◽  
Object Based ◽  
Atmospheric Carbon

Recently, the severe intensification of atmospheric carbon has highlighted the importance of urban tree contributions in atmospheric carbon mitigations in city areas considering sustainable urban green planning and management systems. Explicit and timely information on urban trees and their roles in the atmospheric Carbon Stock (CS) are essential for policymakers to take immediate actions to ameliorate the effects of deforestation and their worsening outcomes. In this study, a detailed methodology for urban tree CS calibration and mapping was developed for the small urban area of Sassuolo in Italy. For dominant tree species classification, a remote sensing approach was applied, utilizing a high-resolution WV3 image. Five dominant species were identified and classified by applying the Object-Based Image Analysis (OBIA) approach with an overall accuracy of 78%. The CS calibration was done by utilizing an allometric model based on the field data of tree dendrometry—i.e., Height (H) and Diameter at Breast Height (DBH). For geometric measurements, a terrestrial photogrammetric approach known as Structure-from-Motion (SfM) was utilized. Out of 22 randomly selected sample plots of 100 square meters (10 m × 10 m) each, seven plots were utilized to validate the results of the CS calibration and mapping. In this study, CS mapping was done in an efficient and convenient way, highlighting higher CS and lower CS zones while recognizing the dominant tree species contributions. This study will help city planners initiate CS mapping and predict the possible CS for larger urban regions to ensure a sustainable urban green management system.

scholarly journals MAPPING URBAN TREES WITHIN CADASTRAL PARCELS USING AN OBJECT-BASED CONVOLUTIONAL NEURAL NETWORK

2019 ◽  
Vol IV-5/W2 ◽  
pp. 111-117 ◽  
Author(s):  
S. Timilsina ◽  
S. K. Sharma ◽  
J. Aryal
Keyword(s):  
Neural Network ◽  
Management Practices ◽  
Urban Trees ◽  
Urban Tree ◽  
Object Based Image Analysis ◽  
Urban Land Cover ◽  
Object Based ◽  
Training Samples ◽  
Level Statistics ◽  
Tree Coverage

Abstract. Urban trees offer significant benefits for improving the sustainability and liveability of cities, but its monitoring is a major challenge for urban planners. Remote-sensing based technologies can effectively detect, monitor and quantify urban tree coverage as an alternative to field-based measurements. Automatic extraction of urban land cover features with high accuracy is a challenging task and it demands artificial intelligence workflows for efficiency and thematic quality. In this context, the objective of this research is to map urban tree coverage per cadastral parcel of Sandy Bay, Hobart from very high-resolution aerial orthophoto and LiDAR data using an Object Based Convolution Neural Network (CNN) approach. Instead of manual preparation of a large number of required training samples, automatically classified Object based image analysis (OBIA) output is used as an input samples to train CNN method. Also, CNN output is further refined and segmented using OBIA to assess the accuracy. The result shows 93.2% overall accuracy for refined CNN classification. Similarly, the overlay of improved CNN output with cadastral parcel layer shows that 21.5% of the study area is covered by trees. This research demonstrates that the accuracy of image classification can be improved by using a combination of OBIA and CNN methods. Such a combined method can be used where manual preparation of training samples for CNN is not preferred. Also, our results indicate that the technique can be implemented to calculate parcel level statistics for urban tree coverage that provides meaningful metrics to guide urban planning and land management practices.

Object-based fusion for urban tree species classification from hyperspectral, panchromatic and nDSM data

2019 ◽  
Vol 40 (14) ◽  
pp. 5339-5365 ◽  
Author(s):  
Josselin Aval ◽  
Sophie Fabre ◽  
Emmanuel Zenou ◽  
David Sheeren ◽  
Mathieu Fauvel ◽  
...  
Keyword(s):  
Tree Species ◽  
Species Classification ◽  
Urban Tree ◽  
Tree Species Classification ◽  
Object Based

scholarly journals Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

2015 ◽  
Vol 7 (12) ◽  
pp. 16917-16937 ◽  
Author(s):  
Dan Li ◽  
Yinghai Ke ◽  
Huili Gong ◽  
Xiaojuan Li
Keyword(s):  
Tree Species ◽  
Species Classification ◽  
Urban Tree ◽  
Tree Species Classification ◽  
Object Based

scholarly journals An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1868
Author(s):  
Martina Deur ◽  
Mateo Gašparović ◽  
Ivan Balenović
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Tree Species ◽  
Classification Accuracy ◽  
New Technologies ◽  
Deciduous Forest ◽  
Species Classification ◽  
Tree Species Classification ◽  
Object Based ◽  
Very High

