bluuebunny/crossref_metadata_2025 huggingface dataset:
VIRTUAL LASER SCANNING OF DYNAMIC SCENES CREATED FROM REAL 4D TOPOGRAPHIC POINT CLOUD DATA
Authors: Winiwarter L. , Anders K. , Schröder D. , Höfle B.
Published: May 2022

Abstract. Virtual laser scanning (VLS) allows the generation of realistic point cloud data at a fraction of the costs required for real acquisitions. It also allows carrying out experiments that would not be feasible or even impossible in the real world, e.g., due to time constraints or when hardware does not exist. A critical part of a simulation is an adequate substitution of reality. In the case of VLS, this concerns the scanner, the laser-object interaction, and the scene. In this contribution, we present a method to recreate a realistic dynamic scene, where the surface changes over time. We first apply change detection and quantification on a real dataset of an erosion-affected high-mountain slope in Tyrol, Austria, acquired with permanent terrestrial laser scanning (TLS). Then, we model and extract the time series of a single change form, and transfer it to a virtual model scene. The benefit of such a transfer is that no physical modelling of the change processes is required. In our example, we use a Kalman filter with subsequent clustering to extract a set of erosion rills from a time series of high-resolution TLS data. The change magnitudes quantified at the locations of these rills are then transferred to a triangular mesh, representing the virtual scene. Subsequently, we apply VLS to investigate the detectability of such erosion rills from airborne laser scanning at multiple subsequent points in time. This enables us to test if, e.g., a certain flying altitude is appropriate in a disaster response setting for the detection of areas exposed to immediate danger. To ensure a successful transfer, the spatial resolution and the accuracy of the input dataset are much higher than the accuracy and resolution that are being simulated. Furthermore, the investigated change form is detected as significant in the input data. We, therefore, conclude the model of the dynamic scene derived from real TLS data to be an appropriate substitution for reality.
FAST CONVERGENCE METHOD FOR GLOBAL OPTIMAL 4DOF REGISTRATION
Authors: Abe J. , Tsuji A. , Abe J.
Published: May 2022

Abstract. Four degrees of freedom (4DoF) registration is a class of point cloud registration problems for finding a rigid transformation to align two point clouds under the constraint that the rigid transformation is composed of a three-dimensional (3D) translation and 1D rotation. This constraint is suitable to align scan pairs acquired using modern terrestrial Light Detection and Ranging (LiDAR) scanners, the scans of which can share the direction of gravity as the Z-axis due to such scanners using tripods or internal inclinometers. We propose a fast convergence method for global optimal 4DoF registration. The proposed method consists of (i) our newly developed 4DoF registration model formulated as an optimization problem involving the cylindrical norm to measure the distance between two points, and (ii) a fast convergence algorithm to find a global optimal solution of the model. We experimentally demonstrated that the proposed method reduced the number of iterations to convergence and computation time compared with a current 4DoF registration method, especially when the given scan pairs are similar but cannot be aligned, which often appears in registration of multiple point clouds.
DEEP FEATURE EXTRACTION BASED ON DYNAMIC GRAPH CONVOLUTIONAL NETWORKS FOR ACCELERATED HYPERSPECTRAL IMAGE CLASSIFICATION
Authors: Liu Q. , Dong Y.
Published: May 2022

Abstract. Deep learning has achieved impressive results on hyperspectral images (HSIs) classification. Among them, supervised learning convolutional neural networks (CNNs) and semi-supervised learning graph neural networks (GNNs) are the two main network frameworks. However, 1) the supervised learning CNN faces the problem of high model time complexity as the number of network layers deepens; 2) the semi-supervised learning GNN faces the problem of high spatial complexity due to the computation of adjacency relations. In this paper, a novel dynamic graph convolutional HSI classification method is proposed, which is called dynamic graph convolutional networks (DGCNet). We first obtain two classification features by implementing flattening and global average pooling operation on the results of the convolution layer, which fully exploits the spatial-spectral information contained in the hyperspectral data. Then the dynamic graph convolution module is applied to extract the intrinsic structural information of each patch. Finally, HSI is classified based on spatial, spectral and structural features. DGCNet uses three branches to process multiple features of HSI in parallel and is trained in a supervised learning manner. In addition, DropBlock and label smoothing regularization techniques are applied to further improve the generalization capability of the model. Comparative experiments show that our proposed algorithm is comparable with the state-of-the art supervised learning models in terms of accuracy while also significantly outperforming in terms of time.
HOW FAR SHOULD I LOOK? A NEURAL ARCHITECTURE SEARCH STRATEGY FOR SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGES
Authors: de Paulo M. C. M. , Turnes J. N. , Happ P. N. , Ferreira M. P. , Marques H. A. , Feitosa R. Q.
Published: May 2022

