1
|
Ewane EB, Bajaj S, Velasquez-Camacho L, Srinivasan S, Maeng J, Singla A, Luber A, de-Miguel S, Richardson G, Broadbent EN, Cardil A, Jaafar WSWM, Abdullah M, Corte APD, Silva CA, Doaemo W, Mohan M. Influence of urban forests on residential property values: A systematic review of remote sensing-based studies. Heliyon 2023; 9:e20408. [PMID: 37842597 PMCID: PMC10568372 DOI: 10.1016/j.heliyon.2023.e20408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 09/22/2023] [Accepted: 09/22/2023] [Indexed: 10/17/2023] Open
Abstract
Urban forests provide direct and indirect benefits to human well-being that are increasingly captured in residential property values. Remote Sensing (RS) can be used to measure a wide range of forest and vegetation parameters that allows for a more detailed and better understanding of their specific influences on housing prices. Herein, through a systematic literature review approach, we reviewed 89 papers (from 2010 to 2022) from 21 different countries that used RS data to quantify vegetation indices, forest and tree parameters of urban forests and estimated their influence on residential property values. The main aim of this study was to understand and provide insights into how urban forests influence residential property values based on RS studies. Although more studies were conducted in developed (n = 55, 61.7%) than developing countries (n = 34, 38.3%), the results indicated for the most part that increasing tree canopy cover on property and neighborhood level, forest size, type, greenness, and proximity to urban forests increased housing prices. RS studies benefited from spatially explicit repetitive data that offer superior efficiency to quantify vegetation, forest, and tree parameters of urban forests over large areas and longer periods compared to studies that used field inventory data. Through this work, we identify and underscore that urban forest benefits outweigh management costs and have a mostly positive influence on housing prices. Thus, we encourage further discussions about prioritizing reforestation and conservation of urban forests during the urban planning of cities and suburbs, which could support UN Sustainable Development Goals (SDGs) and urban policy reforms.
Collapse
Affiliation(s)
- Ewane Basil Ewane
- United Nations Volunteering Program, via Morobe Development Foundation, Lae 00411, Papua New Guinea
- Ecoresolve Inc., San Francisco, CA, USA, 94105
- Department of Geography, Faculty of Social and Management Sciences, University of Buea, P.O. BOX 63 Buea, Cameroon
| | - Shaurya Bajaj
- United Nations Volunteering Program, via Morobe Development Foundation, Lae 00411, Papua New Guinea
- Ecoresolve Inc., San Francisco, CA, USA, 94105
| | - Luisa Velasquez-Camacho
- Unit of Applied Artificial Intelligence, Eurecat, Centre Tecnològic de Catalunya, 08005 Barcelona, Spain
- Department of Agricultural and Forest Sciences and Engineering, University of Lleida, Av. Alcalde Rovira Roure 191, 5198 Lleida, Spain
| | - Shruthi Srinivasan
- Department of Forest Analytics, Texas A&M Forest Service, Dallas, TX 75252, USA
| | - Juyeon Maeng
- United Nations Volunteering Program, via Morobe Development Foundation, Lae 00411, Papua New Guinea
- AAP Labs, Cornell University, USA
| | - Anushka Singla
- United Nations Volunteering Program, via Morobe Development Foundation, Lae 00411, Papua New Guinea
| | - Andrea Luber
- United Nations Volunteering Program, via Morobe Development Foundation, Lae 00411, Papua New Guinea
| | - Sergio de-Miguel
- Department of Agricultural and Forest Sciences and Engineering, University of Lleida, Av. Alcalde Rovira Roure 191, 5198 Lleida, Spain
- Joint Research Unit CTFC-AGROTECNIO-CERCA, Ctra. Sant Llorenç de Morunys km 2, 25280 Solsona, Spain
| | - Gabriella Richardson
- United Nations Volunteering Program, via Morobe Development Foundation, Lae 00411, Papua New Guinea
- Department of Sociology and Anthropology, University of Guelph, Guelph ON, Canada
| | - Eben North Broadbent
- Spatial Ecology and Conservation (SPEC) Lab, School of Forest, Fisheries, and Geomatics Sciences, University of Florida, PO Box 110410, Gainesville, FL 32611, USA
| | - Adrian Cardil
- Department of Agricultural and Forest Sciences and Engineering, University of Lleida, Av. Alcalde Rovira Roure 191, 5198 Lleida, Spain
- Joint Research Unit CTFC-AGROTECNIO-CERCA, Ctra. Sant Llorenç de Morunys km 2, 25280 Solsona, Spain
- Tecnosylva, S.L Parque Tecnológico de León, 24004 León, Spain
| | - Wan Shafrina Wan Mohd Jaafar
- Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Meshal Abdullah
- Department of Geography, College of Arts and Social Sciences, Sultan Qaboos University, Muscat, P.O. Box 50, Oman
- Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX, USA
| | - Ana Paula Dalla Corte
- BIOFIX Research Center, Federal University of Paraná (UFPR), Curitiba 80210-170, Brazil
| | - Carlos Alberto Silva
- Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (Silva Lab), University of Florida, USA
| | - Willie Doaemo
- United Nations Volunteering Program, via Morobe Development Foundation, Lae 00411, Papua New Guinea
- Department of Civil Engineering, Papua New Guinea University of Technology, Lae, 00411, Papua New Guinea
- Morobe Development Foundation, Doyle Street, Trish Avenue-Eriku, Lae 00411, Papua New Guinea
| | - Midhun Mohan
- United Nations Volunteering Program, via Morobe Development Foundation, Lae 00411, Papua New Guinea
- Ecoresolve Inc., San Francisco, CA, USA, 94105
- Department of Geography, University of California-Berkeley, Berkeley, CA 94709, USA
| |
Collapse
|
2
|
Fabijańska A, Cahalan GD. Automatic resin duct detection and measurement from wood core images using convolutional neural networks. Sci Rep 2023; 13:7106. [PMID: 37130881 PMCID: PMC10154293 DOI: 10.1038/s41598-023-34304-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/27/2023] [Indexed: 05/04/2023] Open
Abstract
The structure and features of resin ducts provide valuable information about environmental conditions accompanying the growth of trees in the genus Pinus. Therefore analysis of resin duct characteristics has been an increasingly common measurement in dendrochronology. However, the measurement is tedious and time-consuming since it requires thousands of ducts to be manually marked in an image of an enlarged wood surface. Although tools exist to automate some stages of this process, no tool exists to automatically recognize and analyze the resin ducts and standardize them with the tree rings they belong to. This study proposes a new fully automatic pipeline that quantifies the properties of resin ducts in terms of the tree ring area to which they belong. A convolutional neural network underlays the pipeline to detect resin ducts and tree-ring boundaries. Also, a region merging procedure is used to identify connected components corresponding to successive rings. Corresponding ducts and rings are next related to each other. The pipeline was tested on 74 wood images representing five Pinus species. Over 8000 tree-ring boundaries and almost 25,000 resin ducts were analyzed. The proposed method detects resin ducts with a sensitivity of 0.85 and precision of 0.76. The corresponding scores for tree-ring boundary detection are 0.92 and 0.99, respectively.
Collapse
Affiliation(s)
- Anna Fabijańska
- Institute of Applied Computer Science, Lodz University of Technology, 18 Stefanowskiego Str., 90-537, Lodz, Poland.
| | - Gabriel D Cahalan
- The Nature Conservancy, 425 Barlow Place Suite 100, Bethesda, MD, 20814, USA
| |
Collapse
|
3
|
Mohsan SAH, Othman NQH, Li Y, Alsharif MH, Khan MA. Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends. INTEL SERV ROBOT 2023; 16:109-137. [PMID: 36687780 PMCID: PMC9841964 DOI: 10.1007/s11370-022-00452-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 12/21/2022] [Indexed: 01/18/2023]
Abstract
Recently, unmanned aerial vehicles (UAVs) or drones have emerged as a ubiquitous and integral part of our society. They appear in great diversity in a multiplicity of applications for economic, commercial, leisure, military and academic purposes. The drone industry has seen a sharp uptake in the last decade as a model to manufacture and deliver convergence, offering synergy by incorporating multiple technologies. It is due to technological trends and rapid advancements in control, miniaturization, and computerization, which culminate in secure, lightweight, robust, more-accessible and cost-efficient UAVs. UAVs support implicit particularities including access to disaster-stricken zones, swift mobility, airborne missions and payload features. Despite these appealing benefits, UAVs face limitations in operability due to several critical concerns in terms of flight autonomy, path planning, battery endurance, flight time and limited payload carrying capability, as intuitively it is not recommended to load heavy objects such as batteries. As a result, the primary goal of this research is to provide insights into the potentials of UAVs, as well as their characteristics and functionality issues. This study provides a comprehensive review of UAVs, types, swarms, classifications, charging methods and regulations. Moreover, application scenarios, potential challenges and security issues are also examined. Finally, future research directions are identified to further hone the research work. We believe these insights will serve as guidelines and motivations for relevant researchers.
