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A Novel GNSS Technique for Predicting Boreal Forest Attributes at Low Cost
Abstract
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.
... The GNSS receiver is another common sensor used in environmental observations, such as in forests [141] and when testing soil moisture [142]. GNSS positioning is widely known to be problematic under forest canopy, e.g., [108], [142], and [143]. ...
... and 26.21-37.92% for the plot-based stem volume and aboveground biomass, respectively, when different combinations of receivers and constellations were used [141]. ...
... The combination of multiple GNSS constellations is expected to provide more accurate statistical features. It will thus further improve predictions of forest attributes as the European Galileo and Chinese BeiDou systems come into full operation [141]. ...
... The GNSS receiver is another common sensor used in environmental observations, such as in forests [141] and when testing soil moisture [142]. GNSS positioning is widely known to be problematic under forest canopy, e.g., [108], [142], and [143]. ...
... and 26.21-37.92% for the plot-based stem volume and aboveground biomass, respectively, when different combinations of receivers and constellations were used [141]. ...
... The combination of multiple GNSS constellations is expected to provide more accurate statistical features. It will thus further improve predictions of forest attributes as the European Galileo and Chinese BeiDou systems come into full operation [141]. ...
Remote sensing-based forest investigation and monitoring have become more affordable and applicable in the past few decades. The current bottleneck limiting practical use of the vast volume of remote sensing data lies in the lack of affordable, reliable, and detailed field references, which are required for necessary calibrations of satellite and aerial data and calibrations of relevant allometric models. Conventional field investigations are mostly limited to a small scale, using a small quantity of observations. Rapid development in close-range remote sensing has been witnessed during the past two decades, i.e., in the constant decrease of the costs, size, and weight of sensors; steady improvements in the availability, mobility, and reliability of platforms; and progress in computational capacity and data science. These advances have paved the way for turning conventional expensive and inefficient manual forest in situ data collections into affordable and efficient autonomous observations.
... Satellite EO data were the main data source across all thematic categories, with only two articles considering data obtained from drones (n = 2| UCXp-Aerial [109] and HR TAI [79]). In Figure 7 the vast majority of articles leveraged optical and multispectral sensors except for seven articles, where SAR data (i.e., ALOS PalSAR and Sentinel-1) [81,91], LiDAR 3D point measurements [118], and combined signals of Navstar GPS and Russian GLONASS Global Navigation Positioning Systems (GNSS) [77,116,117,154] were used. Landsat multispectral sensors (e.g., 5 TM, 7 ETM, or 8 OLI) were exploited in 16 articles (LULC = 10; Air monitoring = 2; Natural Hazards = 3; Vegetation monitoring = 1), with the Landsat-8 OLI dominating among the others. ...
... In particular, using the interferometric coherence between four complex Sentinel-1 images acquired before the event (pre-event coherence, γ pre) and during or after the event (co-event coherence, γ co), flooded regions were detected. Examining the vegetation monitoring articles, GNSS bistatic signals were used in two articles [77,154], enabling the calculation of the signal strength loss (SSL) for estimating the forest canopy. The distributions of SSL (denoted by the carrier-to-noise ratio (C/N 0 )) were estimated, subtracting the two acquired signals, which are retrieved by two independent receivers, over the same period and under the same sky conditions; the first placed in an open-space region, and the second inside the forested area. ...
... Seven articles [69,77,110,[119][120][121]154] provided raw data of temperature, soil moisture at a depth of 0-10 cm below the surface of the ground (Flower Power low-cost sensor developed by Parrot S.A.), aerosols and particulate matter (i.e., PM 2.5 ) distributions over the atmosphere, radiation measurements of radionuclide 137Cs (counting microsievert per minute/hour-µSv/h) and GNSS signals. Reference samples and laboratory analysis were usually performed to calibrate the measurements. ...
Recent advances in Earth Observation (EO) placed Citizen Science (CS) in the highest
position, declaring their essential provision of information in every discipline that serves the SDGs, and the 2050 climate neutrality targets. However, so far, none of the published literature reviews has investigated the models and tools that assimilate these data sources. Following this gap of knowledge, we synthesised this scoping systematic literature review (SSLR) with a will to cover this limitation and highlight the benefits and the future directions that remain uncovered. Adopting the SSLR guidelines, a double and two-level screening hybrid process found 66 articles to meet the eligibility criteria, presenting methods, where data were fused and evaluated regarding their performance, scalability level and computational efficiency. Subsequent reference is given on EO-data,
their corresponding conversions, the citizens’ participation digital tools, and Data Fusion (DF) models that are predominately exploited. Preliminary results showcased a reference in the multispectral satellite sensors, with the microwave sensors to be used as a supplementary data source. Approaches such as the “brute-force approach” and the super-resolution models indicate an effective way to overcome the spatio-temporal gaps and the so far reliance on commercial satellite sensors. Passive crowdsensing observations are foreseen to gain a greater audience as, described in, most cases as a
low-cost and easily applicable solution even in the unprecedented COVID-19 pandemic. Immersive platforms and decentralised systems should have a vital role in citizens’ engagement and training process. Reviewing the DF models, the majority of the selected articles followed a data-driven method with the traditional algorithms to still hold significant attention. An exception is revealed in the smaller-scale studies, which showed a preference for deep learning models. Several studies enhanced their methods with the active-, and transfer-learning approaches, constructing a scalable model. In the end, we strongly support that the interaction with citizens is of paramount importance to achieve
a climate-neutral Earth.
... GNSS has eliminated traditional navigation and become an everyday practice in surveying, as well as in fields connected with the monitoring of environmental resources. The system's abilities have also been noted in forestry, because of its high capacity and simplicity in the effective collection of spatial data ( Liu et al. 2017). The precise measurement of a forest is one of the key elements in accurately estimating forest resources ( Liu et al. 2016). ...
... This is a valuable conclusion, because by possessing knowledge about the dominant species and tree stand volume, one is able to partially reduce positioning errors through appropriate planning of the date of measurement and its location. It is clear that it is not the number of trees that determines signal refraction but their height and stand merchantable volume, which is consistent with the findings of other authors ( Kaartinen et al. 2015;Liu et al. 2017). One of the main undertakings of this study was to determine these characteristics of tree stands that can strongly affect the multipath effect and that are relatively easy to determine directly in the field. ...
