scholarly journals Evaluation of Different Algorithms for Estimating the Growing Stock Volume of Pinus massoniana Plantations Using Spectral and Spatial Information from a SPOT6 Image

Forests ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 540 ◽  
Author(s):  
Jingjing Zhou ◽  
Zhixiang Zhou ◽  
Qingxia Zhao ◽  
Zemin Han ◽  
Pengcheng Wang ◽  
...  
Keyword(s):  
Spatial Information ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Window Size ◽  
Pinus Massoniana ◽  
Support Vector ◽  
Moving Window ◽  
Growing Stock ◽  
Textural Parameters ◽  
Stock Volume

Precise growing stock volume (GSV) estimation is essential for monitoring forest carbon dynamics, determining forest productivity, assessing ecosystem forest services, and evaluating forest quality. We evaluated four machine learning methods: classification and regression trees (CART), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF), for their reliability in the estimation of the GSV of Pinus massoniana plantations in China’s northern subtropical regions, using remote sensing data. For all four methods, models were generated using data derived from a SPOT6 image, namely the spectral vegetation indices (SVIs), texture parameters, or both. In addition, the effects of varying the size of the moving window on estimation precision were investigated. RF almost always yielded the greatest precision independently of the choice of input. ANN had the best performance when SVIs were used alone to estimate GSV. When using texture indices alone with window sizes of 3 × 5 × 5 or 9 × 9, RF achieved the best results. For CART, SVM, and RF, R2 decreased as the moving window size increased: the highest R2 values were achieved with 3 × 3 or 5 × 5 windows. When using textural parameters together with SVIs as the model input, RF achieved the highest precision, followed by SVM and CART. Models using both SVI and textural parameters as inputs had better estimating precision than those using spectral data alone but did not appreciably outperform those using textural parameters alone.

scholarly journals Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4483
Author(s):  
W. Gareth Rees ◽  
Jack Tomaney ◽  
Olga Tutubalina ◽  
Vasily Zharko ◽  
Sergey Bartalev
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Boreal Forest ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Fundamental Parameter ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

scholarly journals Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4416 ◽  
Author(s):  
Baohua Yang ◽  
Mengxuan Wang ◽  
Zhengxia Sha ◽  
Bing Wang ◽  
Jianlin Chen ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Unmanned Aerial Vehicles ◽  
Nitrogen Content ◽  
Spatial Information ◽  
Vegetation Indices ◽  
Scale Transformation ◽  
Support Vector ◽  
Spatial Feature ◽  
Aerial Vehicles ◽  
Wavelet Features

Nitrogen (N) content is an important basis for the precise management of wheat fields. The application of unmanned aerial vehicles (UAVs) in agriculture provides an easier and faster way to monitor nitrogen content. Previous studies have shown that the features acquired from UAVs yield favorable results in monitoring wheat growth. However, since most of them are based on different vegetation indices, it is difficult to meet the requirements of accurate image interpretation. Moreover, resampling also easily ignores the structural features of the image information itself. Therefore, a spectral-spatial feature is proposed combining vegetation indices (VIs) and wavelet features (WFs), especially the acquisition of wavelet features from the UAV image, which was transformed from the spatial domain to the frequency domain with a wavelet transformation. In this way, the complete spatial information of different scales can be obtained to realize good frequency localization, scale transformation, and directional change. The different models based on different features were compared, including partial least squares regression (PLSR), support vector regression (SVR), and particle swarm optimization-SVR (PSO-SVR). The results showed that the accuracy of the model based on the spectral-spatial feature by combining VIs and WFs was higher than that of VIs or WF indices alone. The performance of PSO-SVR was the best (R2 = 0.9025, root mean square error (RMSE) = 0.3287) among the three regression algorithms regardless of the use of all the original features or the combination features. Our results implied that our proposed method could improve the estimation accuracy of aboveground nitrogen content of winter wheat from UAVs with consumer digital cameras, which have greater application potential in predicting other growth parameters.

