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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Simulation Based Approach to Estimating the Three Dimensional Structure of the Harvard Forest with Multi-Modal Remote Sensing
摘要： Tracking carbon as it enters and exits each stage of the carbon cycle is necessary to help build understanding of the cycle's mechanics and its effect on climate. Satellite and airplane-based remote sensing technologies have shown promising results in aiding in human understanding of our planet, including vegetative areas. The Harvard Forest has been studied in various ways over the course of the last century. In particular, synthetic aperture radar, LiDAR, and passive optical sensors have each been used to study the Harvard Forest. Employing a form of data fusion, we present an approach to estimate a forest stand's mean canopy height and biomass for each component tree species while employing minimal ground measurements. We present an approach where a database of simulated forest stands is generated containing both homogeneous stands and heterogeneous stands with up to four tree species present in a given stand. Each simulated stand is compared to an input stand on a number of criteria and a figure of similarity is calculated. In the case that a simulated stand isn't found with a figure of similarity below a set threshold, an iterative process is employed to modify the most similar stand to improve the factor of similarity by modifying the stand's species composition, tree densities, heights, and biomasses. A simulated stand, either pre-existing or developed dynamically will be considered a reasonable representation of the physical forest stand and the 3-D structure of the simulated stand will be reported as an estimate for that of the physical forest stand. This method relies heavily on our sensor simulators, including our fractal-based tree geometry generator, as well as SAR, IfSAR, LiDAR, and Optical simulators. We have previously investigated the ability of our method to differentiate between coniferous and deciduous trees in the same forest stand. We propose to extend this to a maximum of four different tree species, and to validate our approach in the Harvard Forest, a heavily studied region in central Massachusetts.
关键词： Harvard Forest,Forest Parameter Estimation,IfSAR,Heterogeneous Forests,SAR,LiDAR
更新于2025-09-23 15:23:52
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Analysis of NIR spectroscopic data using decision trees and their ensembles
摘要： Decision trees and their ensembles became quite popular for data analysis during the past decade. One of the main reasons for that is current boom in big data, where traditional statistical methods (such as, e.g., multiple linear regression) are not very efficient. However, in chemometrics these methods are still not very widespread, first of all because of several limitations related to the ratio between number of variables and observations. This paper presents several examples on how decision trees and their ensembles can be used in analysis of NIR spectroscopic data both for regression and classification. We will try to consider all important aspects including optimization and validation of models, evaluation of results, treating missing data and selection of most important variables. The performance and outcome of the decision tree-based methods are compared with more traditional approach based on partial least squares.
关键词： Decision trees,Classification and regression trees,Random forests,NIR spectroscopy
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) - Aristi Village, Zagorochoria, Greece (2018.6.10-2018.6.12)] 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) - Quantitative Evaluation of Salient Deep Neural Network Features Using Random Forests
摘要： The Deep Neural Networks and Deep Convolutional Neural Network have the property of providing multi-scale features at different layers of the network. Combination of these large number of features is one of the attributed reasons for the performance of the Neural Network (NN) on vision problems. This work uses Random Forests to identify robust features at various layers of the NN and evaluates the classification performance of these features in isolation. We propose a method for evaluation of parts of an already trained network using the selection by entropy maximization property of the Random Forests. We define measures for saliency in terms of contribution to the final classification, and evaluate the feature saliency. Simultaneously, a measure to identify the imperativeness of network features for classification is also formalized. The experiments made on a Hand dataset and the MNIST dataset, quantitatively validate various intuitions like the discriminatory nature of the outer layer features.