The GNSS (Global Navigation Satellite System) receivers are commonly used in forest management in order to determine objects coordinates, area or length assessment and many other tasks which need accurate positioning. Unfortunately, the forest structure strongly limits access to satellite signals, which makes the positioning accuracy much weak comparing to the open areas. The main reason for this issue is the multipath phenomenon of satellite signal. It causes radio waves reflections from surrounding obstacles so the signal do not reach directly to the GNSS receiver's antenna. Around 50% of error in GNSS positioning in the forest is because of multipath effect. In this research study, an attempt was made to quantify the forest stand features that may influence the multipath variability. The ground truth data was collected in six Forest Districts located in different part of Poland. The total amount of data was processed for over 2,700 study inventory plots with performed GNSS measurements. On every plot over 25 forest metrics were calculated and over 25 minutes of raw GNSS observations (1500 epochs) were captured. The main goal of this study was to find the way of multipath quan-tification and search the relationship between multipath variability and forest structure. It was reported that forest stand merchantable volume is the most important factor which influence the multipath phenomenon. Even though the similar geodetic class GNSS receivers were used it was observed significant difference of multipath values in similar conditions.
... Conventional methods of estimating forest volume based on manually measured tree height and diameter at breast height (DBH) often entail significant errors. There is a pressing need to employ more precise scientific approaches for estimating additional parameters related to tree structure across various spatial scales and with higher temporal resolution [1]. ...
In recent times, airborne and terrestrial laser scanning have been utilized to collect point cloud data for forest resource surveys, aiding in the estimation of tree and stand attributes over hectare-scale plots. In this study, an automated approach was devised to estimate the diameter at breast height (DBH) and tree height across the entire sample area, utilizing information acquired from terrestrial laser scanning (TLS) and airborne laser scanning (ULS). Centered around a meticulously managed artificial forest in Northern China, where Mongolian oak and Chinese Scots pine are the predominant species, both TLS and ULS operations were conducted concurrently on each plot. Subsequent to data collection, a detailed processing of the point cloud data was carried out, introducing an innovative algorithm to facilitate the matching of individual tree point clouds from ULS and TLS sources. To enhance the accuracy of DBH estimation, a weighted regression correction equation based on TLS data was introduced. The estimations obtained for the Chinese Scots pine plots showed a correlation of R2 = 0.789 and a root mean square error (RMSE) of 3.2 cm, while for the Mongolian oak plots, an improved correlation of R2 = 0.761 and a RMSE of 3.1 cm was observed between predicted and measured values. This research significantly augments the potential for non-destructive estimations of tree structural parameters on a hectare scale by integrating TLS and ULS technologies. The advancements hold paramount importance in the domain of large-scale forest surveys, particularly in the calibration and validation of aboveground biomass (AGB) estimations.
... Dabove et al. [21] has explained that the GNSS pseudo-range and carrier-phase measurements mobile devices as smartphones and tablets with an Android operating system has transformed the concept of accurate positioning with mobile devices. Liu et al. [22] has showed, a novel computational method of lowcost forest inventory using GNSS signals in a crowdsourcing approach. By this method, to predicted forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions we should extracted the data from widely available GNSS devices by using statistical analysis of GNNS signals. ...
Peat fires cause major environmental problems in Central Kalimantan Province, Indonesia and threaten human health and effect the social-economic sector. The lack of peat fire detection systems is one factor that causing these reoccurring fires. Therefore, in this study, we develop an Android mobile platform application and a web-based application to support the citizen-volunteers who want to contribute wildfires reports, and the decision-makers who wish to collect, visualize, and evaluate these wildfires reports. In this paper, the global navigation satellite system (GNSS) and a global position system (GPS) sensor from a smartphone’s camera, is a useful tool to show the potential fire and smoke’s close-range location. The exchangeable image (EXIF) file image and GPS metadata captured by a mobile phone can store and supply raw observation to our devices and sent it to the data center through global internet communication. This work’s results are the proposed application easy-to-use to monitoring potential peat fire by location and data activity. This paper focuses on developing an application for the mobile platform for peat fire reporting and a web-based application to collect peat fire location for decision-makers. Our main objective is to detect the potential and spread of fire in peatlands as early as possible by utilizing community reports using smartphones.
... They also provide additional valuable information for the variables, such as the feature importance, which gives the possibility to exclude the insignificant variables with minor contribution to the predictions. However, one of the main limitations of ML algorithms is that they are considered as a black box to users, since it is not feasible to examine the internal iterations and perform detailed interpretations of all model components [74,145]. ...
Stem biomass is a fundamental component of the global carbon cycle that is essential for forest productivity estimation. Over the last few decades, Light Detection and Ranging (LiDAR) has proven to be a useful tool for accurate carbon stock and biomass estimation in various biomes. The aim of this study was to investigate the potential of multispectral LiDAR data for the reliable estimation of single-tree total and barkless stem biomass (TSB and BSB) in an uneven-aged structured forest with complex topography. Destructive and non-destructive field measurements were collected for a total of 67 dominant and co-dominant Abies borisii-regis trees located in a mountainous area in Greece. Subsequently, two allometric equations were constructed to enrich the reference data with non-destructively sampled trees. Five different regression algorithms were tested for single-tree BSB and TSB estimation using height (height percentiles and bicentiles, max and average height) and intensity (skewness, standard deviation and average intensity) LiDAR-derived metrics: Generalized Linear Models (GLMs), Gaussian Process (GP), Random Forest (RF), Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost). The results showcased that the RF algorithm provided the best overall predictive performance in both BSB (i.e., RMSE = 175.76 kg and R2 = 0.78) and TSB (i.e., RMSE = 211.16 kg and R2 = 0.65) cases. Our work demonstrates that BSB can be estimated with moderate to high accuracy using all the tested algorithms, contrary to the TSB, where only three algorithms (RF, SVR and GP) can adequately provide accurate TSB predictions due to bark irregularities along the stems. Overall, the multispectral LiDAR data provide accurate stem biomass estimates, the general applicability of which should be further tested in different biomes and ecosystems.
... Existing ground-based experiments and satellite missions dedicated to SMC estimation commonly employ heavy and bulk passive or active sensors, with limited data [41]. A bagging ensemble algorithm, random forest (RF), has been widely used in remote sensing applications to obtain the land cover type [48], the boreal forest attributes [49], precipitation [50], vegetation water content [51], and metal concentration [52], since it is good at capturing nonlinear and complex relationships between inputs and predictors with good estimation results [50,51]. These two typical machine learning methods have great potential for interpreting remote sensing data in the fields of land and sea applications, because they are faster and require fewer training samples while exhibiting better prediction performance, compared to other learning methods [46][47][48]51]. ...