scholarly journals Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 296
Author(s):  
Lin Chen ◽  
Chunying Ren ◽  
Bai Zhang ◽  
Zongming Wang
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Training Dataset ◽  
Changbai Mountains ◽  
Support Vector ◽  
Regression Kriging ◽  
Stand Volume ◽  
L Band

Quantifying stand volume through open-access satellite remote sensing data supports proper management of forest stand. Because of limitations on single sensor and support vector machine for regression (SVR) as well as benefits from hybrid models, this study innovatively builds a hybrid model as support vector machine for regression kriging (SVRK) to map stand volume of the Changbai Mountains mixed forests covering 171,450 ha area based on a small training dataset (n = 928). This SVRK model integrated SVR and its residuals interpolated by ordinary kriging. To determine the importance of multi-sensor predictors from ALOS and Sentinel series, the increase in root mean square error (RMSE) of SVR was calculated by removing the variable after the standardization. The SVRK model achieved accuracy with mean error, RMSE and correlation coefficient in –2.67%, 25.30% and 0.76, respectively, based on an independent dataset (n = 464). The SVRK improved the accuracy of 9% than SVR based on RMSE values. Topographic indices from L band InSAR, backscatters of L band SAR, and texture features of VV channel from C band SAR, as well as vegetation indices of the optical sensor were contributive to explain spatial variations of stand volume. This study concluded that SVRK was a promising approach for mapping stand volume in the heterogeneous temperate forests with limited samples.

scholarly journals Deep Learning Spatial-Spectral Classification of Remote Sensing Images by Applying Morphology-Based Differential Extinction Profile (DEP)

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2893
Author(s):  
Nafiseh Kakhani ◽  
Mehdi Mokhtarzade ◽  
Mohammad Javad Valadan Zoej
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Spatial Information ◽  
World View ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Spectral Classification ◽  
Remote Sensing Images ◽  
Complex Objects ◽  
Suggested Approach

Since the technology of remote sensing has been improved recently, the spatial resolution of satellite images is getting finer. This enables us to precisely analyze the small complex objects in a scene through remote sensing images. Thus, the need to develop new, efficient algorithms like spatial-spectral classification methods is growing. One of the most successful approaches is based on extinction profile (EP), which can extract contextual information from remote sensing data. Moreover, deep learning classifiers have drawn attention in the remote sensing community in the past few years. Recent progress has shown the effectiveness of deep learning at solving different problems, particularly segmentation tasks. This paper proposes a novel approach based on a new concept, which is differential extinction profile (DEP). DEP makes it possible to have an input feature vector with both spectral and spatial information. The input vector is then fed into a proposed straightforward deep-learning-based classifier to produce a thematic map. The approach is carried out on two different urban datasets from Pleiades and World-View 2 satellites. In order to prove the capabilities of the suggested approach, we compare the final results to the results of other classification strategies with different input vectors and various types of common classifiers, such as support vector machine (SVM) and random forests (RF). It can be concluded that the proposed approach is significantly improved in terms of three kinds of criteria, which are overall accuracy, Kappa coefficient, and total disagreement.

scholarly journals A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1678
Author(s):  
Panayu Keelawat ◽  
Nattapong Thammasan ◽  
Masayuki Numao ◽  
Boonserm Kijsirikul
Keyword(s):  
Emotion Recognition ◽  
Spatial Information ◽  
Recognition Performance ◽  
Brain Activity ◽  
Selection Effect ◽  
Window Size ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Support Vector

Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods to recognize emotions, including machine learning techniques like convolutional neural network (CNN). Since CNN has shown its potential in generalization to unseen subjects, manipulating CNN hyperparameters like the window size and electrode order might be beneficial. To our knowledge, this is the first work that extensively observed the parameter selection effect on the CNN. The temporal information in distinct window sizes was found to significantly affect the recognition performance, and CNN was found to be more responsive to changing window sizes than the support vector machine. Classifying the arousal achieved the best performance with a window size of ten seconds, obtaining 56.85% accuracy and a Matthews correlation coefficient (MCC) of 0.1369. Valence recognition had the best performance with a window length of eight seconds at 73.34% accuracy and an MCC value of 0.4669. Spatial information from varying the electrode orders had a small effect on the classification. Overall, valence results had a much more superior performance than arousal results, which were, perhaps, influenced by features related to brain activity asymmetry between the left and right hemispheres.