关键词： Random Forests,Feature Evaluation,CNN,Feature Selection
更新于2025-09-23 15:23:52
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On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping
摘要： (1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions of two small mammal intermediate host species, Ellobius tancrei and Microtus gregalis, which facilitate Em transmission in a highly endemic area of Kyrgyzstan. (2) Methods: A series of land cover maps are derived from (a) single-date Landsat Operational Land Imager (OLI) imagery, (b) time-series Sentinel-1 SAR data, and (c) Landsat OLI and time-series Sentinel-1 SAR data in combination. Small mammal distributions are analyzed in relation to the surrounding land cover class coverage using random forests, before being applied predictively over broader areas. A comparison of models derived from the three land cover maps are made, assessing their potential for use in cloud-prone areas. (3) Results: Classification accuracies demonstrated the combined OLI-SAR classification to be of highest accuracy, with the single-date OLI and time-series SAR derived classifications of equivalent quality. Random forest analysis identified statistically significant positive relationships between E. tancrei density and agricultural land, and between M. gregalis density and water and bushes. Predictive application of random forest models identified hotspots of high relative density of E. tancrei and M. gregalis across the broader study area. (4) Conclusions: This offers valuable information to improve the targeting of limited-resource disease control activities to disrupt disease transmission in this area. Time-series SAR derived land cover maps are shown to be of equivalent quality to those generated from single-date optical imagery, which enables application of these methods in cloud-affected areas where, previously, this was not possible due to the sparsity of cloud-free optical imagery.
关键词： Echinococcus multilocularis,random forests,spatial epidemiology,SAR,land cover,Ellobius tancrei,Microtus gregalis,time-series,Sentinel
更新于2025-09-23 15:23:52
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Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests
摘要： Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption.
关键词： super-resolution,coupled dictionary learning,random forests,low-dose CT
更新于2025-09-23 15:22:29
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Improving satellite-based modelling of gross primary production in deciduous broadleaf forests by accounting for seasonality in light use efficiency
摘要： Vegetation gross primary production (GPP), the photosynthetic yields by green plants per unit area per unit time, is a key metric of carbon flux in understanding the land–atmosphere interactions and terrestrial carbon cycles. Satellite-based light use efficiency (LUE) models are valuable methods to retrieve large-scale terrestrial GPP using remote sensing data. As studies have reported that maximum light use efficiency, a key parameter that is often assumed to be constant in the LUE models, there is a need to explore the effects of LUE seasonality on GPP simulation and ways for correction. This study proposes a method based on leaf area index to account for LUE seasonality and applies it to four different light use efficiency models (i.e., the MOD17 algorithm, the vegetation photosynthesis model, the radiation partitioning model, and the vegetation index model) for comparisons. Based on 59 site-years flux tower data from deciduous broadleaf forest sites in the United States, the results show that all models could simulate daily GPP time series well and explain more than 85.0% variance of tower-based GPP. There is, however, a tendency to overestimate GPP during the non-growing season but underestimate GPP during the growing season. By applying the correction function, GPP simulation using the LUE models improved in all experiments as indicated by increased correlation coefficients, the index of agreement and decreased root-mean-square errors. Among all models, the radiation partitioning model achieves the highest correlation coefficients between modelled and observed daily GPP likely because it considers the influences of direct and diffuse radiation partitioning on daily canopy photosynthesis. Our study indicates that satellite-based light use efficiency models could be successfully applied for deriving daily vegetation GPP and potentially producing daily routine satellite products, while considering the effects of LUE seasonality on canopy could help improve significantly the simulation accuracy of daily GPP in phenology.
关键词： seasonality,light use efficiency,satellite-based modelling,gross primary production,deciduous broadleaf forests
更新于2025-09-23 15:21:21
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Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data
摘要： Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present above-ground biomass (AGB) in Alberta, Canada, by taking advantage of a spatially explicit data set derived from a combination of forest inventory data from 1968 plots and space-borne light detection and ranging (lidar) canopy height data. Ten climatic variables, together with elevation, were used for model development and assessment. Four approaches, including spatial interpolation, non-spatial and spatial regression models, and decision-tree-based modeling with random forests algorithm (a machine-learning technique), were compared to find the “best” estimates. We found that the random forests approach provided the best accuracy for biomass estimates. Non-spatial and spatial regression models gave estimates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total AGB stock in Alberta forests was estimated to be 2.26 × 109 Mg (megagram), with an average AGB density of 56.30 ± 35.94 Mg ha?1. At the species level, three major tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.39 × 109 Mg biomass, accounting for nearly 62 % of total estimated AGB. Spatial distribution of biomass varied with natural regions, land cover types, and species. Furthermore, the relative importance of predictor variables on determining biomass distribution varied with species. This study showed that the combination of ground-based inventory data, spaceborne lidar data, land cover classification, and climatic and environmental variables was an efficient way to estimate the quantity, distribution and variation of forest biomass carbon stocks across large regions.