Global Navigation Satellite System-Reflectometry (GNSS-R) as a microwave remote sensing technique can retrieve the Earth's surface parameters using the GNSS reflected signal from the surface. These reflected signals convey the surface features and therefore can be utilized to detect certain physical properties of the reflecting surface such as soil moisture content (SMC). Up to now, a serial of electromagnetic models (e.g., bistatic radar and Fresnel equations, etc.) are employed and solved for SMC retrieval. However, due to the uncertainty of the physical characteristics of the sites, complexity, and nonlinearity of the inversion process, etc., it is still challenging to accurately retrieve the soil moisture. The popular machine learning (ML) methods are flexible and able to handle nonlinear problems. It can dig out and model the complex interactions between input and output and ultimately make good predictions. In this paper, two typical ML methods, specifically, random forest (RF) and support vector machine (SVM), are employed for SMC retrieval from GNSS-R data of self-designed experiments (in situ and airborne). A comprehensive simulated dataset involving different types of soil is constructed firstly to represent the complex interactions between the variables (reflectivity, elevation angle, dielectric constant, and SMC) for the requirement of training ML regression models. Correspondingly, the main task of soil moisture retrieval (regression) is addressed. Specifically, the post-processed data (reflectivity and elevation angle) from sensor acquisitions are used to make predictions by these two adopted ML methods and compared with the commonly used GNSS-R retrieval method (electromagnetic models). The results show that the RF outperforms the SVM method, and it is more suitable for handling the inversion problem. Moreover, the RF regression model built by the comprehensive dataset demonstrates satisfactory accuracy and strong universality, especially when the soil type is not uniform or unknown. Furthermore, the typical task of detecting water/soil (classification) is discussed. The ML algorithms demonstrate a high potential and efficiency in SMC retrieval from GNSS-R data.
... In recent years, with the rapid accumulation of forest biomass data throughout the world, researchers have attempted to improve forest biomass estimations and proposed various stand biomass estimation methods [7,10,[16][17][18]. Studies have shown that stand biomass is closely related to some easily measured stand variables, such as the quadratic mean diameter, average height, and basal area of the stand [7,8,19]. ...
Currently, forest biomass estimation methods at the regional scale have attracted the
greatest attention from researchers, and the development of stand biomass models has become
popular a trend. In this study, a total of 5074 measurements on 1053 permanent sample plots were
obtained in the Eastern Da Xing’an Mountains, and three additive systems of stand biomass
equations were developed. The first additive system (M-1) used stand variables as the predictors
(i.e., stand basal area and average height), the second additive system (M-2) utilized stand volume
as the sole predictor, and the third additive system (M-3) included both stand volume and biomass
expansion and conversion factors (BCEFs) as the predictors. The coefficients of the three model
systems were estimated with nonlinear seemingly unrelated regression (NSUR), while the
heteroscedasticity of the model residuals was solved with the weight function. The jackknifing
technique was used on the residuals, and several statistics were used to assess the prediction
performance of each model. We comprehensively evaluated four stand biomass estimation methods
(i.e., M-1, M-2, M-3 and a constant BCEF (M-4)). Here, we showed that the (1) three additive systems
of stand biomass equations showed good model fitting and prediction performance, (2) M-3
significantly improved the model fitting and performance and provided the most accurate
predictions for most stand biomass components, and (3) the ranking of the four stand biomass
estimation methods followed the order of M-3 > M-2 > M-4 > M-1. Our results demonstrated these
additive stand biomass models could be used to estimate the stand aboveground and belowground
biomass for the major forest types in the Eastern Da Xing’an Mountains, although the most
appropriate method depends on the available data and forest type.
... A recent effort in solving for trajectory errors involves graph optimization of the GNSS-IMU solution of an MLS using only the scanning data and tree detections . Another potential benefit of integration these observations is that GNSS signal may also provide another data source for estimating forest attributes, e.g., biomass, at a plot level (Liu et al., 2017). ...
Accurate assessments of forest resources rely on ground truth data that are collected via in-situ measurements, which are fundamental for all other statistical- and/or remote-sensing-based deductions on quantified forest attributes. The major bottleneck of the current in-situ observation system is that the data collection is time consuming, and, thus, limited in extent, which potentially biases any further inferences made. Consequently, conventional field-data-collection approaches can hardly keep pace with the coverage, scale and frequency required for contemporary and future forest inventories. In-situ measurements from mobile platforms seem to be a promising technique to solve this problem and are estimated at least 10 times faster than static techniques (e.g., terrestrial laser scanning, TLS) at the plot level. However, the mobile platforms are still at the very early stages of development, and it is unclear which three-dimensional (3D) forest measurements the mobile systems can provide and at what accuracy. This study presents a quantitative evaluation of the performance of mobile platforms in a variety of forest conditions and through a comparison with state-of-the-art static in-situ observations. Two mobile platforms were used to collect field data, where the same laser-scanning system was both mounted on top of a vehicle and wore by an operator. The static in-situ observation from TLS is used as a baseline for the evaluation. All point clouds involved were processed through the same processing chain and compared to conventional manual measurement. The evaluation results indicate that the mobile platforms can assess homogeneous forests as well as static observations, but they cannot yet assess heterogeneous forest as required by practical applications. The major challenge is twofold: mobile-data coverage and accuracy. Future research should focus on the robust registration techniques between strips, especially in complex forest conditions, since errors of data registration results in significant impacts on tree attributes estimation accuracy. In cases that the spatial inconstancy cannot be eliminated, attributes estimation in single strips, i.e., the multi-single-scan approach, is an alternative. Meanwhile, operator training deserves attention since the data quality from mobile platforms is partly determined by the operators’ selection of trajectory in the field.
... Lewis et al. [22] modeled the proportion of 3D GNSS fixes, PDOP, and location error using the percent canopy cover and satellite view (to represent terrain obstruction). Newer studies have taken advantage of the correlation between GNSS signal strength and forest stand characteristics by evaluating the potential to predict and map forest parameters using GNSS signals [24,25]. GNSS receiver type (survey-, mapping-, or recreation-grade) also affects the accuracy of position measurements [5,[26][27][28][29][30]. Survey-grade receivers are capable of subcentimeter accuracy in the open and sub-meter accuracy under mature forest conditions [30,31]. ...
Real-time positioning on mobile devices using global navigation satellite system (GNSS) technology paired with radio frequency (RF) transmission (GNSS-RF) may help to improve safety on logging operations by increasing situational awareness. However, GNSS positional accuracy for ground workers in motion may be reduced by multipath error, satellite signal obstruction, or other factors. Radio propagation of GNSS locations may also be impacted due to line-of-sight (LOS) obstruction in remote, forested areas. The objective of this study was to characterize the effects of forest stand characteristics, topography, and other LOS obstructions on the GNSS accuracy and radio signal propagation quality of multiple Raveon Atlas PT GNSS-RF transponders functioning as a network in a range of forest conditions. Because most previous research with GNSS in forestry has focused on stationary units, we chose to analyze units in motion by evaluating the time-to-signal accuracy of geofence crossings in 21 randomly-selected stands on the University of Idaho Experimental Forest. Specifically, we studied the effects of forest stand characteristics, topography, and LOS obstructions on (1) the odds of missed GNSS-RF signals, (2) the root mean squared error (RMSE) of Atlas PTs, and (3) the time-to-signal accuracy of safety geofence crossings in forested environments. Mixed-effects models used to analyze the data showed that stand characteristics, topography, and obstructions in the LOS affected the odds of missed radio signals while stand variables alone affected RMSE. Both stand characteristics and topography affected the accuracy of geofence alerts.
