Evaluation of a Smartphone App for Forest Sample  Plot Measurements

Vastaranta, Mikko; Latorre, Eduardo González; Luoma, Ville; Saarinen, Ninni; Holopainen, Markus; Hyyppä, Juha

doi:10.3390/f6041179

Cited by 43 publications

(34 citation statements)

References 19 publications

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“…Yao et al [40] reported R 2 = 0.66 (lower than the Relasphone results in Finland) for plot-level basal area estimation with TLS, but a slightly better agreement for biomass (R 2 = 0.85), similar to the best results for pine and total stem volume in Mexico. The accuracies of the commercial forest inventory app Trestima was reported on 25 plots from another site in southern Finland [46]. The relative RMSE for Relasphone-based total basal area in Finland (RMSE(%) = 29.66%; Table 4) was comparable to that of Trestima (19.7%-29.3%) and slightly better in Mexico (RMSE(%) = 17.21%-18%).…”

Section: Relasphone Measurements Versus Other Forest Mensuration Methodsmentioning

confidence: 68%

“…Automatic image processing was investigated by Molinier et al in 2011 [45], and the interactive Relasphone application (http://www.relasphone.com) was released for free. Since then, similar smartphone apps have been proposed, such as MOTI for professional foresters and Trestima, a commercial application developed in 2012 that sends images to a server for automatic or semi-automatic processing [46]. To our knowledge, Relasphone measurements are the only ones that were extensively tested versus reference forest inventory plot data in two different biomes-boreal zone in Finland [47] and temperate zone in Durango, Mexico [48]-then combined with satellite images to produce maps of growing stock volume (GSV) or aboveground biomass (AGB).…”

Section: Introductionmentioning

confidence: 99%

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Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

et al. 2016

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Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland (boreal zone) were in good agreement with reference forest inventory plot data on pine (R 2 = 0.75, RMSE = 5.33 m 2 /ha), spruce (R 2 = 0.75, RMSE = 6.73 m 2 /ha) and birch (R 2 = 0.71, RMSE = 4.98 m 2 /ha), with total relative RMSE(%) = 29.66%. In Durango, Mexico (temperate zone), Relasphone stem volume measurements were best for pine (R 2 = 0.88, RMSE = 32.46 m 3 /ha) and total stem volume (R 2 = 0.87, RMSE = 35.21 m 3 /ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations.

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Section: Relasphone Measurements Versus Other Forest Mensuration Methodsmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

et al. 2016

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“…Calipers, a device used to measure the distance between two opposite sides, can easily measure the diameter or cross-section of a tree trunk [10]. With advancements in equipment, modern methods of collecting forest inventory data include laser rangefinders [11], terrestrial light detection and ranging (LiDAR; [12,13]), and even smartphone applications [14], which improve efficiency and data quality. Although different methods can be used to collect forest data depending on various technologies and their suitability, the methods must always be used on the ground.…”

Section: Introductionmentioning

confidence: 99%

Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

et al. 2017

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Methods for accurately measuring biophysical parameters are a key component for quantitative evaluation regarding to various forest applications. Conventional in situ measurements of these parameters take time and expense, encountering difficultness at locations with heterogeneous microtopography. To obtain precise biophysical data in such situations, we deployed an unmanned aerial system (UAS) multirotor drone in a cypress forest in a mountainous area of Japan. The structure from motion (SfM) method was used to construct a three-dimensional (3D) model of the forest (tree) structures from aerial photos. Tree height was estimated from the 3D model and compared to in situ ground data. We also analyzed the relationships between a biophysical parameter, diameter at breast height (DBH), of individual trees with canopy width and area measured from orthorectified images. Despite the constraints of ground exposure in a highly dense forest area, tree height was estimated at an accuracy of root mean square error = 1.712 m for observed tree heights ranging from 16 to 24 m. DBH was highly correlated with canopy width (R 2 = 0.7786) and canopy area (R 2 = 0.7923), where DBH ranged from 11 to 58 cm. The results of estimating forest parameters indicate that drone-based remote-sensing methods can be utilized to accurately analyze the spatial extent of forest structures.

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“…In another study, [18] used a novel approach for acquisition of forest horizontal structure (stem distribution) with panoramic photography or a similar technique which may be applied through smartphone [19]; however, a recent technique using multi-view stereopsis (MVS) combined with computer vision and photogrammetry, for example [20], alongside algorithms such as Scale-invariant feature transform (SIFT) [21] or Speeded Up Robust Features (SURF) [22], allows the use of common optical cameras for the reconstruction of 3D objects to improve the architectural representation of tree stems and crown structures as, for example, demonstrated in [23,24]. The reconstruction is based on automatic detection of the same points, i.e., stem or crown features, in subsequent paired images.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Reconstruction of the Tree Stem Surface Using Terrestrial Close-Range Photogrammetry

2016

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Airborne laser scanning (ALS) allows for extensive coverage, but the accuracy of tree detection and form can be limited. Although terrestrial laser scanning (TLS) can improve on ALS accuracy, it is rather expensive and area coverage is limited. Multi-view stereopsis (MVS) techniques combining computer vision and photogrammetry may offer some of the coverage benefits of ALS and the improved accuracy of TLS; MVS combines computer vision research and automatic analysis of digital images from common commercial digital cameras with various algorithms to reconstruct three-dimensional (3D) objects with realistic shape and appearance. Despite the relative accuracy (relative geometrical distortion) of the reconstructions available in the processing software, the absolute accuracy is uncertain and difficult to evaluate. We evaluated the data collected by a common digital camera through the processing software (Agisoft PhotoScan ©) for photogrammetry by comparing those by direct measurement of the 3D magnetic motion tracker. Our analyses indicated that the error is mostly concentrated in the portions of the tree where visibility is lower, i.e., the bottom and upper parts of the stem. For each reference point from the digitizer we determined how many cameras could view this point. With a greater number of cameras we found increasing accuracy of the measured object space point positions (as expected), with a significant positive change in the trend beyond five cameras; when more than five cameras could view this point, the accuracy began to increase more abruptly, but eight cameras or more provided no increases in accuracy. This method allows for the retrieval of larger datasets from the measurements, which could improve the accuracy of estimates of 3D structure of trees at potentially reduced costs.

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Evaluation of a Smartphone App for Forest Sample Plot Measurements

Cited by 43 publications

References 19 publications

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

Accuracy of Reconstruction of the Tree Stem Surface Using Terrestrial Close-Range Photogrammetry

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