Yunsheng Wang

Possibilities of a Personal Laser Scanning System for Forest Mapping and Ecosystem Services

A professional-quality, personal laser scanning (PLS) system for collecting tree attributes was demonstrated in this paper. The applied system, which is wearable by human operators, consists of a multi-constellation navigation system and an ultra-high-speed phase-shift laser scanner mounted on a rigid baseplate and consisting of a single sensor block. A multipass-corridor-mapping method was developed to process PLS data and a 2,000 m2 forest plot was utilized in the test. The tree stem detection accuracy was 82.6%; the root mean square error (RMSE) of the estimates of tree diameter at breast height (DBH) was 5.06 cm; the RMSE of the estimates of tree location was 0.38 m. The relative RMSE of the DBH estimates was 14.63%. The results showed, for the first time, the potential of the PLS system in mapping large forest plots. Further research on mapping accuracy in various forest conditions, data correction methods and multi-sensoral positioning techniques is needed. The utilization of this system in different applications, such as harvester operations, should also be explored. In addition to collecting tree-level and plot-level data for forest inventory, other possible applications of PLS for forest ecosystem services include mapping of canopy gaps, measuring leaf area index of large areas, documenting and visualizing forest routes feasible for recreation, hiking and berry and mushroom picking.

Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

It is anticipated that many of the future forest mapping applications will be based on three-dimensional (3D) point clouds. A comparison study was conducted to verify the explanatory power and information contents of several 3D remote sensing data sources on the retrieval of above ground biomass (AGB), stem volume (VOL), basal area (G), basal-area weighted mean diameter (Dg) and Lorey’s mean height (Hg) at the plot level, utilizing the following data: synthetic aperture radar (SAR) Interferometry, SAR radargrammetry, satellite-imagery having stereo viewing capability, airborne laser scanning (ALS) with various densities (0.8–6 pulses/m2) and aerial stereo imagery. Laser scanning is generally known as the primary source providing a 3D point cloud. However, photogrammetric, radargrammetric and interferometric techniques can be used to produce 3D point clouds from space- and air-borne stereo images. Such an image-based point cloud could be utilized in a similar manner as ALS providing that accurate digital terrain model is available. In this study, the performance of these data sources for providing point cloud data was evaluated with 91 sample plots that were established in Evo, southern Finland within a boreal forest zone and surveyed in 2014 for this comparison. The prediction models were built using random forests technique with features derived from each data sources as independent variables and field measurements of forest attributes as response variable. The relative root mean square errors (RMSEs) varied in the ranges of 4.6% (0.97 m)–13.4% (2.83 m) for Hg, 11.7% (3.0 cm)–20.6% (5.3 cm) for Dg, 14.8% (4.0 m2/ha)–25.8% (6.9 m2/ha) for G, 15.9% (43.0 m3/ha)–31.2% (84.2 m3/ha) for VOL and 14.3% (19.2 Mg/ha)–27.5% (37.0 Mg/ha) for AGB, respectively, depending on the data used. Results indicate that ALS data achieved the most accurate estimates for all forest inventory attributes. For image-based 3D data, high-altitude aerial images and WorldView-2 satellite optical image gave similar results for Hg and Dg, which were only slightly worse than those of ALS data. As expected, spaceborne SAR data produced the worst estimates. WorldView-2 satellite data performed well, achieving accuracy comparable to the one with ALS data for G, VOL and AGB estimation. SAR interferometry data seems to contain more information for forest inventory than SAR radargrammetry and reach a better accuracy (relative RMSE decreased from 13.4% to 9.5% for Hg, 20.6% to 19.2% for Dg, 25.8% to 20.9% for G, 31.2% to 22.0% for VOL and 27.5% to 20.7% for AGB, respectively). However, the availability of interferometry data is limited. The results confirmed the high potential of all 3D remote sensing data sources for forest inventory purposes. However, the assumption of using other than ALS data is that there exist a high quality digital terrain model, in our case it was derived from ALS.

The Use of a Hand-Held Camera for Individual Tree 3D Mapping in Forest Sample Plots

This paper evaluated the feasibility of a terrestrial point cloud generated utilizing an uncalibrated hand-held consumer camera at a plot level and measuring the plot at an individual-tree level. Individual tree stems in the plot were detected and modeled from the image-based point cloud, and the diameter-at-breast-height (DBH) of each tree was estimated. The detected-results were compared with field measurements and with those derived from the single-scan terrestrial laser scanning (TLS) data. The experiment showed that the mapping accuracy was 88% and the root mean squared error of DBH estimates of individual trees was 2.39 cm, which is acceptable for practical applications and was similar to the results achieved using TLS. The main advantages of the image-based point cloud data lie in the low cost of the equipment required for the data collection, the simple and fast field measurements and the automated data processing, which may be interesting and important for certain applications, such as field inventories by landowners who do not have supports from external experts. The disadvantages of the image-based point cloud data include the limited capability of mapping small trees and complex forest stands.

