bluuebunny/crossref_metadata_2025 huggingface dataset:
Joint analysis of convective structure from the APR-2 precipitation radar and the DAWN Doppler wind lidar during the 2017 Convective Processes Experiment (CPEX)
Authors: Turk F. Joseph , Hristova-Veleva Svetla , Durden Stephen L. , Tanelli Simone , Sy Ousmane , Emmitt G. David , Greco Steve , Zhang Sara Q.
Published: August 2020

Abstract. The mechanisms linking convection and cloud dynamical
processes are major factors in much of the uncertainty in both weather and
climate prediction. Further constraining the uncertainty in convective cloud
processes linking 3-D air motion and cloud structure through models and
observations is vital for improvements in weather forecasting and
understanding limits on atmospheric predictability. To date, there have been
relatively few airborne observations specifically targeted for linking the
3-D air motion surrounding developing clouds to the subsequent development
(or nondevelopment) of convective precipitation. During the May–June 2017
Convective Processes Experiment (CPEX), NASA DC-8-based airborne
observations were collected from the JPL Ku- and Ka-band Airborne Precipitation
Radar (APR-2) and the 2 µm Doppler Aerosol Wind (DAWN) lidar during
approximately 100 h of flight. For CPEX, the APR-2 provided the vertical air
motion and structure of the cloud systems in nearby precipitating regions
where DAWN is unable to sense. Conversely, DAWN sampled vertical wind
profiles in aerosol-rich regions surrounding the convection but is unable
to sense the wind field structure within most clouds. In this paper,
the complementary nature of these data are presented from the 10–11 June
flight dates, including the APR-2 precipitation structure and Doppler wind
fields as well as adjacent wind profiles from the DAWN data.
Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17
Authors: Wheeler Kathryn I. , Dietze Michael C.
Published: August 2020

Abstract. Monitoring leaf phenology allows for tracking the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Networks of finite-scale remote sensing, such as the PhenoCam Network, provide valuable information on phenological state at high temporal resolution, but have limited coverage. To more broadly remotely sense phenology, satellite-based data that has lower temporal resolution has primarily been used (e.g., 16-day MODIS NDVI product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and -17) allow the monitoring of NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we examine the current capacity of this new data to measure the phenology of deciduous broadleaf forests for the first two full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the start, middle, and end of season transition dates to those obtained from PhenoCam and MODIS 16-day NDVI and EVI products. Compared to the MODIS indices, GOES was more correlated with PhenoCam at the start and middle of spring, but had a larger bias (3.35 ± 0.03 days later than PhenoCam) at the end of spring. Satellite-based autumn transition dates were mostly uncorrelated with those of PhenoCam. PhenoCam data produced significantly more certain (all p-values
A high-resolution reanalysis of global fire weather from 1979 to 2018 – overwintering the Drought Code
Authors: McElhinny Megan , Beckers Justin F. , Hanes Chelene , Flannigan Mike , Jain Piyush
Published: August 2020

Abstract. We present a global high-resolution calculation of the Canadian Fire Weather
Index (FWI) System indices using surface meteorology from the ERA5 HRES
reanalysis for 1979–2018. ERA5 HRES represents an improved dataset compared
to several other reanalyses in terms of accuracy, as well as spatial and
temporal coverage. The FWI calculation is performed using two different
procedures for setting the start-up value of the Drought Code (DC) at the
beginning of the fire season. The first procedure, which accounts for the
effects of inter-seasonal drought, overwinters the DC by adjusting the fall
DC value with a fraction of accumulated overwinter precipitation. The second
procedure sets the DC to its default start-up value (i.e. 15) at the start
of each fire season. We validate the FWI values over Canada using station
observations from Environment and Climate Change Canada and find generally
good agreement (mean Spearman correlation of 0.77). We also show that
significant differences in early season DC and FWI values can occur when the
FWI System calculation is started using the overwintered versus default DC
values, as is highlighted by an example from 2016 over North America. The
FWI System moisture codes and fire behaviour indices are made available for
both versions of the calculation at https://doi.org/10.5281/zenodo.3626193 (McElhinny et al., 2020), although
we recommend using codes and indices calculated with the overwintered DC,
unless specific research requirements dictate otherwise.
Bias and error in modelling thermochronometric data: resolving a potential increase in Plio-Pleistocene erosion rate
Authors: Willett Sean D. , Herman Frederic , Fox Matthew , Stalder Nadja , Ehlers Todd A. , Jiao Ruohong , Yang Rong
Published: August 2020