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

scholarly journals Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 692
Author(s):  
MD Abdul Mueed Choudhury ◽  
Ernesto Marcheggiani ◽  
Andrea Galli ◽  
Giuseppe Modica ◽  
Ben Somers
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Tree Species ◽  
Carbon Stock ◽  
Dominant Species ◽  
Remote Sensing Data ◽  
Data Sources ◽  
Lidar Data ◽  
Urban Tree ◽  
Sensing Data

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

scholarly journals An Operational Workflow of Deciduous-Dominated Forest Species Classification: Crown Delineation, Gap Elimination, and Object-Based Classification

2019 ◽  
Vol 11 (18) ◽  
pp. 2078 ◽  
Author(s):  
Yuhong He ◽  
Jian Yang ◽  
John Caspersen ◽  
Trevor Jones
Keyword(s):  
Tree Species ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Structural Diversity ◽  
Species Classification ◽  
Individual Tree ◽  
Spectral Overlap ◽  
Object Based ◽  
Early Fall ◽  
Crown Delineation

Recent advances in remote sensing technology provide sufficient spatial detail to achieve species-level classification over large vegetative ecosystems. In deciduous-dominated forests, however, as tree species diversity and forest structural diversity increase, the frequency of spectral overlap between species also increases and our ability to classify tree species significantly decreases. This study proposes an operational workflow of individual tree-based species classification for a temperate, mixed deciduous forest using three-seasonal WorldView images, involving three steps of individual tree crown (ITC) delineation, non-forest gap elimination, and object-based classification. The process of species classification started with ITC delineation using the spectral angle segmentation algorithm, followed by object-based random forest classifications. A total of 672 trees was located along three triangular transects for training and validation. For single-season images, the late-spring, mid-summer, and early-fall images achieve the overall accuracies of 0.46, 0.42, and 0.35, respectively. Combining the spectral information of the early-spring, mid-summer, and early-fall images increases the overall accuracy of classification to 0.79. However, further adding the late-fall image to separate deciduous and coniferous trees as an extra step was not successful. Compared to traditional four-band (Blue, Green, Red, Near-Infrared) images, the four additional bands of WorldView images (i.e., Coastal, Yellow, Red Edge, and Near-Infrared2) contribute to the species classification greatly (OA: 0.79 vs. 0.53). This study gains insights into the contribution of the additional spectral bands and multi-seasonal images to distinguishing species with seemingly high degrees of spectral overlap.

scholarly journals Effective Placement Methods of Vermicompost Application in Urban Tree Species: Implications for Sustainable Urban Afforestation

2020 ◽  
Vol 12 (14) ◽  
pp. 5822
Author(s):  
Huong Thi Thuy Dao ◽  
Jeong Min Seo ◽  
Jonathan O. Hernandez ◽  
Si Ho Han ◽  
Woo Bin Youn ◽  
...  
Keyword(s):  
Nutrient Uptake ◽  
Tree Species ◽  
Growth Parameters ◽  
Belowground Biomass ◽  
Chamaecyparis Obtusa ◽  
Urban Trees ◽  
Nutrient Concentrations ◽  
Larix Kaempferi ◽  
Urban Tree ◽  
Slow Growing

Knowledge on growth and nutrient uptake characteristics of urban trees and effective strategies to grow trees can help accomplish the goal of urban afforestation initiatives in a sustainable way. Thus, the study investigated the effects of different vermicompost (VC) application placements on the growth and nutrient uptake of three contrasting tree species (fast-growing Betula platyphylla and Larix kaempferi and slow-growing Chamaecyparis obtusa) to provide implications for growing tree stocks for sustainable urban afforestation programs. Five placement methods were used in the greenhouse trial: no fertilization (CON), surface placement (VCs), subsurface placement at 6-cm depth (VCc), bottom placement (35-cm depth (VCb)), and mixed with soil (VCm). We measured the growth parameters such as height, root collar diameter (RCD), and biomass and analyzed foliar nutrient concentrations in response to different placement treatments of VC. Relative height growth was the highest at VCc (132% (B. platyphylla), 114% (L. kaempferi)) and VCs ((57%) C. obtusa). Significant improvement in aboveground and belowground biomass growth of all species at VCs and VCc compared to the other treatments was also observed. Generally, VC treatments significantly increased N concentration compared to CON in all species. In conclusion, fertilizing the fast- and slow-growing urban tree species using VCs and/or VCc is relevant to growing high quality planting stocks for sustainable urban afforestation purposes.

Sign in / Sign up

Export Citation Format

Share Document