scholarly journals USING MOBILE LASER SCANNING POINT CLOUDS TO EXTRACT URBAN ROADSIDE TREES FOR ECOLOGICAL BENEFITS ESTIMATION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 211-216
Author(s):  
W. Fan ◽  
B. Yang ◽  
F. Liang ◽  
Z. Dong
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Morphological Parameters ◽  
Heat Island ◽  
Individual Tree ◽  
Ecological Benefits ◽  
Urban Heat ◽  
The City ◽  
Tree Extraction ◽  
Parameters Calculation

Abstract. Roadside trees in the city play a crucial role in addressing the issues of air pollution, urban heat island effects, road noise, and so on. This paper proposes an efficient and robust method to automatically extract individual roadside trees with morphological parameters from mobile laser scanning (MLS) point clouds for ecological benefits estimation. The proposed method consists of four steps: MLS data pre-processing, pole-like objects classification, individual tree extraction, morphological parameters calculation for ecological benefits estimation. The proposed method is verified using three complex datasets in Shanghai, China. Comprehensive experiments demonstrate that the proposed method achieves good performance in extracting individual tree in terms of average precision and recall (91.83%, 92.60%), and provides detailed information for ecological benefits estimation.

scholarly journals Individual tree extraction from terrestrial laser scanning data via graph pathing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Di Wang ◽  
Xinlian Liang ◽  
Gislain II Mofack ◽  
Olivier Martin-Ducup
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Tree Level ◽  
Forest Types ◽  
Computationally Efficient ◽  
Large Area ◽  
Individual Tree ◽  
Single Crown ◽  
Tree Extraction

Abstract Background Individual tree extraction from terrestrial laser scanning (TLS) data is a prerequisite for tree-scale estimations of forest biophysical properties. This task currently is undertaken through laborious and time-consuming manual assistance and quality control. This study presents a new fully automatic approach to extract single trees from large-area TLS data. This data-driven method operates exclusively on a point cloud graph by path finding, which makes our method computationally efficient and universally applicable to data from various forest types. Results We demonstrated the proposed method on two openly available datasets. First, we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest plots. Second, we successfully extracted 270 trees from one hectare temperate forest. Quantitative validation resulted in a mean Intersection over Union (mIoU) of 0.82 for single crown segmentation, which further led to a relative root mean square error (RMSE%) of 21.2% and 23.5% for crown area and tree volume estimations, respectively. Conclusions Our method allows automated access to individual tree level information from TLS point clouds. The proposed method is free from restricted assumptions of forest types. It is also computationally efficient with an average processing time of several seconds for one million points. It is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications, ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.

scholarly journals Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation

2021 ◽  
Vol 13 (2) ◽  
pp. 223
Author(s):  
Zhenyang Hui ◽  
Shuanggen Jin ◽  
Dajun Li ◽  
Yao Yevenyo Ziggah ◽  
Bo Liu
Keyword(s):  
Transfer Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Principal Component ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Individual Tree ◽  
Terrestrial Lidar ◽  
Initial Segmentation ◽  
Tree Extraction

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.

Potential for Individual Tree Monitoring in Ponderosa Pine-Dominated Forests Using Unmanned Aerial System Structure from Motion Point Clouds

2021 ◽  
Author(s):  
Matthew B. Creasy ◽  
Wade Travis Tinkham ◽  
Chad M. Hoffman ◽  
Jody C. Vogeler
Keyword(s):  
Forest Structure ◽  
Ponderosa Pine ◽  
Laser Scanning ◽  
Point Clouds ◽  
System Structure ◽  
Detection Methods ◽  
Unmanned Aerial System ◽  
Individual Tree ◽  
Detection Rates ◽  
Ponderosa Pine Forests

Characterization of forest structure is important for management-related decision making, monitoring, and adaptive management. Increasingly, observations of forest structure are needed at both finer resolutions and across greater extents to support spatially explicit management planning. Unmanned aerial system (UAS)-based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment than existing methods such as airborne laser scanning (LiDAR). This study utilizes nearly 5,000 stem-mapped trees in ponderosa pine-dominated forests to evaluate several algorithms for detecting individual tree locations and characterizing crown area across tree sizes. Our results indicate that adaptive variable-window detection methods with UAS-based canopy height models have greater tree detection rates compared to fixed window analysis across a range of tree sizes. Using the UAS approach, probability of detecting individual trees decreases from 97% for dominant overstory to 67% for suppressed understory trees. Additionally, crown radii were correctly determined within 0.5 m for approximately two-thirds of sampled trees. These findings highlight the potential for UAS photogrammetry to characterize forest structure through the detection of trees and tree groups in open-canopy ponderosa pine forests. Further work should investigate how these methods transfer to more diverse species compositions and forest structures.

scholarly journals Urban "Heat-Island Effect" and its Connection with Architectural and Climatic Features on the Example of Poltava

2018 ◽  
Vol 7 (3.2) ◽  
pp. 597
Author(s):  
Yuri Golik ◽  
Oksana Illiash ◽  
Nataliia Maksiuta
Keyword(s):  
Urban Heat Island ◽  
Traditional Method ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Island Effect ◽  
Temperature Regimes ◽  
Architectural Features ◽  
Urban Heat ◽  
The Given ◽  
The City

The concept of "heat-island effect", its structure and features of formation over the city are given. The climatic and other features of the city that influence the formation of this phenomenon are mentioned.  The data on functioning in the city of the municipal production enterprise of the heat economy is indicated. The traditional method for determining the formation of the urban "heat-island effect" is described. The data and comparative graphs on the temperature regimes of the city and region are presented. The possibility of influencing architectural features of the city on the formation of the "heat-island-effect" is determined. According to the obtained results, further integrated researches are proposed for obtaining reliable results of the given question. 