Abstract. Thermochronometry provides one of few methods to quantify rock exhumation rate and history, including potential changes in exhumation rate. Thermochronometric ages can resolve rates, accelerations, and complex histories by exploiting different closure temperatures and path lengths using data distributed in elevation. We investigate how the resolution of an exhumation history is determined by the distribution of ages and their closure temperatures through an error analysis of the exhumation history problem. We define the sources of error, defined in terms of resolution, model error and methodological bias in the inverse method used by Herman et al. (2013) which combines data with different closure temperatures and elevations. The error analysis provides a series of tests addressing the various types of bias, including addressing criticism that there is a tendency of thermochronometric data to produce a false inference of faster erosion rates towards the present day because of a spatial correlation bias (Schildgen et al., 2018). Tests based on synthetic data demonstrate that the inverse method used by Herman et al. (2013) has no methodological or model bias towards increasing erosion rates. We do find significant resolution errors with sparse data, but these errors are not systematic, tending rather to leave inferred erosion rates at or near a Bayesian prior. To explain the difference in conclusions between our analysis and that of Schildgen et al. (2018), we examine their paper and find that their model tests used an incorrect geotherm calculation, invalidating their models. We also found that Schildgen et al. (2018) applied a biased operator to the results of Herman et al. (2013) thereby distorting the original results producing a bias that was falsely attributed to the original inverse model. Our reanalysis and interpretation show that the original results of Herman et al. (2013) are correct and there is no evidence for a systematic bias.
Evaluation of the capacities of a field absolute quantum gravimeter (AQG#B01)
Authors: Cooke Anne-Karin , Champollion Cédric , Le Moigne Nicolas
Published: August 2020

Abstract. Quantum gravimeters are a promising new development allowing for continuous, high-frequency absolute gravity monitoring while remaining user-friendly and transportable. In this study, we present experiments carried out to assess the capacity of the AQG#B01 in view of future deployment as a field gravimeter for hydro-geophysical applications. The AQG#B01 is the field version follow-up of the AQG#A01 portable absolute quantum gravimeter developed by MuQuans. We assess the instrument's performance in terms of stability (absence of instrumental drift), sensitivity in relation to other gravimeters, and hydrogeological mass changes. We discuss the observations concerning the accuracy of the AQG#B01 in comparison with a state-of-the-art absolute gravimeter (Micro-g-LaCoste, FG5#228). Repeatability is tested by instrument displacement between close-by measurement positions. We report the repeatability to be better than 50 nm s−2. No significant instrumental drift was observed over several weeks of measurement. This study furthermore investigates whether changes of instrument tilt and external temperature and combination of both, which are likely to occur during field campaigns, influence the measurement of gravitational attraction. We repeatedly tested external temperatures between 20 and 30 °C and did not find any significant effect. As an example of a geophysical signal, a 100 nm s−2 gravity change is detected with the AQG#B01 after a rainfall event at the Larzac geodetic observatory (Southern France). The data agreed with the gravity changes measured with a superconducting relative gravimeter (GWR, iGrav#002) and the expected gravity change simulated as an infinite Bouguer slab approximation. We close with operational recommendations for potential users and discuss specific possible future field applications. While not claiming completeness, we nevertheless present the first characterisation of a quantum gravimeter carried out by future users. Crucial criteria for the assessment of its suitability in field applications have been investigated and are complemented with a discussion of further necessary experiments.
Development of a revised method for indicators of hydrologic alteration for analyzing the cumulative impacts of cascading reservoirs on flow regime
Authors: Zhou Xingyu , Huang Xiaorong , Zhao Hongbin , Ma Kai
Published: August 2020