Abstract. Neural architecture search (NAS) is a subset of automated machine learning that tries to find the best neural network to perform a given task. In this article, a network search space is defined and applied to perform the semantic segmentation of satellite imagery. Due to the spatial nature of the data, the search space uses cells that group parallel operations with kernels of different sizes, providing options to accommodate the neighborhood information required to perform a better classification. The architecture search space follows a UNet-like network. The proposed approach uses scaled sigmoid gates, a strategy for network pruning that was adapted to search for the best operations on the cell search space. The architecture achieved by the proposed approach uses wider kernels on lower resolution feature maps, which leads to the interpretation that some pixels required information from pixels farther away than expected. The resulting network was compared to a very similar UNet-like network that only used 3×3 convolutions. The resulting network shows slightly better results on the test set.
AI-DRIVEN MAIZE YIELD FORECASTING USING UNMANNED AERIAL VEHICLE-BASED HYPERSPECTRAL AND LIDAR DATA FUSION
Authors: Dilmurat K. , Sagan V. , Moose S.
Published: May 2022

Abstract. The increased availability of remote sensing data combined with the wide-ranging applicability of artificial intelligence has enabled agriculture stakeholders to monitor changes in crops and their environment frequently and accurately. Applying cutting-edge technology in precision agriculture also enabled the prediction of pre-harvest yield from standing crop signals. Forecasting grain yield from standing crops benefits high-throughput plant phenotyping and agriculture policymaking with information on where crop production is likely to decline. Advanced developments in the Unmanned Aerial Vehicle (UAV) platform and sensor technologies aided high-resolution spatial, spectral, and structural data collection processes at a relatively lower cost and shorter time. In this study, UAV-based LiDAR and hyperspectral images were collected during the growing season of 2020 over a cornfield near Urbana Champaign, Illinois, USA. Hyperspectral imagery-based canopy spectral & texture features and LiDAR point cloud-based canopy structure features were extracted and, along with their combination, were used as inputs for maize yield prediction under the H2O Automated Machine Learning framework (H2O-AutoML). The research results are (1) UAV Hyperspectral imagery can successfully predict maize yield with relatively decent accuracies; additionally, LiDAR point cloud-based canopy structure features are found to be significant indicators for maize yield prediction, which produced slightly poorer, yet comparable results to hyperspectral data; (2) regardless of machine learning methods, integration of hyperspectral imagery-based canopy spectral and texture information with LiDAR-based canopy structure features outperformed the predictions when using a single sensor alone; (3)the H2O-AutoML framework presented to be an efficient strategy for machine learning-based data-driven model building.
AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURES
Authors: Ghosh B. , Garg S. , Motagh M.
Published: May 2022

Abstract. Floods are the most frequent, costliest natural disasters having devastating consequences on people, infrastructure, and the ecosystem. During flood events near real-time satellite imagery has proven to be an efficient management tool for disaster management authorities. However one of the challenges is accurate classification and segmentation of flooded water. The generalization ability of binary segmentation using threshold split-based method, is limited due to the effects of backscatter, geographical area, and time of image collection. Recent advancements in deep learning algorithms for image segmentation has demonstrated excellent potential for improving flood detection. However, there have been limited studies in this domain due to the lack of large scale labeled flood event dataset. In this paper, we present two deep learning approaches, first using a UNet and second, using a Feature Pyramid Network (FPN), both based on a backbone of EfficientNet-B7, by leveraging publicly available Sentinel-1 dataset provided jointly by NASA Interagency Implementation and Advanced Concepts Team, and IEEE GRSS Earth Science Informatics Technical Committee. The dataset covers flood events from Nebraska, North Alabama, Bangladesh, Red River North, and Florence. The performances of both networks were evaluated with multiple training, testing, and validation. During testing, the UNet model achieved the meanIOU score of 75.06% and the FPN model achieved the meanIOU score of 75.76%.
RESEARCH ON NDVI NORMALIZATION METHOD BASED ON GF IMAGES
Authors: Tao Y. , Huang W. , Gan W. , Shen H.
Published: May 2022

Abstract. The existing NDVI products have problems in terms of low spatial resolution and inconsistent values at a large geographical scale. Based on medium and high-resolution multi-source remote sensing data (GF-1 and GF-2 data), this paper normalized NDVI by combining absolute radiation normalization with relative radiation normalization. And the existing relative radiation normalization method, single-scene global linear normalization (SGloLM) method, is improved to adapt to the production of large-range high-resolution NDVI products. Aiming at the problem of obvious mosaic seams when the SGloLM method is applied to multi-scene images, it is mainly improved from two aspects. One is to improve the coefficient solution of the SGloLM algorithm and propose a new method considering the surrounding multi-scene data, the multi-scene global linear model (MGloLM). The other is to incorporate the Maximum Value Composite (MVC) method to synthesize the maximum value of NDVI at different times in a season, to represent the optimal situation of vegetation growth in the current season. In this study, combined experiments of different methods were performed, as well as qualitative and quantitative evaluations. The experimental results show that SGloLM+MVC and the MGloLM+MVC methods can better eliminate the mosaic seams, and their histogram is most similar to the histogram of standard data, and all quantitative evaluation indexes of SGloLM+MVC are optimal (CC=0.7804, MAD=0.0643, RMSE=0.1012).
ANALYSIS OF URBAN EXPANSION CHARACTERISTICS OF YANGTZE RIVER DELTA URBAN AGGLOMERATION BASED ON DMSP/OLS NIGHTTIME LIGHT DATA
Authors: Li M. , Zhang W. , Zheng X. , Xu K.
Published: May 2022