The global navigation satellite system (GNSS)-based passive radar shows potential for permanent maritime surveillance. In this article, the GNSS signals are exploited for vessel target imaging. From the obtained radar image, meaningful information about the vessel, such as its shape, position, length, and orientation can be extracted. In addition, the vessel is observed from different angles by spatially diverse GNSS satellites, and the multistatic geometry enables to enhance the imagery quality. The main drawback of GNSS-based passive radar stays in its limited power budget. And the inaccessible motion makes the noncooperative vessel smeared using conventional radar imaging methods. To address the problems, at first, each bistatic echo over a long observation time is integrated with the range and Doppler (RD) domain after removing the 2-D migrations. The signal-to-noise ratio (SNR) can be increased after the step. Then, with respect to a particular target velocity, the local Cartesian plane is constructed, and the multiple RD maps are projected and combined in the plane to obtain the multistatic image. In view of the inaccessibility of target kinematic parameters, such imaging processing is modeled as an optimization problem, where vessel’s velocity is set as a decision variable and the aim is to minimize the image entropy. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which a well-focused vessel image can be obtained. In May 2021, we have successfully carried out the world’s first BeiDou-based passive radar maritime experiment, and the effectiveness of the proposed method is verified against the experimental data.
The objective of this study was to construct a BeiDou navigation satellite system (BDS)/global positioning system (GPS)-integrated positioning algorithm that meets the accuracy requirement of forest surveys and to analyze its accuracy to provide theoretical and technical support for accurate positioning and navigation in forests. The Quercus variabilis broad-leaved forest in Jiufeng National Forest Park and the Sabina Coniferous forest in Dongsheng Bajia forest farm were selected as the study area. A Sanding T-23 multi-frequency three-constellation receiver and a u-blox NEO-M8T multi-constellation receiving module were used for continuous observation under the forest canopy. Compared with T-23, the u-blox NEO-M8T is much lighter and more flexible in the forest. The BDS/GPS-integrated positioning algorithm for forests was constructed by temporally and spatially unifying the satellite systems and using a reasonable observed value weighting method. Additionally, the algorithm is also written into the RTKLIB software to calculate the three-dimensional (3D) coordinates of the forest observation point in the World Geodetic System 1984 (WGS-84) coordinate system. Finally, the results were compared with the positioning results obtained using GPS alone. The experimental results indicated that, compared with GPS positioning, there were 13–27 visible satellites available for the BDS/GPS-integrated positioning algorithm for forests, far more than the satellites available for the GPS positioning algorithm alone. The Position Dilution of Precision (PDOP) values for the BDS/GPS-integrated positioning ranged from 0.5 to 1.9, lower than those for GPS positioning. The signal noise ratio (SNR) of the BDS/GPS-integrated satellite signals and GPS satellite signals were both in the range of 10–50 dB-Hz. However, because there were more visible satellites for the BDS/GPS-integrated positioning, the signals from the BDS/GPS-integrated satellites were stronger and had a more stable SNR than those from the GPS satellites alone. The results obtained using the BDS/GPS-integrated positioning algorithm for forests had significantly higher theoretical and actual accuracies in the X, Y and Z directions than those obtained using the GPS positioning algorithm. This suggests that the BDS/GPS-integrated positioning algorithm can obtain more accurate positioning results for complex forest environments.
Uydu görüntülerinin, derin öğrenme ile sınıflandırılmasında yaşanılan en büyük zorluk yeterince etiketli veri bulunamamasıdır. Bu sorunun çözümünde kitle kaynak yaklaşımının kullanılabilirliği araştırılmış ve uygulama ile gösterilmiştir. Kitle kaynak yöntemi, kullanıcıların dinamik yapıda veri üretebileceği şekilde hazırlanan web platformu üzerinden uygulanmıştır. Kullanıcılardan, verilen yardım dokümanı ile istenilen sınıfları içerecek şekilde etiketli veri oluşturmaları istenmiştir. Bu veri setlerinin kontrolü kullanıcılar tarafından sınıflara ait çizilmiş poligonlara verilen puan ile tespit edilmiştir. Toplamda 260262 piksel içeren veri seti hazırlanmıştır. Elde edilen veriler orijinal görüntü ile çakıştırılıp görsel olarak da incelenmiştir. Son olarak bu 40 parça görüntü, başarılı sonuçlar verdiği test edilerek üretilen spektral ve uzamsal yapıda CNN modeli ile sınıflandırılmıştır. Ortalamada genel doğruluk değerlerinin %95 üzeri sonuçlar verdiği tespit edilmiştir. Elde edilen sonuçlar kitle kaynak ile bu problemin çözülebileceğini göstermektedir. Kitle kaynağın sınıflandırma sonrası doğruluk değerlendirmede kullanımı aynı arayüzde sunulan modül vasıtasıyla gerçekleştirilmiştir. Bu amaçla, kullanıcılardan rastgele oluşturulmuş 1000 referans noktası için sınıf değerleri girmeleri istenmiştir. Farklı kullanıcılar tarafından 1000 referans noktasına ait girilen değerler üç kere oluşturularak çoğunluk oylaması yöntemiyle sonuç veri seti elde edilmiştir. Sonuçlar, kitle kaynak ile yüksek uzamsal çözünürlükteki multispektral görüntüler için referans noktalarının üretileceğini göstermektedir.
Anahtar Kelimeler:
Mobile CrowdSensing is a powerful sensing paradigm, which provides sufficient social data for cognitive analytics in industrial sensing and industrial manufacturing. Sparse MCS, as a variant, only senses the data in a few subareas, and infers the unsensed ones. Existing works usually assume that the data are linearly spatio-temporal dependent. Moreover, in many cases, users not only require inferring the current data, but also interest in predicting the near future. Facing these problems, we propose a deep learning-enabled industrial sensing and prediction scheme based on Sparse MCS, which consists of two parts: matrix completion and future prediction. We first propose a Deep Matrix Factorization method to retain the non-linear temporal-spatial relationship and perform high-precision matrix completion. Then, we further propose a Nonlinear Auto-Regressive neural network and a Stacked Denoising Auto-Encoder to predict the near future. The experiments on industrial sensing data sets show the effectiveness of our methods.
The data obtained with remote sensing sensors are processed to fuel studies related to the Earth’s resources and environment. Sensor technologies being developed are increasing the diversity and capabilities of remote sensing data. However, these advancements are also multiplying the volume of information enormously, straining resources for processing the data. Meanwhile, the analysis and interpretation of most satellite data have not yet been fully automated, and human intervention is still necessary at certain stages. In addition, recent deep-learning methods that offer solutions to many remote sensing problems require a large amount of labeled data.