Abstract. The impacts of reservoirs, especially multiple reservoirs, on the
flow regimes and ecosystems of rivers have received increasing attention.
The most widely used metrics to quantify the characteristics of flow regime
alterations are the indicators of hydrologic alteration (IHAs) which include
33 parameters. Due to the difference in the degree of alteration and the
intercorrelation among IHA parameters, the conventional method of evaluating
IHA parameters that assigns the same weight to each indicator is obviously
inadequate. A revised IHA method is proposed by utilizing the projection
pursuit (PP) and real-coded accelerated genetic algorithm (RAGA). Data
reliability is analyzed by using the length of record (LOR) method. The
projection values reflecting the comprehensive characteristics of the
evaluation parameters are calculated. Based on these methods, a scientific
and reliable evaluation of the cumulative impacts of cascading reservoirs on
the flow regime was made by examining the Jinsha River. The results showed
that with the increase in the number of reservoirs, the alteration degrees
of IHA parameters gradually increased in groups 1, 2, 3 and 4 but
decreased in group 5 (each group addresses the magnitude, timing, frequency,
duration and rate of change in turn), and the flow duration curves showed a
declining trend at the high-flow part and an increasing trend at the low-flow part. The flow regime alteration of the outlet section was more stable
than before. This change had a negative impact on downstream fish
reproduction and ecological protection. An attempt at ecological regulation
was made to simulate the natural rising process of water, and four major
Chinese carps have a positive response to the flood peak process caused by
manual regulation.
POLARIMETRIC SAR DATA FROM SENTINEL-1A APPLIED TO EARLY CROP CLASSIFICATION
Authors: Oldoni L. V. , Prudente V. H. R. , Diniz J. M. F. S. , Wiederkehr N. C. , Sanches I. D. , Gama F. F.
Published: August 2020

Abstract. This paper aims to map crops in two Brazilian municipalities, Luís Eduardo Magalhães (LEM) and Campo Verde, using dual-polarimetric Sentinel-1A images. The specific objectives were: (1) to evaluate the accuracy gain in the crop classification using Sentinel-1A multitemporal data backscatter coefficients and ratio (σ0VH, σ0VV and, σ0VH/σ0VV, denominate BS group) in comparison to the addition of polarimetric attributes (σ0VH, σ0VV, σ0VH/σ0VV, H, and α, denominate BP group) and; (2) to assess the accuracy gain in the earliest crop classification, creating new scenarios with the addition of the new SAR data together with the previous images for each date and group (BS and BP) during the crop development. For BS and BP groups, 13 e 10 scenarios were analyzed in LEM and Campo Verde, respectively. For the classification process, we used the Random Forest (RF) algorithm. In the LEM site, the best results for BS and BP groups were equivalent (overall accuracy: ∼82%), while for the Campo Verde site, the classification accuracy for the BP group (overall accuracy: ∼80%) was 2% higher than the BS group. The addition of new images during the crop development period increased the earliest crop classification overall accuracy, stabilizing from mid-February in LEM and mid-December in Campo Verde, after 10 and 8 images, respectively. After these periods, the gain in classification accuracy was small with the addition of new images. In general, our results suggest the backscattering coefficients and polarimetric attributes extracted from the Sentinel-1A imagery exhibited a great performance to discriminate croplands.
AUTOMATED CLASSIFICATION OF CROP TYPES AND CONDITION IN A MEDITERRANEAN AREA USING A FINE-TUNED CONVOLUTIONAL NEURAL NETWORK
Authors: Ruiz L. A. , Almonacid-Caballer J. , Crespo-Peremarch P. , Recio J. A. , Pardo-Pascual J. E. , Sánchez-García E.
Published: August 2020