Abstract. Urban agglomeration is the strongest driving force of national economic development. The formation and development of urban agglomeration is an important feature of modern urbanization process, and it is of great significance to study its spatial expansion and development process for regional sustainable development. Based on the DMSP/OLS nighttime light remote sensing data from 2000 to 2010, this paper uses threshold method to extract the urban built-up area of the Yangtze River Delta urban agglomeration. The expansion characteristics of urban built-up area in Yangtze River Delta urban agglomeration are analysed from three aspects: expansion area, expansion intensity and expansion type. The results show that the urban agglomeration in The Yangtze River Delta is experienced a rapid urban built-up area expansion process during the decade. The proportion of urban built-up area increased from 9.95% to 15.46%, the average nighttime light intensity increased from 10.18 to 23.43, and the type of urban built-up area expansion was filling type.
A COMPARATIVE STUDY OF SEVERAL SLFN-BASED CLASSIFICATION ALGORITHMS FOR URBAN AND RURAL LAND USE
Authors: Lin Y. , Xie G. , Zhang T. , Yu J. , Zhang H. , Cai J.
Published: May 2022

Abstract. In the study of urban sustainable development, accurate classification of land use has become an important basis for monitoring urban dynamic changes. Hence it is necessary to develop the appropriate recognition model for urban-rural land use. Although deep learning algorithms have become a research hotspot in image classification tasks in recent years, and many good results have been achieved. But other machine learning algorithms are not going away. Compared deep learning with machine learning, there are some advantages and disadvantages in data dependence, hardware dependence, feature processing, problem solving methods, execution time, and interpretability, etc. Especially in the classification for remote sensing images, the continuous research and development of traditional machine learning algorithms is still of great significance. In this paper, the performances of several SLFN-based classification algorithms were studied and compared, including ELM, RBF K-ELM, mixed K-ELM, A-ELM and SVM. Extreme Learning Machine (ELM) is a new algorithm for single-hidden-layer feedforward neural network (SLFN). It has simple structure, fast speed and is easy to train. In some applications, however, standard ELM is prone to be overfitting and its performance will be affected seriously when outliers exist. In order to explore the performance of ELM and its improved algorithm for urban-rural land use classification, comparative experiments between three improved ELM algorithms (RBF K-ELM, mixed K-ELM and A-ELM), ELM and SVM with image data from several study areas were performed, and the classification accuracy and efficiency were analysed. The results show that the three improved ELM algorithms perform better than the standard ELM and SVM both in overall accuracy and Kappa coefficient. However, it is worth noting that the computation efficiency of RBF K-ELM and mixed K-ELM decreases greatly with larger image, the time cost is much more than other algorithms. Compared with other algorithms, A-ELM has the advantages of higher Overall Accuracy and less classification time.
MONITORING HANGING GLACIER DYNAMICS FROM SAR IMAGES USING CORNER REFLECTORS AND FIELD MEASUREMENTS IN THE MONT-BLANC MASSIF
Authors: Kaushik S. , Leinss S. , Ravanel L. , Trouvé E. , Yan Y. , Magnin F.
Published: May 2022

Abstract. This paper focuses on the monitoring of a small hanging glacier with Synthetic Aperture Radar (SAR) images using a combination of artificial Corner Reflectors (CRs) and dGNSS field measurements. First, to test the performance of the CRs in high-resolution X-band and medium-resolution C-band images acquired by PAZ and Sentinel-1 satellites respectively, a series of tests was performed with two CRs installed in the Chamonix valley. After the confirmation of good visibility in the valley datasets from both satellites, four CRs were installed in summer 2021 on a steep (≈ 52° average slope) hanging glacier on the North face of Aiguille du Midi (3842 ma.s.l., Mont-Blanc massif). After the successful installation on the hanging glacier, all CRs were visible in both sets of images (PAZ and Sentinel-1). Using the CRs as a reference, glacier displacements were estimated in SAR images using complex cross-correlation, and validated by using ground measurements (dGNSS) and theoretical relationships between ice thickness and surface velocity. We observed average summer velocities of ≈ 0.05 m/day at the front of the glacier from field measurements and ≈ 0.04 m/day from SAR displacement estimates. The study shows the potential and limitations of using CRs as a tool for monitoring glaciers in complex topographies.
WEAKLY SUPERVISED LEARNING FOR TREELINE ECOTONE CLASSIFICATION BASED ON AERIAL ORTHOIMAGES AND AN ANCILLARY DSM
Authors: Dvořák J. , Potůčková M. , Treml V.
Published: May 2022