Recent research shows that crowdsourcing can contribute to solving such problems. The use of crowdsourcing allows easy solutions to difficult-to-solve geospatial problems and also reduces the time, cost, and effort involved in various applications. For this study, we conducted a systematic literature review (SLR) and examined the use of crowdsourcing in remote sensing science. In addition, we present
a comprehensive online table summarizing crowdsourcing studies in remote sensing for the benefit of readers and future studies. The study shows that the use of crowdsourcing in remote sensing is relatively new. The study also found that remote sensing-related crowdsourcing articles began to be published in 2016 and the number of such articles is increasing
We first develop the network paradigm that is currently dominating the way we think about the internet and introduce varieties of social networking that are being fashioned in interactive web environments. This serves to ground our arguments about Web 2.0 technologies. These constitute ways in which users of web-based services can take on the role of producers as well as consumers of information that derive from such services with sharing becoming a dominant mode of adding value to such data. These developments are growing Web 2.0 from the ground up, enabling users to derive hitherto unknown, hidden and even new patterns and correlations in data that imply various kinds of social networking. We define crowdsourcing and crowdcasting as essential ways in which large groups of users come together to create data and to add value by sharing. This is highly applicable to new forms of mapping. We begin by noting that maps have become important services on the internet with nonproprietary services such as Google Maps being ways in which users can fashion their own functionality. We review various top-down and bottom-up strategies and then present our own contributions in the form of GMapCreator that lets users fashion new maps using Google Maps as a base. We have extended this into an archive of pointers to maps created by this software, which is called MapTube, and we demonstrate how it can be used in a variety of contexts to share map information, to put existing maps into a form that can be shared, and to create new maps from the bottom up using a combination of crowdcasting, crowdsourcing and traditional broadcasting. We conclude by arguing that these developments define a neogeography which is essentially ‘mapping for the masses’.
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 (D-g) and Lorey's mean height (H-g) 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/m(2)) 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 H-g, 11.7% (3.0 cm)-20.6% (5.3 cm) for D-g, 14.8% (4.0 m(2)/ha)-25.8% (6.9 m(2)/ha) for G, 15.9% (43.0 m(3)/ha)-31.2% (84.2 m(3)/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 H-g and D-g, 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 H-g, 20.6% to 19.2% for D-g, 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.
A harvester enables detailed roundwood data to be collected during harvesting operations by means of the measurement apparatus integrated into its felling head. These data can be used to improve the efficiency of wood procurement and also replace some of the field measurements, and thus provide both less costly and more detailed ground truth for remote sensing based forest inventories. However, the positional accuracy of harvester-collected tree data is not sufficient currently to match the accuracy per individual trees achieved with remote sensing data. The aim in the present study was to test the accuracy of various instruments utilizing global satellite navigation systems (GNSS) in motion under forest canopies of varying densities to enable us to get an understanding of the current state-of-the-art in GNSS-based positioning under forest canopies. Tests were conducted using several different combinations of GNSS and inertial measurement unit (IMU) mounted on an all-terrain vehicle (ATV) "simulating" a moving harvester. The positions of 224 trees along the driving route were measured using a total-station and real-time kinematic GPS. These trees were used as reference items. The position of the ATV was obtained using GNSS and IMU with an accuracy of 0.7 m (root mean squared error (RMSE) for 2D positions). For the single-frequency GNSS receivers, the RMSE of real-time 2D GNSS positions was 4.2-9.3 m. Based on these results, it seems that the accuracy of novel single-frequency GNSS devices is not so dependent on forest conditions, whereas the performance of the tested geodetic dual-frequency receiver is very sensitive to the visibility of the satellites. When postprocessing can be applied, especially when combined with IMU data, the improvement in the accuracy of the dual-frequency receiver was significant.
During the past decade in forest mapping and monitoring applications, the ability to acquire spatially accurate, 3D remote-sensing information by means of laser scanning, digital stereo imagery and radar imagery has been a major turning point. These 3D data sets that use single-or multi-temporal point clouds enable a wide range of applications when combined with other geoinformation and logging machine-measured data. New technologies enable precision forestry, which can be defined as a method to accurately determine characteristics of forests and treatments at stand, sub-stand or individual tree level. In precision forestry, even individual tree-level assessments can be used for simulation and optimization models of the forest management decision support system. At the moment, the forest industry in Finland is looking forward to next generation's forest inventory techniques to improve the current wood procurement practices. Our vision is that in the future, the data solution for detailed forest management and wood procurement will be to use multi-source and -sensor information. In this communication, we review our recent findings and describe our future vision in precision forestry research in Finland.
Forests are the dominant terrestrial ecosystem on Earth. We review the
environmental factors controlling their structure and global distribution
and evaluate their current and future trajectory. Adaptations of trees to climate
and resource gradients, coupled with disturbances and forest dynamics,
create complex geographical patterns in forest assemblages and structures.
These patterns are increasingly discernible through new satellite and airborne
observation systems, improved forest inventories, and global ecosystem
models. Forest biomass is a complex property affected by forest distribution,
structure, and ecological processes. Since at least 1990, biomass
density has consistently increased in global established forests, despite increasing
mortality in some regions, suggesting that a global driver such as
elevated CO2 may be enhancing biomass gains. Global forests have also apparently
become more dynamic. Advanced information about the structure,
distribution, and biomass of the world’s forests provides critical ecological
insights and opportunities for sustainable forest management and enhancing
forest conservation and ecosystem services.
Currently, information on forest biomass is available from a mixture of sources, including in-situ measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products. These data tend to be regional or national, based on different methodologies and not easily accessible. One of the few maps available is the Global Forest Resources Assessment (FRA) produced by the Food and Agriculture Organization of the United Nations (FAO 2005) which contains aggregated country-level information about the growing stock, biomass and carbon stock in forests for 229 countries and territories. This paper presents a technique to downscale the aggregated results of the FRA2005 from the country level to a half degree global spatial dataset containing forest growing stock; above/belowground biomass, dead wood and total forest biomass; and above-ground, below-ground, dead wood, litter and soil carbon. In all cases, the number of countries providing data is incomplete. For those countries with missing data, values were estimated using regression equations based on a downscaling model. The downscaling method is derived using a relationship between net primary productivity (NPP) and biomass and the relationship between human impact and biomass assuming a decrease in biomass with an increased level of human activity. The results, presented here, represent one of the first attempts to produce a consistent global spatial database at half degree resolution containing forest growing stock, biomass and carbon stock values. All results from the methodology described in this paper are available online at www.iiasa.ac.at/Research/FOR/.
