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  "license_title": "Creative Commons Attribution Share-Alike 
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(CC-BY-SA)",
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  "name": "ramerenwald-close-range-remote-sensing-benchmark",
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  "name": "ramerenwald-close-range-remote-sensing-benchmark",
            
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  "notes": "Close Range Remote Sensing Benchmark for different LiDAR 
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  "notes": "Close Range Remote Sensing Benchmark for different LiDAR 
            
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and photogrammetric Sensors in a mixed temperate forest.\r\nBenchmarks 
54
and photogrammetric Sensors in a mixed temperate forest.\r\nBenchmarks 
            
55
are needed to evaluate the performance of different close-range remote 
55
are needed to evaluate the performance of different close-range remote 
            
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sensing devices and approaches, both in terms of efficiency as well as 
56
sensing devices and approaches, both in terms of efficiency as well as 
            
57
accuracy. In this study we evaluate the performance of two terrestrial 
57
accuracy. In this study we evaluate the performance of two terrestrial 
            
58
(TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser 
58
(TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser 
            
59
scanning systems to detect trees and extract the diameter at breast 
59
scanning systems to detect trees and extract the diameter at breast 
            
60
height (DBH) in three plots with a steep gradient in tree and 
60
height (DBH) in three plots with a steep gradient in tree and 
            
61
understorey vegetation density. As a novelty, we also tested the 
61
understorey vegetation density. As a novelty, we also tested the 
            
62
acquisition of 3D point-clouds using a low-cost action camera (GoPro) 
62
acquisition of 3D point-clouds using a low-cost action camera (GoPro) 
            
63
in conjunction with the Structure from Motion (SfM) technique and 
63
in conjunction with the Structure from Motion (SfM) technique and 
            
64
compared its performance with those of the more costly LiDAR 
64
compared its performance with those of the more costly LiDAR 
            
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devices.",
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devices.",
            
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  "num_resources": 3,
            
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  "num_tags": 8,
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  "num_tags": 8,
            
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  "organization": {
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  "organization": {
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2017-04-20T16:51:21.920128",
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    "created": "2017-04-20T16:51:21.920128",
            
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    "description": "We develop and apply comprehensive and robust 
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    "description": "We develop and apply comprehensive and robust 
            
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methods to extract and classify natural objects from continuous and 
72
methods to extract and classify natural objects from continuous and 
            
73
discrete raster datasets. Relevant features are acquired to describe 
73
discrete raster datasets. Relevant features are acquired to describe 
            
74
changes in landscape and land resources at different levels using 
74
changes in landscape and land resources at different levels using 
            
75
image data. Mathematical-statistical methods are adopted for automatic 
75
image data. Mathematical-statistical methods are adopted for automatic 
            
76
detection and description of image objects. Thus we contribute 
76
detection and description of image objects. Thus we contribute 
            
77
concepts, methods and data to describe/detect area wide changes and 
77
concepts, methods and data to describe/detect area wide changes and 
            
78
processes in the resources of landscape.\r\n\r\n### Tasks and main 
78
processes in the resources of landscape.\r\n\r\n### Tasks and main 
            
79
research\r\n\r\n* Development and application of methods to extract 
79
research\r\n\r\n* Development and application of methods to extract 
            
80
natural objects from continuous data.\r\n* Development of methods for 
80
natural objects from continuous data.\r\n* Development of methods for 
            
81
a comprehensive description of natural and anthropogenetic boundaries 
81
a comprehensive description of natural and anthropogenetic boundaries 
            
82
in continuous pattern (e.g. map signatures, vegetation transition, 
82
in continuous pattern (e.g. map signatures, vegetation transition, 
            
83
forest borders).\r\n* Development and application of methods to 
83
forest borders).\r\n* Development and application of methods to 
            
84
extract 3D-information from remotely sensed data for description of 
84
extract 3D-information from remotely sensed data for description of 
            
85
natural structures and changes. The main focus lies on wood and its 
85
natural structures and changes. The main focus lies on wood and its 
            
86
embedding/interaction within/with the landscape.\r\n* Conception and 
86
embedding/interaction within/with the landscape.\r\n* Conception and 
            
87
development of data acquisition based on high resolution remote 
87
development of data acquisition based on high resolution remote 
            
88
sensing data.\r\n* Conception, development and maintenance of the 
88
sensing data.\r\n* Conception, development and maintenance of the 
            
89
software interface in area wide data acquisition using airborne remote 
89
software interface in area wide data acquisition using airborne remote 
            
90
sensing data.\r\n* Scientific expert advice and support in the fields 
90
sensing data.\r\n* Scientific expert advice and support in the fields 
            
91
of photogrammetry and survey at WSL. Maintenance, enhancements and 
91
of photogrammetry and survey at WSL. Maintenance, enhancements and 
            
92
future development in these specific fields.\r\n* Adequate 
92
future development in these specific fields.\r\n* Adequate 
            
93
presentation of scientific results on national level and in noted 
93
presentation of scientific results on national level and in noted 
            
94
international journals and at international 
94
international journals and at international 
            
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congresses/workshops/symposia.\r\n\r\n__Further information__: 
95
congresses/workshops/symposia.\r\n\r\n__Further information__: 
            
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l/organization/research-units/landscape-dynamics/remote-sensing.html",
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l/organization/research-units/landscape-dynamics/remote-sensing.html",
            
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    "state": "active",
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    "title": "Remote Sensing",
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    "title": "Remote Sensing",
            
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  "publication": "{\"publication_year\": \"2023\", \"publisher\": 
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  "publication": "{\"publication_year\": \"2023\", \"publisher\": 
            
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\"EnviDat\"}",
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\"EnviDat\"}",
            
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  "related_datasets": "",
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  "related_datasets": "",
            
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  "related_publications": " * K\u00fckenbrink, D.; Marty, M.; 
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  "related_publications": " * K\u00fckenbrink, D.; Marty, M.; 
            
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B\u00f6sch, R.; Ginzler, C. Benchmarking laser scanning and 
112
B\u00f6sch, R.; Ginzler, C. Benchmarking laser scanning and 
            
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terrestrial photogrammetry to extract forest inventory parameters in a 
113
terrestrial photogrammetry to extract forest inventory parameters in a 
            
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complex temperate forest. International Journal of Applied Earth 
114
complex temperate forest. International Journal of Applied Earth 
            
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Observation and Geoinformation. Volume 113, 2022. 
115
Observation and Geoinformation. Volume 113, 2022. 
            
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0.1016/j.jag.2022.102999](https://doi.org/10.1016/j.jag.2022.102999)",
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0.1016/j.jag.2022.102999](https://doi.org/10.1016/j.jag.2022.102999)",
            
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