Abstract. Convolutional neural networks (CNNs) effectively classify standard datasets in remote sensing (RS). Yet, real-world data are more difficult to classify using CNNs because these networks require relatively large amounts of training data. To reduce training data requirements, two approaches can be followed – either pretraining models on larger datasets or augmenting the available training data. However, these commonly used strategies do not fully resolve the lack of training data for land cover classification in RS. Our goal is to classify trees and shrubs from aerial orthoimages in the treeline ecotone of the Krkonoše Mountains, Czechia. Instead of training a model on a smaller, human-labelled dataset, we semiautomatically created training data using an ancillary normalised Digital Surface Model (nDSM) and image spectral information. This approach can complement existing techniques, trading accuracy for a larger labelled dataset while assuming that the classifier can handle the training data noise. Weakly supervised learning on a CNN led to 68.99% mean Intersection over Union (IoU) and 81.65% mean F1-score for U-Net and 72.94% IoU and 84.35% mean F1-score for our modified U-Net on a test set comprising over 1000 manually labelled points. Notwithstanding the bias resulting from the noise in training data (especially in the least occurring tree class), our data show that standard semantic segmentation networks can be used for weakly supervised learning for local-scale land cover mapping.
TEMPORALLY TRANSFERABLE MACHINE LEARNING MODEL FOR TOTAL SUSPENDED MATTER RETRIEVAL FROM SENTINEL-2
Authors: Niroumand-Jadidi M. , Bovolo F.
Published: May 2022

Abstract. The empirical (regression-based) models have long been used for retrieving water quality parameters from optical imagery by training a model between image spectra and collocated in-situ data. However, a need clearly exists to examine and enhance the temporal transferability of models. The performance of a model trained in a specific period can deteriorate when applied at another time due to variations in the composition of constituents, atmospheric conditions, and sun glint. In this study, we propose a machine learning approach that trains a neural network using samples distributed in space and time, enabling the temporal robustness of the model. We explore the temporal transferability of the proposed neural network and standard band ratio models in retrieving total suspended matter (TSM) from Sentinel-2 imagery in San Francisco Bay. Multitemporal Sentinel-2 imagery and in-situ data are used to train the models. The transferability of models is then examined by estimating the TSM for imagery acquired after the training period. In addition, we assess the robustness of the models concerning the sun glint correction. The results imply that the neural network-based model is temporally transferable (R2 ≈ 0.75; RMSE ≈ 7 g/m3 for retrievals up to 70 g/m3) and is minimally impacted by the sun glint correction. Conversely, the ratio model showed relatively poor temporal robustness with high sensitivity to the glint correction.
COMBINED FLOODING AND WATER QUALITY MONITORING DURING SHORT EXTREME EVENTS USING SENTINEL 2: THE CASE STUDY OF GLORIA STORM IN EBRO DELTA
Authors: Angelats E. , Soriano-González J. , Fernández-Tejedor M. , Alcaraz C.
Published: May 2022

Abstract. Short extreme events have significant impact on landscape and ecosystems in low-lying and exposed areas such as deltaic systems. In this context, this paper proposes a combined methodology for the mapping and monitoring of the flooding and water quality dynamics of coastal areas under extreme storms from Sentinel 2 imagery. The proposed methodology has been applied in a coastal bay of the Ebro Delta (Catalonia, NE Spain) to evaluate jointly the impact of Gloria storm (January 2020) in land-flooding and water quality. The experimental results show that the Gloria storm had a strong morphological impact and altered the water quality (chl-a) dynamics. The results show a recovery in terms of water quality after some weeks but in contrast the coastal morphology did not show the same degree of resilience. This paper is the first step of an overall goal that is to set the bases in a long term, for a workflow for rapid response and continuous monitoring of storm effects in coastal areas and/or highly valuable ecosystems such as the Ebro Delta.
CLOUD DETECTION BY INTER-BAND PARALLAX AND A-CONTRARIO VALIDATION
Authors: Grompone von Gioi R. , Hessel C. , Dagobert T. , Morel J. M. , de Franchis C.
Published: May 2022

Abstract. The presence of clouds often causes detection errors in automatic optical satellite images analysis. Cloud detection is thus an important pre-processing step. This work exploits the apparent movement of clouds relative to the ground that is observed between bands due to the parallax effect in push-broom satellites. A simple optical flow is computed by correlation of the gradient direction on a local window. Then, a statistical validation test based on the a-contrario theory is used to reject spurious detections. The proposed method achieves performances in the same order of magnitude as the state of the art, while relying on less input information.
SOYBEAN YIELD FORECAST USING DUAL-POLARIMETRIC C-BAND SYNTHETIC APERTURE RADAR
Authors: Hosseini M. , Becker-Reshef I. , Sahajpal R. , Lafluf P. , Leale G. , Puricelli E. , Skakun S. , McNairn H.
Published: May 2022