In the present study we tested the performance of different combinations of airborne laser scanning (ALS) and aerial photograph-based features in the estimation of forest variables. The combinations were subsets of a total of 172 features extracted from the remotely sensed material. The subsets were based on expert judgment or a genetic algorithm (GA). The non-parametric k-nearest neighbour (k-NN) algorithm was applied to derive the estimates. The best performing feature set was obtained after four consecutive steps of GA, each starting with the best features found in the previous step. The best set contained 11 features, 8 of them originating from the ALS data. This set was further weighted with a downhill simplex algorithm, and a relative mean volume RMSE of 27.1% was obtained. The results were slightly worse than in other Finnish ALS studies, most probably due to a larger amount of deciduous trees and greater variation of forests in the study area.
An approach to modeling the regional ionospheric total electron content (TEC) based on spherical cap harmonic analysis is
presented. This approach not only provides a better regional TEC mapping accuracy, but also the capability for ionospheric
model prediction based on spectrum analysis and least squares collocation. Unlike conventional approaches, which predict the
immediate TEC with models using current observations, the spherical cap harmonic approach utilizes models using past observations
to predict a model which will provide future TEC values. A significant advantage in comparison with conventional approaches
is that the spherical cap harmonic approach can be used to predict the long-term TEC with reasonable accuracy. This study
processes a set of GPS data with an observation time span of 1year from two GPS networks in China. The TEC mapping accuracy
of the spherical cap harmonic model is compared with the polynomial model and the global ionosphere model from IGS. The results
show that the spherical cap harmonic model has a better TEC mapping accuracy with smoother residual distributions in both
temporal and spatial domains. The TEC prediction with the spherical cap harmonic model has been investigated for both short-
and long-term intervals. For the short-term interval, the prediction accuracies for the latencies of 1-day, 2-days, and 3-days
are 2.5 TECU, 3.5 TECU, and 4.5 TECU, respectively. For the long-term interval, the prediction accuracy is 4.5 TECU for a
2-month latency.
KeywordsGPS–Ionosphere TEC mapping–Regional ionosphere model–Ionosphere TEC prediction–Spherical cap harmonic analysis
Terrestrial gross primary production (GPP) is the largest global CO2 flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 ± 8 petagrams of carbon
per year (Pg C year−1) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over
40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate
predictions exhibit a large between-model variation of GPP’s latitudinal patterns and show higher spatial correlations between
GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation
response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled
climate–carbon cycle process models.
The National Forest Inventory of Finland (NFI) exploits satellite
image data (Landsat TM) and digital elevation data in addition to ground
measurements. The main purpose is to have estimates for essentially
smaller areas (e.g. for some 1000 hectares) than what is possible with
ground measurements only. The main objective of the study is to test the
applicability of SAR images of ERS-1 together with other information
sources, e.g. HUTSCAT, in estimating forest resources in large areas,
and, if possible, to develop an operative forest inventory system which
utilizes SAR-data
Approaches to deriving forest information from laser scanner data have generally made use of two methods: the area-based and individual tree-based approaches. In this paper, these two methods were evaluated and compared for their abilities to predict forest attributes at the plot level using the same datasets. Airborne laser scanner data were collected over the Evo forest area, southern Finland, with an averaging point density of 2.6 points/m2. Mean height, mean diameter and volume were predicted from laser-derived features for plots (area-based method) or tree height, diameter at breast height and volume for individual trees (individual tree-based method) using random forests technique. To evaluate and compare the two forest inventory methods, the root-mean-squared error (RMSE) and correlation coefficient (R) between the predicted and observed plot-level values were computed. The results indicated that both area-based method (with an RMSE of 6.42% for mean height, 10.32% for mean diameter and 20.90% for volume) and individual tree-based method (with an RMSE of 5.69% for mean height, 10.77% for mean diameter and 18.55% for volume) produced promising and compatible results. Increase in point density is expected to increase the accuracy of the individual tree-based technique more than that of the area-based technique.
We first develop the network paradigm that is currently dominating the way we think about the internet and introduce varieties of social networking that are being
fashioned in interactive web environments. This serves to ground our arguments about Web 2.0 technologies. These constitute ways in which users of web-based services
can take on the role of producers as well as consumers of information that derive from such services with sharing becoming a dominant mode of adding value to such data.
These developments are growing Web 2.0 from the ground up, enabling users to derive hitherto unknown, hidden and even new patterns and correlations in data that imply various kinds of social networking. We define crowdsourcing and crowdcasting as essential ways in which large groups of users come together to create data and to add value by sharing. This is highly applicable to new forms of mapping. We begin by noting that maps have become important services on the internet with nonproprietary services such as Google Maps being ways in which users can fashion their own functionality. We review various top-down and bottom-up strategies and then present our own contributions in the form of GMapCreator that lets users fashion new maps using Google Maps as a base. We have extended this into an
archive of pointers to maps created by this software, which is called MapTube, and we demonstrate how it can be used in a variety of contexts to share map information, to put existing maps into a form that can be shared, and to create new maps from the bottom up using a combination of crowdcasting, crowdsourcing and traditional broadcasting. We conclude by arguing that these developments define a
neogeography which is essentially ‘mapping for the masses’.
Global land cover is one of the essential terrestrial baseline datasets available for ecosystem modeling, however uncertainty remains an issue. Tools such as Google Earth offer enormous potential for land cover validation. With an ever increasing amount of very fine spatial resolution images (up to 50 cm × 50 cm) available on Google Earth, it is becoming possible for every Internet user (including non remote sensing experts) to distinguish land cover features with a high degree of reliability. Such an approach is inexpensive and allows Internet users from any region of the world to get involved in this global validation exercise. The Geo-Wiki Project is a global network of volunteers who wish to help improve the quality of global land cover maps. Since large differences occur between existing global land cover maps, current ecosystem and land-use science lacks crucial accurate data (e.g., to determine the potential of additional agricultural land available to grow crops in Africa), volunteers are asked to review hotspot maps of global land cover disagreement and determine, based on what they actually see in Google Earth and their local knowledge, if the land cover maps are correct or incorrect. Their input is recorded in a database, along with uploaded photos, to be used in the future for the creation of a new and improved hybrid global land cover map.
Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.
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.
http://www.crcnetbase.com/isbn/9781482217988
Stereo images have long been the main practical data source for the high-accuracy retrieval of 3-D information over large areas. However, stereoscopy has been surpassed by laser scanning (LS) techniques in recent years, particularly in forested areas, because the reflection of laser points from object surfaces directly provides 3-D geometric features and because the laser beam has good penetration capacity through forest canopies. In the last few years, image-based point clouds have become a more widely available data source because of advances in matching algorithms and computer hardware. This paper explores the possibility of using consumer cameras for forest field data collection and presents an application of terrestrial image-based point clouds derived from a handheld camera to forest plot inventories. In the experiment, the sample forest plot was photographed in a stop-and-go mode using different routes and camera settings. Five data sets were generated from photographs taken in the field, representing different photographic conditions. The stem detection accuracy ranged between 60% and 84%, and the root-mean-square errors of the estimated diameters at breast height were between 2.98 and 6.79 cm. The performance of image-based point clouds in forest data collection was compared with that of point clouds derived from two LS techniques, i.e., terrestrial LS (the professional level) and personal LS (an emerging technology). The study indicates that the construction of image-based point clouds of forest field data requires only low-cost, low-weight, and easy-to-use equipment and automated data processing. Photographic measurement is easy and relatively fast. The accuracy of tree attribute estimates is close to an acceptable level for forest field inventory but is lower than that achieved with the tested LS techniques.