关键词： random forests,remote sensing,lidar,forest biomass,carbon storage,Alberta
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Spatial and Temporal Properties of SMOS Retrieval Over Tropical Forests
摘要： In this paper, retrieval results obtained using the last version (V650) of SMOS level 2 algorithms are tested considering pixels of Africa and South America. Yearly average values of vegetation optical depth are compared against forest height estimates at continental scale. For selected areas of African woody savannah, multitemporal trends of SM and VOD are compared against environmental variables available from Climatic Research Unit data base.
关键词： Soil Moisture,Forests,Vegetation Optical Depth,SMOS
更新于2025-09-23 15:21:01
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Terrestrial laser scanninga??derived canopy interception index for predicting rainfall interception
摘要： Rainfall interception (RI) by forest canopies is an important process in hydrological cycling in forest ecosystems. However, accurately predicting RI is a challenging topic. In this study, a dimensionless descriptor, canopy interception index (CII), for predicting RI was defined. The terrestrial laser scanning was used to estimate CII in four temperate forest types, including Korean pine (Pinus koraiensis) plantation forest (KPF) stands, larch (Larix spp.) plantation forest (LPF) stands, mixed broadleaved forest (MBF) stands and Mongolian oak (Quercus mongolica) forest (MOF) stands. Using the measured RI values over the rainy seasons in 2017 and 2018, CII’s performance for predicting RI was tested, and also compared with several other indices (LAI: leaf area index, PAI: plant area index and ACH: average canopy height). The results indicated that CII was significantly and strongly related with RI for the four forest types together (R2 = 0.79), as well as for an individual forest type (R2 = 0.55~0.63). More importantly, its performance was better than those from LAI (R2 = 0.33~0.43), PAI (R2 = 0.40~0.53) and ACH (R2 = 0.35). All those results demonstrated that CII was an efficient index for accurately predicting RI. The potential applications of CII were also discussed.
关键词： dimensionless descriptor,terrestrial laser scanning,temperate forests,canopy interception index,rainfall interception
更新于2025-09-23 15:19:57
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[IEEE 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) - St. Petersburg and Moscow, Russia (2020.1.27-2020.1.30)] 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) - Investigation of an UWB Antipodal Tapered Slot Antenna Element Based on Substrate Integrated Waveguide in an Antenna Array
摘要： In 2008, the first commercial wave farm came online in Portugal. As with other types of renewable energy, the electricity obtained from waves has the drawback of intermittency. Knowing a few hours ahead how much energy waves will hold can contribute to a better management of the electricity grid. In this work, three types of statistical models have been used to create up to 24-h forecasts of the zonal and meridional components of wave energy flux (WEF) levels at three directional buoys located off the coast in the Bay of Biscay. Each model’s performance has been compared at a 95% confidence level with the simplest prediction (persistence of levels), along with the forecasts provided by the physics-based WAve Modeling (WAM) wave model at the nearest grid point. The results indicate that for forecasting horizons between 3 and roughly 16 h ahead, the statistical models built on random forests (RFs) outperform the rest, including WAM and persistence.
关键词： Applied physics,forecasting,random forests (RFs),wave energy flux (WEF),fluid mechanics,Bay of Biscay
更新于2025-09-23 15:19:57