scholarly journals Using hypertemporal Sentinel-1 data to predict forest growing stock volume

2021 ◽  
Author(s):  
Shaojia Ge ◽  
Erkki Tomppo ◽  
Yrjö Rauste ◽  
Ronald E. McRoberts ◽  
Jaan Praks ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Temporal Dynamics ◽  
Support Vector ◽  
List Type ◽  
Prediction Errors ◽  
Growing Stock ◽  
Reduction Techniques ◽  
Combined Images ◽  
Stock Volume

AbstractIn this study, we assess the potential of long time series of Sentinel-1 SAR data to predict forest growing stock volume and evaluate the temporal dynamics of the predictions. The boreal coniferous forests study site is located near the Hyytiälä forest station in central Finland and covers an area of 2,500 km2 with nearly 17,000 stands. We considered several prediction approaches (linear, support vector and random forests regression) and fine-tuned them to predict growing stock volume in several evaluation scenarios. The analyses used 96 Sentinel-1 images acquired over three years. Different approaches for aggregating SAR images and choosing feature (predictor) variables were evaluated. Our results demonstrate considerable decrease in RMSEs of growing stock volume as the number of images increases. While prediction accuracy using individual Sentinel-1 images varied from 85 to 91 m3/ha RMSE (relative RMSE 50-53%), RMSE with combined images decreased to 75.6 m3/ha (relative RMSE 44%). Feature extraction and dimension reduction techniques facilitated achieving the near-optimal prediction accuracy using only 8-10 images. When using assemblages of eight consecutive images, the GSV was predicted with the greatest accuracy when initial acquisitions started between September and January.HighlightsTime series of 96 Sentinel-1 images is analysed over study area with 17,762 forest stands.Rigorous evaluation of tools for SAR feature selection and GSV prediction.Improved periodic seasonality using assemblages of consecutive Sentinel-1 images.Analysis of combining images acquired in “frozen” and “dry summer” conditions.Competitive estimates using calculation of prediction errors with stand-area weighting.

scholarly journals Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors

2021 ◽  
pp. 1-17
Author(s):  
Nils Lindgren ◽  
Håkan Olsson ◽  
Kenneth Nyström ◽  
Mattias Nyström ◽  
Göran Ståhl
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume

scholarly journals Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data

2019 ◽  
Vol 11 (4) ◽  
pp. 384 ◽  
Author(s):  
Erkki Tomppo ◽  
Oleg Antropov ◽  
Jaan Praks
Keyword(s):  
Support Vector ◽  
Detection Accuracy ◽  
Climate Trends ◽  
Growing Stock ◽  
Forest Ecosystem Services ◽  
Multi Temporal ◽  
Snow Damage ◽  
Damage Mapping ◽  
Extent Of Damage ◽  
Stock Volume

Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by disturbances are critical for estimating extent of damage, assessing economical influence and guiding forest management activities. In this study we focus on snow load damage detection from C-Band SAR images. Snow damage is one of the least studied forest damages, which is getting more common due to current climate trends. The study site was located in the southern part of Northern Finland and the SAR data were represented by the time series of C-band SAR scenes acquired by the Sentinel-1 sensor. Methods used in the study included improved k nearest neighbour method, logistic regression analysis and support vector machine classification. Snow damage recordings from a large snow damage event that took place in Finland during late 2018 were used as reference data. Our results showed an overall detection accuracy of 90%, indicating potential of C-band SAR for operational use in snow damage mapping. Additionally, potential of multitemporal Sentinel-1 data in estimating growing stock volume in damaged forest areas were carried out, with obtained results indicating strong potential for estimating the overall volume of timber within the affected areas. The results and research questions for further studies are discussed.

Modelling growing stock volume of forest stands with various ALS area-based approaches

2021 ◽  
Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Forest Stand ◽  
Boosted Regression Trees ◽  
Forest Stands ◽  
Individual Tree ◽  
Growing Stock ◽  
Modelling Techniques ◽  
Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

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