Abstract. Crop yield forecast is important, but determining productivity is an on-going challenge for agricultural communities including policy makers. An increase in the number of satellites, improved temporal and spatial resolutions and more open data policies, are leading to wider interest by the agricultural sector in exploiting space-based data at moderate spatial resolutions (10–30 m) and varying wavelengths (optical and microwave) for crop yield monitoring. This study evaluated Sentinel-1 dual-polarimetric data to forecast soybean yields one month before the harvest at field scales over a site in central Argentina. Specifically, polarimetric features were extracted from the Sentinel-1 data using the M-Chi decomposition. An Artificial Neural Network (ANN) model was trained using a time series of the single-bounce and volume scattering parameters derived from M-Chi from stacks of data acquired during the growing season. To estimate soybean yield from the ANN model, an innovative iterative retrieval method was developed. This retrieval approach improved the accuracies of the soybean yield forecast delivering a final coefficient of determination (R2) of 0.81 with a root mean square error (RMSE) of 755.81 kg/ha and mean absolute error (MAE) of 581.65 kg/ha. These accuracies demonstrate a high potential of Synthetic Aperture Radar (SAR) data from Sentinel-1 for soybean yield forecast at field scales.
SPATIAL ANALYSIS OF VEGETATION DENSITY CLASSIFICATION IN DETERMINING ENVIRONMENTAL IMPACTS USING UAV IMAGERY
Authors: Jaya N. M. P. , Harmayani K. D. , Widhiawati I. A. R. , Atmaja G. S. , Jaya I M. W.
Published: May 2022

Abstract. Along with remote sensing technology development, vegetation monitoring can be performed using satellite imagery or Unmanned Aerial Vehicle (UAV) data. UAV imagery with a high resolution, between 3–5 cm at an altitude <100 m, is able to present specific land conditions without being affected by the weather. Information related to vegetation density is one of the components in the Environmental Impact Analysis (EIA) study of a proposed project development due to vegetation removal. In this study, information from consumer-grade cameras of a low-cost UAV platform was explored to classify vegetation density using the potential of RGB imagery-based vegetation index (VI). The correlation coefficient (R2) between field observation data and the seven different values of VI demonstrated moderate to strong correlation. The highest linier correlation of 80.16% (R2 = 0.64) was performed by the Green Red Vegetation Index (GRVI). Classification of the vegetation density was established by applying the object-based image analysis method through the combination of supervised machine learning algorithm of Support Vector Machine (SVM) and the GRVI vegetation index. The vegetation density classification consists of very low, low, medium, high, and very high-density classes. The data can be utilized in determining vegetation management efforts from the presence of a proposed project in the EIA study. The use of UAV imagery is considered effective in identifying vegetation density.
CLIMATIC EFFECTS ON ARGENTINA’S TRADITIONAL DRINK – A MACHINE LEARNING BASED YERBA MATE PRODUCTIVITY PREDICTION
Authors: Kemper H. , Schmidt S. , Keller T.
Published: May 2022

Abstract. Yerba mate belongs to the most important agricultural products of Argentina. Due to climate change, together with El Niño and droughts, yields are negatively affected. Drought propagation from precipitation deficits to plant water stress, its impacts and especially its predictability are becoming an emerging field of research. This paper explores how the future yerba mate production in the states of Misiones and Corrientes, north-eastern Argentina, can be projected by using climate variables and satellite data. The applied methodology focuses on a machine learning approach based on multiple linear regression and random forest regression. The results indicate a significant relationship between yerba mate and the NDVI as well as the SOI. The highest yerba mate productivity is expected during weak La Niña events. The methodology of this analysis can successfully predict mate productivity based on satellite and climate data and can also easily be used for further research areas.
ASSESSMENT OF CANOPY AND GROUND HEIGHT ACCURACY FROM GEDI LIDAR OVER STEEP MOUNTAIN AREAS
Authors: Kutchartt E. , Pedron M. , Pirotti F.
Published: May 2022

Abstract. Active remote sensing systems orbiting the Earth are only a small portion of the current constellation of satellites and will increase in number and advance in technology in the future. The launch of the GEDI sensor in December 2018, for an expected life-span period of about 2 years, is a fundamental step of this revolution, as it is the first spaceborne full-waveform lidar specifically designed for measuring the structure of ecosystems, providing information of the vertical profile of forests.Accuracy assessment of GEDI height metrics in the context of an Alpine forest environment in steep terrain scenarios has been conducted in this study. We used discrete return lidar from a recent aerial laser scanner survey as reference to analyse differences of heights of terrain elevation and maximum canopy height of the vegetation detected in each GEDI footprint. The height metrics differences between the discrete lidar and the GEDI data were then analysed to verify any correlation with the following factors: morphology (terrain slope), land cover (land cover type, fraction of canopy cover, vegetation density), GEDI laser beam characteristics (day/night-time acquisition, full power vs coverage laser beam, beam ID, laser sensitivity). Further analysis involved shifting the footprints’ location in 8 different direction and 4 distances to assess the impact of geolocation errors on accuracy and precision.Results show that what most influences accuracy in this study is the terrain slope, very likely linked to the uncertainty of geolocation of the GEDI footprints, suggesting caution in using single GEDI footprints if located in steep environments. Other than slope, terrain height accuracy varies mostly with forest type (conifer vs broadleaves), but not significantly with other factors. Canopy height instead is affected by most factors; high vegetation canopy is overestimated by ∼3 m in GEDI, and underestimated by 3 m over heath and bushes (median difference). Higher sensitivity pulses and night-time pulses provide better accuracy. Laser beams with full power also have better accuracy; beams with id 1000 and 1011 provide the most accurate canopy heights. Shifting the footprint position decreased accuracy except at 15 m and 270° with respect to orbit direction (left-looking).
HYPERBLEND: SIMULATING SPECTRAL REFLECTANCE AND TRANSMITTANCE OF LEAF TISSUE WITH BLENDER
Authors: Riihiaho K. A. , Rossi T. , Pölönen I.
Published: May 2022