3D Modeling of Coarse Fluvial Sediments Based on Mobile Laser Scanning Data

High quality sedimentary measurements are required for studying fluvial geomorphology and hydrological processes e.g., flood and river dynamics. Mobile laser scanning (MLS) currently provides the opportunity to achieve high precision measurements of coarse fluvial sediments in a large survey area. Our study aims to investigate the capability of single-track MLS data for individual particle-based sediment modeling. Individual particles are firstly detected and delineated from a digital surface model (DSM) that is generated from the MLS data. 3D MLS points of each detected individual particle are then extracted from the point cloud. The grain size and the sphericity as well as the orientation of each individual particle are estimated based on the extracted MLS points. According to the evaluations conduced in the paper, it is possible to detect and to model sediment particles above 63 mm from a single-track MLS point cloud with a high reliability. The paper further discusses the strength and the challenges of individual particle-based approach for sedimentary measurement.

Autonomous Collection of Forest Field Reference—The Outlook and a First Step with UAV Laser Scanning

A compact solution for the accurate and automated collection of field data in forests has long been anticipated, and tremendous efforts have been made by applying various remote sensing technologies. The employment of advanced techniques, such as the smartphone-based relascope, terrestrial and mobile photogrammetry, and laser scanning, have led to steady progress, thus steering their applications to a practical stage. However, all recent strategies require human operation for data acquisition, either to place the instrument on site (e.g., terrestrial laser scanning, TLS) or to carry the instrument by an operator (e.g., personal laser scanning, PLS), which remained laborious and expensive. In this paper, a new concept of autonomous forest field investigation is proposed, which includes data collection above and inside the forest canopy by integrating an unmanned aircraft vehicle (UAV) with autonomous driving. As a first step towards realizing this concept, the feasibility of automated tree-level field measurements from a mini-UAV laser scanning system is evaluated. A “low-cost” Velodyne Puck LITE laser scanner is applied for the test. It is revealed that, with the above canopy flight data, the detection rate was 100% for isolated and dominant trees. The accuracy of direct measurements on the diameter at breast height (DBH) from the point cloud is between 5.5 and 6.8 cm due to the system and the methodological error propagation. The estimation of DBH from point cloud metrics, on the other hand, showed an accuracy of 2.6 cm, which is comparable to the accuracies obtained with terrestrial surveys using mobile laser scanning (MLS), TLS or photogrammetric point clouds. The estimation of basal area, stem volume and biomass of individual trees could be obtained with less than 20% RMSE, which is adequate for field reference measurements at tree level. Such results indicate that the concept of UAV laser scanning-based automated tree-level field reference collection can be feasible, even though the whole topic requires further research.

Can global navigation satellite system signals reveal the ecological attributes of forests

Abstract Forests have important impacts on the global carbon cycle and climate, and they are also related to a wide range of industrial sectors. Currently, one of the biggest challenges in forestry research is effectively and accurately measuring and monitoring forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. A low-cost crowdsourcing solution is needed for forest inventory to collect forest variables. Here, we propose global navigation satellite system (GNSS) signals as a novel type of observables for predicting forest attributes and show the feasibility of utilizing GNSS signals for estimating important attributes of forest plots, including mean tree height, mean diameter at breast height, basal area, stem volume and tree biomass. The prediction accuracies of the proposed technique were better in boreal forest conditions than those of the conventional techniques of 2D remote sensing. More importantly, this technique provides a novel, cost-effective way of collecting large-scale forest measurements in the crowdsourcing context. This technique can be applied by, for example, harvesters or persons hiking or working in forests because GNSS devices are widely used, and the field operation of this technique is simple and does not require professional forestry skills.

A Novel GNSS Technique for Predicting Boreal Forest Attributes at Low Cost

One of the biggest challenges in forestry research is the effective and accurate measuring and monitoring of forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. This paper presented a novel computational method of low-cost forest inventory using global navigation satellite system (GNSS) signals in a crowdsourcing approach. Statistical features of GNSS signals were extracted from widely available GNSS devices and were used for predicting forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions. The basic evidence of the predictions is the physical correlations between forest variables and the responses of GNSS signals penetrating through the forest. The random forest algorithm was applied to the predictions. GNSS-derived prediction accuracies were comparable with those of the most accurate 2-D remote sensing techniques, and the predictions can be improved further by integration with other publicly available data sources without additional cost. This type of crowdsourcing technique enables the collection of up-to-date forest data at low cost, and it significantly contributes to the development of new reference data collection techniques for forest inventory. Currently, field reference can account for half of the total costs of forest inventory.

Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution

The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral synthesis of geographical location, human motion state and mobility context. A geo-context computing solution consists of a positioning engine, a motion state recognition engine, and a context inference component. In the geo-context concept, the human motion states and mobility context are associated with the geographical location where they occur. A hybrid geo-context computing solution is implemented that runs on a smartphone, and it utilizes measurements of multiple sensors and signals of opportunity that are available within a smartphone. Pedestrian location and motion states are estimated jointly under the framework of hidden Markov models, and they are used in a reciprocal manner to improve their estimation performance of one another. It is demonstrated that pedestrian location estimation has better accuracy when its motion state is known, and in turn, the performance of motion state recognition can be improved with increasing reliability when the location is given. The geo-context inference is implemented simply with the expert system principle, and more sophisticated approaches will be developed.

Tulvariskien hallinta uusilla teknologioilla

Tulvat ovat vahingollisimpia luonnon aiheuttamia tuhoja Euroopassa. Viimeisen kahdenkymmenenviiden vuoden aikana puolentoistasataa tulvaa ovat aiheuttaneet lähes tuhannen ihmishengen menetyksen, puoli miljoonaa ihmistä on joutunut muuttamaan pois kodeistaan, taloudelliset menetykset ovat olleet n. 30 miljardia euroa. Suomessa tulvien aiheuttamat vahingot ovat olleet huomattavasti vähäisempiä, mutta paikalliset vahingot ovat olleet merkittäviä. Kevään 2005 tulvat Ivalossa ja Kittilässä aiheuttivat noin 5 milj. euron vahingot. Rankkasadetulva syksyllä 2007 aiheutti 22 milj. euron vahingot Porissa. Tulvien aiheuttamat rahalliset vahingot yhdessä ihmishenkien menetysten kanssa sekä tulvien lisääntyminen ja esiintymisajankohdan muutokset ovat voimistaneet tulviin kohdistuvaa tutkimusta sekä Suomessa että ulkomailla. Lisääntyneet tulvat ja niihin liittyvät vahingot Euroopassa johtivat EU:n tulvadirektiivin laatimiseen, jossa jäsenvaltioita velvoitetaan kartoittamaan sekä mahdollisten suurtulvien tulva-alueet että tulvista aiheutuvat riskit ja edelleen laatimaan kokonaisvaltaiset hallintasuunnitelmat tulvariskien hallintaan. Tulvadirektiivi antaa viitekehyksen tulvatilanteiden ennakointiin sekä tulvaennustuksiin ja -riskien ennustuksiin Suomessa. Se velvoittaa jäsenvaltion arvioimaan potentiaaliset tulva-alueensa. Suomessa tällaisia alueita on seitsemänkymmentä. Näille alueille on tehty yleispiirteinen tulvavaaraeli tulvan laajuuskartoitus. Tulvavaarakartoituksessa kartoitetaan eri toistuvuusajoilla (esim. tilastollisesti kerran 20 vuodessa tapahtuva tulva) tapahtuvien tulvien laajuudet ja syvyydet. Tulvavaarakartat laaditaan Suomessa vähintään toistuvuusajoille 1/20, 1/50, 1/100, 1/250 ja 1/1000 vuodessa. Tulvavaarakarttojen lisäksi tehdään tulvariskien kartoitus. Tulvariskikartoissa esitetään tulvavaarakarttojen toistuvuuksien mukaisesti esiintyviin tulviin mahdollisesti liittyvät vahingolliset seuraukset mukaan lukien seurauksista mahdollisesti kärsivien asukkaiden määrä, alueella harjoitettavan taloudellisen toiminnan tyyppi ja laitokset, jotka voivat aiheuttaa äkillistä veden tai maaperän pilaantumista tulvatilanteessa ja toisaalta seurauksista mahdollisesti kärsivät suojelualueet. Näiden kartoitusten ja asiantuntija-arvioiden perusteella Maaja metsätalousministeriö nimesi 21 aluetta vuonna 2011, joilla vesistöjen tai meren tulvimisesta aiheutuvat riskit ovat merkittäviä. Näistä alueista 17 sijaitsee sisämaassa vesistöjen varrella ja neljä rannikolla. Merkittäville tulvariskialueille tehdään paraikaa tulvariskien hallinnan suunnitelmia. Tulvariskien hallintasuunnitelmissa on esitettävä tulvariskien hallintatavoitteet ja toimenpiteet niiden saavuttamiseksi. Suunnitelmissa käsitellään kaikkia tulvariskien hallinnan näkökohtia. Niissä keskitytään tulvien ehkäisyyn, suojeluun sekä valmiustoimiin ja otetaan huomioon myös vesistöalueen erityispiirteet. Mahdollisen suurtulvan aiheuttamien vahinkojen on alustavasti arvioitu kohoavan Suomessa jopa 550 milj. euron suuruisiksi.