Forest inventory attributes can be estimated using forest height data derived from remote sensing datasets. In this study, we estimated forest inventory attributes; i.e. stem volume, basal area, and Lorey's height using Tandem-X (TDX) interferometric synthetic aperture radar (InSAR) elevation models and coherence data assisted by ancillary terrain models. It is well known that SAR interferometry is sensitive to the structural changes taking place in the target between image acquisitions; e.g. changes caused by wind in vegetated areas. As regards non-simultaneous InSAR imaging, temporal changes in the target lead to loss of coherence and consequently to digital elevation models of low quality. Therefore, repeat-pass InSAR data have limited use in forest resource mapping. The problem of low coherence in forested areas can be partially avoided by using simultaneous InSAR data acquisition, e.g. the bistatic TDX SAR satellite system, which was launched in June 2010. We processed five interferometric TDX pairs from a test site located in Southern Finland, and collected data from 335 field plots, to study the accuracy of TDX InSAR elevation models and coherence data in forest resource mapping at a resolution of 314 m 2. The SAR-derived elevation models (16m 2 pixel size) were converted to heights above the ground level using a digital terrain model based on airborne laser scanning data. The random forest (RF) method was used to create a model for estimating the forest attributes using remote-sensing-derived metrics as the predictors. The results for accuracy of prediction were the following: the relative RMSE of 32% was obtained for stem volume, 20% for Lorey's height, and 29% for basal area. The forest inventory attributes were derived from the TDX data with an accuracy equivalent with the accuracy of other remote sensing techniques. This result shows the potential of TDX data in robust and cost-effective forest inventory covering large areas.
Terrestrial laser scanning (TLS) has been demonstrated to be an efficient measurement method in plot-level forest inventories. A permanent sample plot in national forest inventories is typically a small area of forest with a radius of approximately 10 m. In practice, whether reference data can be automatically and accurately collected for larger plot sizes is of great interest. It is expensive to collect references in large areas utilizing conventional measurement tools. The application of static TLS is a possible choice but is very challenging due to its lack of mobility. In this letter, a mobile laser scanning (MLS) system was tested and its implications for forest inventories were discussed. The system is composed of a high performance laser scanner, a navigation unit, and a six-wheeled all-terrain vehicle. In this experiment, about 0.4 ha forest area was mapped utilizing the MLS system. The stem mapping accuracy was 87.5%; the root mean square errors of the estimations of the diameter at breast height and the location were 2.36 cm and 0.28 m, respectively. These results indicate that the MLS system has the potential to accurately map large forest plots and further research on mapping accuracy and cost–benefit analyses is needed.
[1] Precise knowledge of the Arctic ionosphere total electron content (TEC) and its variations has scientific relevance due to the unique characteristics of the polar ionosphere. Understanding the Arctic TEC is also important for precise positioning and navigation in the Arctic because the ionosphere is one of the main sources of error in satellite positioning. This study utilized the spherical cap harmonic analysis (SCHA) method to map the Arctic TEC for the most recent solar cycle from 2000 to 2013, and analyzed the distributions and variations of the Arctic TEC at different temporal and spatial scales. Even with different ionosphere conditions during the solar cycle, the results showed that the existing International GNSS Service (IGS) stations are sufficient for mapping the Arctic TEC. The SCHA method provides adequate accuracy and resolution to analyze the spatiotemporal distributions and variations of the Arctic TEC under different ionosphere conditions and to track ionization patches in this polar region (e.g., the ionization event of September 26, 2011). The results derived from the SCHA model were compared to direct observations using the SuperDARN radar. The SCHA method is able to predict the TEC in the long and short terms. This paper presented a long-term prediction with a relative uncertainty of 75% for a latency of one solar cycle and a short-term prediction with errors of ±2.2 TECU, ±3.8 TECU and ±4.8 TECU for a latency of 1, 2 and 3 days, respectively. The SCHA is an effective method for mapping, predicting and analyzing the Arctic TEC.
GNSS-Reflectometry (GNSS-R) is a remote sensing technique which performs bistatic measurements of the earth surface scattering. This paper presents some theoretical simulations of the specular scattering coefficient of a forested area, with the aim of demonstrating the potentiality of GNSS-R in monitoring forest biomass. The study is performed by means of an electromagnetic model developed in the past years and tested over several vegetation covered sites in its active and passive version. Here, after showing a comparison between model results and measurements over a forest site in the monostatic configuration, and after summarizing other previous validations, the extension to the specular configuration, typical of GNSS-R systems, will be presented. Namely, simulations are carried out at circular polarization and a sensitivity analysis of the received power in the specular configuration to some soil and forest parameters is shown.In the GNSS-R configuration, the theoretical response of vegetation shows a decreasing trend with increasing biomass, due to the increasing attenuation by the plant canopy which reduces the coherent scattering from the soil. The latter, however, remains higher than incoherent scattering even when forest biomass is large, especially at RL polarization and low incidence angle. Consequently the magnitude of the received power is sensitive to the forest biomass without exhibiting the typical saturation of radar backscattering measurements, and it may thus allow biomass retrieval.
Experiences from Nordic countries and Canada have shown that the retrieval of the stem volume and mean tree height of a tree or at stand level from laser scanner data performs as well as, or better than, photogrammetric methods, and better than other remote sensing methods. This paper reviews the methods of small‐footprint airborne laser scanning for extracting forest inventory data, mainly in the boreal forest zone. The methods are divided into the following categories: extraction of terrain and canopy height model; feature extraction approaches (canopy height distribution and individual‐tree‐based techniques, techniques based on the synergetic use of aerial images and lidar, and other new approaches); tree species classification and forest growth using laser scanner; and the use of intensity and waveform data in forest information extraction. Despite this, the focus is on methods, some review of quality obtained, especially in the boreal forest area, is included. Several recommendations for future research are given to foster the methodology development.