scholarly journals A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

2021 ◽  
Author(s):  
Shihan Chen ◽  
Yuanjian Yang ◽  
Fei Deng ◽  
Yanhao Zhang ◽  
Duanyang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Model Framework ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
The City

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

scholarly journals Solar Irradiance Reduction Using Optimized Green Infrastructure in Arid Hot Regions: A Case Study in El-Nozha District, Cairo, Egypt

2021 ◽  
Vol 13 (17) ◽  
pp. 9617 ◽  
Author(s):  
Wesam M. Elbardisy ◽  
Mohamed A. Salheen ◽  
Mohammed Fahmy
Keyword(s):  
Climate Change ◽  
Urban Heat Island ◽  
Green Infrastructure ◽  
Solar Irradiance ◽  
Urban Trees ◽  
Heat Island ◽  
Physiological Equivalent Temperature ◽  
Urban Climatology ◽  
Urban Heat ◽  
The City

In the Middle East and North Africa (MENA) region, studies focused on the relationship between urban planning practice and climatology are still lacking, despite the fact that the latter has nearly three decades of literature in the region and the former has much more. However, such an unfounded relationship that would consider urban sustainability measures is a serious challenge, especially considering the effects of climate change. The Greater Cairo Region (GCR) has recently witnessed numerous serious urban vehicular network re-development, leaving the city less green and in need of strategically re-thinking the plan regarding, and the role of, green infrastructure. Therefore, this study focuses on approaches to the optimization of the urban green infrastructure, in order to reduce solar irradiance in the city and, thus, its effects on the urban climatology. This is carried out by studying one of the East Cairo neighborhoods, named El-Nozha district, as a representative case of the most impacted neighborhoods. In an attempt to quantify these effects, using parametric simulation, the Air Temperature (Ta), Mean Radiant Temperature (Tmrt), Relative Humidity (RH), and Physiological Equivalent Temperature (PET) parameters were calculated before and after introducing urban trees, acting as green infrastructure types that mitigate climate change and the Urban Heat Island (UHI) effect. Our results indicate that an optimized percentage, spacing, location, and arrangement of urban tree canopies can reduce the irradiance flux at the ground surface, having positive implications in terms of mitigating the urban heat island effect.

scholarly journals Urban heat island in Bloemfontein during winter

2013 ◽  
Vol 32 (1) ◽  
Author(s):  
Pieter Snyman ◽  
A. Stephen Steyn
Keyword(s):  
Maximum Intensity ◽  
Urban Heat Islands ◽  
Urban Air ◽  
Heat Island ◽  
Heat Islands ◽  
Air Temperatures ◽  
Local Topography ◽  
Urban Heat ◽  
The Difference ◽  
The City

Urban heat islands (UHIs) are characterised by warmer urban air temperatures compared to rural air temperatures, and the intensity is equal to the difference between the two. Air temperatures are measured at various sites across the city of Bloemfontein and then analysed to determine the UHI characteristics. The UHI is found to have a horseshoe shape and reaches a maximum intensity of 8.2 °C at 22:00. The UHI is largely affected by the local topography.

scholarly journals Assessment of Changes in Land Use/Land Cover and Land Surface Temperatures and Their Impact on Surface Urban Heat Island Phenomena in the Kathmandu Valley (1988–2018)

2020 ◽  
Vol 9 (12) ◽  
pp. 726
Author(s):  
Md. Omar Sarif ◽  
Bhagawat Rimal ◽  
Nigel E. Stork
Keyword(s):  
Urban Heat Island ◽  
Land Surface ◽  
Agricultural Land ◽  
Kathmandu Valley ◽  
Heat Island ◽  
City Center ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
The City ◽  
Normal Difference

More than half of the world’s populations now live in rapidly expanding urban and its surrounding areas. The consequences for Land Use/Land Cover (LULC) dynamics and Surface Urban Heat Island (SUHI) phenomena are poorly understood for many new cities. We explore this issue and their inter-relationship in the Kathmandu Valley, an area of roughly 694 km2, at decadal intervals using April (summer) Landsat images of 1988, 1998, 2008, and 2018. LULC assessment was made using the Support Vector Machine algorithm. In the Kathmandu Valley, most land is either natural vegetation or agricultural land but in the study period there was a rapid expansion of impervious surfaces in urban areas. Impervious surfaces (IL) grew by 113.44 km2 (16.34% of total area), natural vegetation (VL) by 6.07 km2 (0.87% of total area), resulting in the loss of 118.29 km2 area from agricultural land (17.03% of total area) during 1988–2018. At the same time, the average land surface temperature (LST) increased by nearly 5–7 °C in the city and nearly 3–5 °C at the city boundary. For different LULC classes, the highest mean LST increase during 1988–2018 was 7.11 °C for IL with the lowest being 3.18 °C for VL although there were some fluctuations during this time period. While open land only occupies a small proportion of the landscape, it usually had higher mean LST than all other LULC classes. There was a negative relationship both between LST and Normal Difference Vegetation Index (NDVI) and LST and Normal Difference Moisture Index (NDMI), respectively, and a positive relationship between LST and Normal Difference Built-up Index (NDBI). The result of an urban–rural gradient analysis showed there was sharp decrease of mean LST from the city center outwards to about 15 kms because the NDVI also sharply increased, especially in 2008 and 2018, which clearly shows a surface urban heat island effect. Further from the city center, around 20–25 kms, mean LST increased due to increased agriculture activity. The population of Kathmandu Valley was 2.88 million in 2016 and if the growth trend continues then it is predicted to reach 3.85 million by 2035. Consequently, to avoid the critical effects of increasing SUHI in Kathmandu it is essential to improve urban planning including the implementation of green city technologies.

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