Abstract. Remotely sensing vegetation condition and health hazards requires modeling the connection of plants’ biophysical and biochemical parameters to their spectral response. Even though many models exist already, the field suffers from lack of access to program code. In this study, we will assess the feasibility of open-source 3D-modeling and rendering software Blender in simulating hyperspectral reflectance and transmittance of leaf tissue to serve as a base for a more advanced large-scale simulator. This is the first phase of a larger HyperBlend project, which will provide a fully open-source, canopy scale leaf optical properties model for simulating remotely sensed hyperspectral images. Test results of the current HyperBlend model show good agreement with real-world measurements with root mean squared error around 1‰. The program code is available at https://github.com/silmae/ hyperblend.
EVALUATING PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF OPTICAL, SAR, AND AN EXTREME GRADIENT BOOSTING MODEL
Authors: Tamiminia H. , Salehi B. , Mahdianpari M. , Beier C. M. , Johnson L.
Published: May 2022

Abstract. The above-ground biomass (AGB) estimation monitoring provides a powerful tool for the assessment of carbon emission and sequestration. Using remote sensing technique is an environmentally friendly way of biomass estimation. Thus, this paper investigated optical (i.e. Landsat 8 OLI and Sentinel-2), synthetic aperture radar (SAR) (global phased array type L-band SAR (PALSAR/PALSAR-2) and Sentinel-1), and their integration for AGB estimation of the Pack demonstration forest in Warrensburg, NY. Importantly, a LiDAR AGB raster of the study area was used as reference data for training/testing purposes. Then, an extreme gradient boosting (Xgboost) machine learning model was used to predict biomass values. The major goal of this study was to compare the performance of pixel-based and object-based image analysis (OBIA) for the AGB estimation. Results indicated that the object-based approach improved the RMSE of AGB prediction about 6.28 Mg/ha for optical + SAR, 6.17 Mg/ha for SAR, and 5.6 Mg/ha for optical data in comparison to the pixel-based approach. Moreover, the combination of optical and SAR data increased the prediction accuracy regardless of feature extraction approach.
ON THE EFFECT OF DEM QUALITY FOR LANDSLIDE SUSCEPTIBILITY MAPPING
Authors: Karakas G. , Kocaman S. , Gokceoglu C.
Published: May 2022

Abstract. Generating precise and up-to-date landslide susceptibility maps (LSMs) in landslide-prone areas is important to identify hazard potential in the future. The data quality and the method selection affect the accuracy of the LSMs. In this context, the accuracy and precision of the digital elevation models (DEMs) used as input are among the most important performance elements. Therefore, the influence of DEM accuracy and spatial resolution in producing LSMs was investigated here. A high accuracy DEM with 5 m grid spacing produced from aerial photographs and the EU-DEM v1.1 freely accessible from Copernicus Land Monitoring Service with 25 m spatial resolution were used for producing two different LSMs using the Random Forest (RF) method in this study. The RF method has proven success for this purpose. A total of eight conditioning factors, which include topographical and geological features, was used as model input. The landslide inventory was derived with the help of aerial stereo images with 20 cm and 30 cm ground sampling distances. The performances of the LSMs were assessed with receiver operating characteristics (ROC) area under curve (AUC) values. In addition, the results were compared with visual inspection. The results show that although the AUC values obtained from the aerial DEM (0.95) and EU-DEM v1.1 (0.93) were comparable; based on the visual assessments, the LSM obtained from the higher resolution DEM was found more successful in detecting the landslides and thus exhibited better prediction performance.
LANDSLIDE INVENTORY MAPPING FROM LANDSAT-8 NDVI TIME SERIES USING ADAPTIVE LANDSLIDE INTERVAL DETECTION
Authors: Wen T.-H. , Teo T.-A.
Published: May 2022

Abstract. A landslide inventory map is one of the essential sources of geospatial information for land resource management. This study proposes an interval-based landslide detection strategy using satellite images' time series vegetation index. Landslide trends to be abruptly changed with the landscape can be clearly detected using the time series vegetation index. The proposed adaptive landslide interval detection (LID) method is a two stage algorithm. The first stage extracts local extremes and divides the time series to obtain discriminative intervals. Then, a predefined threshold is applied to determine whether a landslide occurs in each interval. The experiment compares the proposed scheme and the traditional time series forest (TSF) algorithm on a pre-labeled dataset. In the comparison of the results obtained from TSF and LID, the validation results show that the proposed LID has better discriminative ability in real scenarios, and the landslide detection rate for large areas reaches 85%. But the TSF can only provide good results on well-defined and unmixing datasets. In addition, LID does not require a large number of training datasets and can be applied to irregular time series with various lengths. In summary, this study demonstrated that the LID global pattern concerned splitting strategy is more effective than TSF random interval segmentation.
EFFECTS OF GF-6 SATELLITE RED-EDGE BANDS ON PEANUT DROUGHT MONITORING
Authors: Zhang Y. , Li P. , Liu T. , He J. , Wang L. , Guo Y. , Zhang H. , Yang X.
Published: May 2022