Collapse
Affiliation(s)
- Syed Agha Hassnain Mohsan
- Optical Communications Laboratory, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan, 316021 Zhejiang China
| | | | - Yanlong Li
- Optical Communications Laboratory, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan, 316021 Zhejiang China
- Ministry of Education Key Laboratory of Cognitive Radio and Information Processing, Guilin University of Electronic Technology, Guilin, 541004 China
| | - Mohammed H. Alsharif
- Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul, 05006 Korea
| | - Muhammad Asghar Khan
- Department of Electrical Engineering, Hamdard Institute of Engineering & Technology, Islamabad, 44000 Pakistan
| |
Collapse
|
4
|
Abstract
Recently, unmanned aerial vehicles (UAVs), also known as drones, have come in a great diversity of several applications such as military, construction, image and video mapping, medical, search and rescue, parcel delivery, hidden area exploration, oil rigs and power line monitoring, precision farming, wireless communication and aerial surveillance. The drone industry has been getting significant attention as a model of manufacturing, service and delivery convergence, introducing synergy with the coexistence of different emerging domains. UAVs offer implicit peculiarities such as increased airborne time and payload capabilities, swift mobility, and access to remote and disaster areas. Despite these potential features, including extensive variety of usage, high maneuverability, and cost-efficiency, drones are still limited in terms of battery endurance, flight autonomy and constrained flight time to perform persistent missions. Other critical concerns are battery endurance and the weight of drones, which must be kept low. Intuitively it is not suggested to load them with heavy batteries. This study highlights the importance of drones, goals and functionality problems. In this review, a comprehensive study on UAVs, swarms, types, classification, charging, and standardization is presented. In particular, UAV applications, challenges, and security issues are explored in the light of recent research studies and development. Finally, this review identifies the research gap and presents future research directions regarding UAVs.
Collapse
|
5
|
Abstract
The method of collecting aerial images or videos by unmanned aerial vehicles (UAVs) for object search has the advantages of high flexibility and low cost, and has been widely used in various fields, such as pipeline inspection, disaster rescue, and forest fire prevention. However, in the case of object search in a wide area, the scanning efficiency and real-time performance of UAV are often difficult to satisfy at the same time, which may lead to missing the best time to perform the task. In this paper, we design a wide-area and real-time object search system of UAV based on deep learning for this problem. The system first solves the problem of area scanning efficiency by controlling the high-resolution camera in order to collect aerial images with a large field of view. For real-time requirements, we adopted three strategies to accelerate the system, as follows: design a parallel system, simplify the object detection algorithm, and use TensorRT on the edge device to optimize the object detection model. We selected the NVIDIA Jetson AGX Xavier edge device as the central processor and verified the feasibility and practicability of the system through the actual application of suspicious vehicle search in the grazing area of the prairie. Experiments have proved that the parallel design of the system can effectively meet the real-time requirements. For the most time-consuming image object detection link, with a slight loss of precision, most algorithms can reach the 400% inference speed of the benchmark in total, after algorithm simplification, and corresponding model’s deployment by TensorRT.
Collapse
|
6
|
Mapping Key Indicators of Forest Restoration in the Amazon Using a Low-Cost Drone and Artificial Intelligence. REMOTE SENSING 2022. [DOI: 10.3390/rs14040830] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Monitoring the vegetation structure and species composition of forest restoration (FR) in the Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts (RPAs) associated with deep learning (DL) are becoming powerful tools for vegetation monitoring, this study aims to use DL to automatically map individual crowns of Vismia (low resilience recovery indicator), Cecropia (fast recovery indicator), and trees in general (this study refers to individual crowns of all trees regardless of species as All Trees). Since All Trees can be accurately mapped, this study also aims to propose a tree crown heterogeneity index (TCHI), which estimates species diversity based on: the heterogeneity attributes/parameters of the RPA image inside the All Trees results; and the Shannon index measured by traditional fieldwork. Regarding the DL methods, this work evaluated the accuracy of the detection of individual objects, the quality of the delineation outlines and the area distribution. Except for Vismia delineation (IoU = 0.2), DL results presented accurate values in general, as F1 and IoU were always greater than 0.7 and 0.55, respectively, while Cecropia presented the most accurate results: F1 = 0.85 and IoU = 0.77. Since All Trees results were accurate, the TCHI was obtained through regression analysis between the canopy height model (CHM) heterogeneity attributes and the field plot data. Although TCHI presented robust parameters, such as p-value < 0.05, its results are considered preliminary because more data are needed to include different FR situations. Thus, the results of this work show that low-cost RPA has great potential for monitoring FR quality in the Amazon, because Vismia, Cecropia, and All Trees can be automatically mapped. Moreover, the TCHI preliminary results showed high potential in estimating species diversity. Future studies must assess domain adaptation methods for the DL results and different FR situations to improve the TCHI range of action.
Collapse
|