The detection and repair of the cycle slip or gross error is a key step for high precision global positioning system (GPS) carrier phase navigation and positioning due to interruption or unlocking of GPS signal. A number of methods have been developed to detect and repair cycle slips in the last two decades through cycle slip linear combinations of available GPS observations, but such approaches are subject to the changing GPS sampling and complex algorithms. Furthermore, the small cycle slip and gross error cannot be completely repaired or detected if the sampling is quite longer under some special observation conditions, such as Real Time Kinematic (RTK) positioning. With the development of the GPS modernization or Galileo system with three frequencies signals, it may be able to better detect and repair the cycle slip and gross error in the future. In this paper, the cycle slip and gross error of GPS carrier phase data are detected and repaired by using a new combination of the simulated multi-frequency GPS carrier phase data in different conditions. Results show that various real-time cycle slips are completely repaired with a gross error of up to 0.2 cycles.
This is a review of the latest developments in different fields of remote sensing for forest biomass mapping. The main fields of research within the last decade have focused on the use of small footprint airborne laser scanning systems, polarimetric synthetic radar interferometry and hyperspectral data. Parallel developments in the field of digital airborne camera systems, digital photogrammetry and very high resolution multispectral data have taken place and have also proven themselves suitable for forest mapping issues. Forest mapping is a wide field and a variety of forest parameters can be mapped or modelled based on remote sensing information alone or combined with field data. The most common information required about a forest is related to its wood production and environmental aspects. In this paper, we will focus on the potential of advanced remote sensing techniques to assess forest biomass. This information is especially required by the REDD (reducing of emission from avoided deforestation and degradation) process. For this reason, new types of remote sensing data such as fullwave laser scanning data, polarimetric radar interferometry (polarimetric systhetic aperture interferometry, PolInSAR) and hyperspectral data are the focus of the research. In recent times, a few state-of-the-art articles in the field of airborne laser scanning for forest applications have been published. The current paper will provide a state-of-the-art review of remote sensing with a particular focus on biomass estimation, including new findings with fullwave airborne laser scanning, hyperspectral and polarimetric synthetic aperture radar interferometry. A synthesis of the actual findings and an outline of future developments will be presented.
Recent advances in developing new airborne instruments and space-borne missions and in SAR technology, especially in interferometry and coherence estimation, have roused questions: can such new SAR data be utilized in operational forest inventory? What is the accuracy of different satellite data for forest inventory? This paper verifies the explanatory power and information contents of several remote sensing data sources on the retrieval of stem volume, basal area, and mean height, utilizing the following data: Landsat TM, Spot PAN and XS, ERS-1/2 PRI and SLC (coherence estimation), airborne data from imaging spectrometer AISA, radar-derived forest canopy profiles (obtained with HUTSCAT), and aerial photographs. Ground truth data included three different sets ranging from conventional forest inventory data to intensive field checking where one man-day was spent for assessing one stand. Multivariate and neural network methods were applied in data analysis. The results suggested that (1) radar-derived stand profiles obtained with 100 m spacing was the most accurate data source in this comparison and was of equivalent accuracy with conventional forest inventory for mean height and stem volume estimation, (2) aerial photographs (scale 1 : 20,000) gave comparable results with the imaging spectrometer AISA, (3) the satellite images used for the estimation in the decreasing explanation power were Spot XS, Spot PAN, Landsat TM, ERS SAR coherence, JERS SAR intensity images (PRI); and ERS SAR intensity images (PRI). It appears that optical images still include more information for forest inventory than radar images, (4) from all satellite radar methods, the coherence technique seemed to be superior to other methods.
This paper depicts an approach for predicting individual tree attributes, i.e., tree height, diameter at breast height (DBH) and stem volume, based on both physical and statistical features derived from airborne laser-scanning data utilizing a new detection method for finding individual trees together with random forests as an estimation method. The random forests (also called regression forests) technique is a nonparametric regression method consisting of a set of individual regression trees. Tests of the method were performed, using 1476 trees in a boreal forest area in southern Finland and laser data with a density of 2.6 points per m2. Correlation coefficients (R) between the observed and predicted values of 0.93, 0.79 and 0.87 for individual tree height, DBH and stem volume, respectively, were achieved, based on 26 laser-derived features. The corresponding relative root-mean-squared errors (RMSEs) were 10.03%, 21.35% and 45.77% (38% in best cases), which are similar to those obtained with the linear regression method, with maximum laser heights, laser-estimated DBH or crown diameters as predictors. With random forests, however, the forest models currently used for deriving the tree attributes are not needed. Based on the results, we conclude that the method is capable of providing a stable and consistent solution for determining individual tree attributes using small-footprint laser data.
In this paper we review recent developments of crowdsourcing geospatial data. While traditional mapping is nearly exclusively coordinated and often also carried out by large organisations, crowdsourcing geospatial data refers to generating a map using informal social networks and web 2.0 technology. Key differences are the fact that users lacking formal training in map making create the geospatial data themselves rather than relying on professional services; that potentially very large user groups collaborate voluntarily and often without financial compensation with the result that at a very low monetary cost open datasets become available and that mapping and change detection occur in real time. This situation is similar to that found in the Open Source software environment.We shortly explain the basic technology needed for crowdsourcing geospatial data, discuss the underlying concepts including quality issues and give some examples for this novel way of generating geospatial data. We also point at applications where alternatives do not exist such as life traffic information systems. Finally we explore the future of crowdsourcing geospatial data and give some concluding remarks.
The comparison of results of different forest studies is extremely
difficult due to differences in test sites and studied stand
characteristics, validation procedures, parameters used as an evaluation
criteria, selection of stands, and the number of predictors used to name
but a few. All these account for a large variation of the obtained
accuracy. Additionally, in most reports inadequate information is given
to convert statistically results from one study to the other. Since very
few studies, such as Hyyppä e t al. (2000), exist
where various remote sensing data sources and methods are verified in
the same test site, much of the knowledge of the applicability of
various data sources and methods for forest inventory has to be obtained
by studies carried out in different tests sites. However, there is a
single parameter, stand size, affecting strongly comparisons of forestry
inventory results. The effect of stand size on the accuracy of remote
sensing-based standwise forest inventory has not been reported
extensively. The most dramatic changes occur at the level where stands
are small. Not surprisingly, stand size has been successfully utilized
as an auxiliary parameter in some studies. This paper describes how the
accuracy of estimation is influenced by the stand size. Both spaceborne
and airborne data are used in order to show that the effect is not just
based on large pixel sizes or the effects of border pixels in spaceborne
data. The accuracy of the following remote sensing data, SPOT Pan and
XS, Landsat TM, ERS-1/2 SAR PRI and SLC, and airborne data from imaging
spectrometer (AISA) is verified as a function of stand size in the range
1 to 20 ha. The paper presents curves that assist in converting results
from one stand size to another and compares results of some studies in
different test sites. Stand size seems to explain most of the
variability of the results; however, for detailed comparison, more
carefully described results are needed. Recommendations to design future
forest studies are given in order to help the statistical conversion of
results from one study to another
GNSS Market Report: Issue 3
- Report Gnss Market
- Gnss Market Report
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