Abstract. Red-edge band is an indicator band to describe the health status of crops. In order to explore the impact of the red-edge bands on the accuracy of agricultural drought monitoring, this study used GF-6 WFV and Landsat8 to calculate the Red-edge Normalized Difference Vegetation Index (NDVI705), Vogelmann red-edge index 1 (VOG1), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to construct temperature vegetation drought index (TVDI), this index integrated vegetation index and land surface temperature information, the soil relative humidity data and the measured drought grade data were respectively used for correlation analysis and classification accuracy verification of the TVDI results. The results showed that dry edge equation fit with red edge bands were higher than that of non-red edge bands. The dry edge equation based on VOG1 index has the highest fit, with a maximum coefficient determination of 0.92; TVDI constructed by the above vegetation index have significant negative correlation with soil relative humidity. The TVDI based on the VOG1 index has a better correlation with the relative soil humidity, with a maximum correlation coefficient of 0.85; and realized the dynamic monitoring of peanut drought grade, the overall accuracy rate of peanut drought grade monitoring reached 92.59%, and the result of TVDI classification was in good agreement with the actual drought grade measurement result. The red edge bands of GF-6 satellite can effectively improve the accuracy of peanut drought monitoring and better characterize peanut drought information. This research provides a data reference for the application of GF-6 WFV data in agricultural drought monitoring.
COASTAL CHANGE DETECTION ALONG THE FIRE ISLAND, US UNDER THE IMPACT OF HURRICANE SANDY BASED ON LIDAR DATA
Authors: Zhou Y. , Tian S. , Wang H. , Chang C. , Gao Y. , Zhang B. , Wang X.
Published: May 2022

Abstract. Coastal erosion occurring all over the world is a disastrous coastal geological phenomenon. It can be caused by many natural factors such as wind and waves. Hurricane Sandy made landfall on the east coast of the United States at the end of October 2012 and caused severe damage to the economy and ecological environment. In this paper, we employ the ground-based LiDAR which has higher spatial resolution to observe the coastal change detection at Fire Island, New York where the place is severely affected. The research showed that sediment accumulation occurred away from the coast, up to 2.9 meters, erosion close to the coast, up to 9 meters. The total volume of the study area decreased by 78160.96 cubic meters. The coastline retreated by 32.86 meters on average. In addition, a website has been designed to record coastal erosion anytime and anywhere. We hope this study will help people better understand the impact of hurricanes on coastal erosion, enhance our awareness of environmental protection, and provide scientific information for further study.
SUPER-RESOLUTION RESEARCH ON REMOTE SENSING IMAGES IN THE MEGACITY BASED ON IMPROVED SRGAN
Authors: Xin L. , Li Z. , Wang S.
Published: May 2022

Abstract. Remote sensing images of Earth observation with high spatial resolution and high temporal resolution are critical for the application of remote sensing technology in Megacities.With the development of Smart City,more demands which are still difficult to be perfectly satisfied on the spatial resolution and temporal resolution of remote sensing images have been put forward.This paper studies the use of SRGAN which means Super-Resolution using a Generative Adversarial Network (a network structure that uses the loss function considering the perceptual loss and the adversarial loss to improve the spatial resolution of remote sensing images) for super-resolution reconstruction of single remote sensing image.It is able to enhance the spatial resolution of remote sensing images and improve the depth and breadth of remote sensing images.We adjust the reasonable parameters and network structure for our research by analysing the SRGAN in the network architecture, the perceptual loss and the adversarial loss.A super-resolution model is obtained by training with aerial photogrammetry images whose spatial resolution are 0.1 meter in Shanghai.We find the improved SRGAN has a good performance in in remote sensing image super-resolution by comparing the super-resoved images with real high-resolution images in visual perception, spatial position mapping accuracy and chromaticity spatial information. In addition, it is proved that the trained model is also effective to deal with Worldview-2 and SuperView-1 satellite images whose spatial resolution are 0.5 m. Our research shows that our method which can effectively realize the super-resolution of remote sensing images has great potential in the application of remote sensing technology such as urban mapping and changes monitoring.
ANALYSIS: IMPACT OF IMAGE MATCHING METHODS ON JITTER COMPENSATION
Authors: Ye G. , Pan J. , Wang M. , Zhu Y. , Jin S.
Published: May 2022

Abstract. The degradation of image quality caused by satellite jitter has drawn attention, and many researches have illustrated the importance of the jitter compensation. As the essential component of jitter compensation, image matching involves in the determination of jitter processing accuracy. Hence, the impact of imaging matching methods on jitter compensation is explored in this paper. At first, a framework based on imaging matching is built for jitter compensation. Two typical sub-pixel accuracy matching methods (i.e. correlation coefficient and least squares matching, as well as phase correlation matching) could then be served for the framework. The experiments are designed by using multispectral images of GF-1 satellite, and quantitative evaluations show that compared with correlation coefficient and least squares matching, phase matching method makes the accuracy of obtained jitter curve increase with the amplitude error decreasing by more than 0.012 pixel as well as phase error decreasing by more than 0.0119 rad, and makes the quality of restored images improved on both geometry and radiation. It indicates that phase matching method has better performance with respect to the framework of jitter compensation in this paper.
RESEARCH ON QUALITY TESTING AND EVALUATION TECHNOLOGY OF FUNDAMENTAL GEOGRAPHIC INFORMATION DATABASE SYSTEM
Authors: Zhang H. , Shang Y. , Tong X. , Chen J. , Ma W. , Li M. , Lu Y. , Chen H.
Published: May 2022

Abstract. The extensive application of fundamental geographic information data in various industries makes it play an increasingly important role in national economic construction and social development. As a fundamental geographic information database construction project with data and database management system as its core, quality testing and evaluation is the key link and means for measuring database suitability and system application success or failure. This paper proposes a set of fundamental geographic information database system quality model, conducts research on quality testing techniques and methods, and establishes a quality evaluation system, which has been applied in national and provincial basic geographic information database systems.
THE DETECTION OF DISTURBANCE EFFECTS OF NORWAY SPRUCE (PICEA ABIES (L.) KARST.) IN UAV MULTISPECTRAL IMAGERY
Authors: Desaine I. , Bāders E. , Romāns E. , Krišāns O. , Seipulis A. , Jansons Ā.
Published: May 2022

Abstract. This study analyses spectral separability of Norway spruce (Picea abies (L.) Karst.) trees several months after induction of mechanical damage (a simulated wind damage), and one year after the damage – at the beginning of vegetation season and then following a period of drought. Experiment includes sample group of trees (Bent trees) over three plots that underwent static pulling test, therefore simulating survival after storm event. They were compared to a group of trees of the same dimensions that did not undergo static pulling test (Control trees). Spectral reflectance data was collected using unmanned aerial vehicle (UAV) and multispectral sensor. Reflectance of four bands were extracted, converted into indices, and analysed with statistical tests. There were differences in plot response to experiment, as one of the plots failed to show significant difference between groups. Overall, multiple indices proved great results for spectral separation between Control and Bent trees. MACI (Modified Anthocyanin Content Index) was the most consistent in differentiating between groups. Other indices that represent chlorophyll content and photosynthetic activity were also relatively sensitive for stress detection. However, on the contrary to what was expected, period of drought did not seem to affect spectral reflectance of canopies. There was a distinct difference of chlorophyll and anthocyanin content, and photosynthetic activity in sample trees, but these deviations did not manifest in larger scale post drought. Moreover, MACI and GNDVI (Green Normalized Difference Vegetation Index) even reduced mean value gap after the period of low precipitation.
A VOXEL-BASED METHOD FOR THE THREE-DIMENSIONAL MODELLING OF HEATHLAND FROM LIDAR POINT CLOUDS: FIRST RESULTS
Authors: Homainejad N. , Zlatanova S. , Pfeifer N.
Published: May 2022

Abstract. Bushfires are an intrinsic part of the New South Wales’ (NSW) environment in Australia, especially in the Blue Mountains region (11400km2), that is dominated by fire prone vegetation that includes heathland. Many of the Australian native plants in this region are fire-prone and combustible, and many species even require fire to regenerate. The classification of the lateral and vertical distribution of living vegetation is necessary to manage the complexity of bushfires. Currently, interpretation of aerial and satellite images is the prevalent method for the classification of vegetation in NSW. The result does not represent important vegetation structural attributes, such as vegetation height, subcanopy height, and destiny. This paper presents an automated method for the three-dimensional modelling of heathland and important heathland parameters, such as heath shrub height and continuity, and sparse tree and mallee height and density in support of bushfire behaviour modelling. For this study airborne lidar point clouds with a density of 120 points per square meter are used. For the processing and modelling the study is divided into a point cloud processing phase and a voxel-based modelling phase. The point cloud processing phase consists of the normalisation of the height and extraction of the above ground vegetation, while the voxel phase consists of seeded region growing for segmentation, and K-means clustering for the classification of the vegetation into three different canopy layers: a) heath shrubs, b) sparse trees and mallee, c) tall trees.
CONTRASTIVE SELF-SUPERVISED DATA FUSION FOR SATELLITE IMAGERY
Authors: Scheibenreif L. , Mommert M. , Borth D.
Published: May 2022

Abstract. Self-supervised learning has great potential for the remote sensing domain, where unlabelled observations are abundant, but labels are hard to obtain. This work leverages unlabelled multi-modal remote sensing data for augmentation-free contrastive self-supervised learning. Deep neural network models are trained to maximize the similarity of latent representations obtained with different sensing techniques from the same location, while distinguishing them from other locations. We showcase this idea with two self-supervised data fusion methods and compare against standard supervised and self-supervised learning approaches on a land-cover classification task. Our results show that contrastive data fusion is a powerful self-supervised technique to train image encoders that are capable of producing meaningful representations: Simple linear probing performs on par with fully supervised approaches and fine-tuning with as little as 10% of the labelled data results in higher accuracy than supervised training on the entire dataset.