Abstract. Crop classification based on satellite and aerial imagery is a recurrent application in remote sensing. It has been used as input for creating and updating agricultural inventories, yield prediction and land management. In the context of the Common Agricultural Policy (CAP), farmers get subsidies based on the crop area cultivated. The correspondence between the declared and the actual crop needs to be monitored every year, and the parcels must be properly maintained, without signs of abandonment. In this work, Sentinel- 2 time series images and 4-band Very High Resolution (VHR) aerial orthoimages from the Spanish National Programme of Aerial Orthophotography (PNOA) were combined in a pre-trained Convolutional Neural Network (CNN) (VGG-19) adapted with a double goal: (i) the classification of agricultural parcels in different crop types; and (ii) the identification of crop condition (i.e., abandoned vs. non-abandoned) of permanent crops in a Mediterranean area of Spain. A total of 1237 crop parcels from the CAP declarations of 2019 were used as ground truth to classify into cereals, fruit trees, olive trees, vineyards, grasslands and arable land, from which 80% were used for training and 20% for testing. The overall accuracy obtained was greater than 93% both, at parcel and area levels. Olive trees were the least accurate crop, mostly misclassified with fruit trees, and young vineyards were slightly confused with cereal and arable land. In the assessment of crop condition, only 9.65% of the abandoned plots were missed (omission errors), and 7.21% of plots were over-detected (commission errors), having a 99% of overall accuracy from a total of 1931 image subset samples. The proposed methodology based on CNN is promising for its operational application in crop monitoring and in the detection of abandonments in the context of CAP subsidies, but a more exhaustive number of training samples is needed for extension to other crop types and geographical areas.
LIGHT ENVIRONMENT AND LAI MONITORING IN RICE COMMUNITY BY GROUND- AND UAV-BASED REMOTE SENSING
Authors: Yamashita M. , Toyoda H. , Tanaka Y.
Published: August 2020

Abstract. Cultivation management adapted to various environmental changes has been required under changing climate, hence it is necessary to monitor the growth status efficiently. Photosynthesis in crop production is the essential process in crop growth model for forecasting the yield. In this study, we focused on light environment and LAI as important factors in photosynthesis, and conducted the observations to obtain time-series RGB images taken by digital cameras fixed on tower and mounted on UAV in the paddy field and to measure PPFDs at the top (I0) and the bottom (I) of rice canopy during planted period. Three kinds of vegetation color indices (CIs), vegetation coverage (VC) and plant height (PH) derived by processing RGB images were compared with relative PPFD (I/I0) and LAI calculated from relative PPFD. As the results, CIs and VC were basically expressed linear relations with relative PPFD and PH showed the exponential relation with relative PPFD and the linear relation with LAI respectively. Through this study, we clarified that VC and PH would be effective parameters for spatio-temporal monitoring of light environment and LAI.
COAXIALITY CALCULATION METHOD FOR DROPPING OPERATION OF LUNAR SURFACE SAMPLING MISSION BASED ON MONOCULAR VISION USING ELLIPSE AND LINE FEATURES
Authors: Bo Z. , Wan W. , Liu C. , Di K. , Liu Z. , Peng M. , Wang Y.
Published: August 2020

Abstract. In the lunar surface sampling mission, the drop operation that transferring the packaging container into the sample return container is a vital step. In this paper, for supporting the drop operation, we proposed a method to measure the coaxiality between the two containers based on the monocular vision pose measurement using ellipse and line features. The method first extracts ellipse and line features in the 2D image to calculate the initial parameters. Then, the initial parameters and the point set are used to fit the curve by the cylinder perspective contours fitting method, where the constraints between ellipse and line features are used to improve the robustness to varying noises. Next, given the fitting ellipse and camera parameters, the pose of the packaging container is solved by using circle-based method. Finally, the coaxiality of the two containers is calculated based on the relative pose between them. Simulation experiments were carried out and the results show the effectiveness and reliability of the proposed method.
DEEP LEARNING FOR 3D RECONSTRUCTION OF THE MARTIAN SURFACE USING MONOCULAR IMAGES: A FIRST GLANCE
Authors: Chen Z. , Wu B. , Liu W. C.
Published: August 2020

Abstract. The paper presents our efforts on CNN-based 3D reconstruction of the Martian surface using monocular images. The Viking colorized global mosaic and Mar Express HRSC blended DEM are used as training data. An encoder-decoder network system is employed in the framework. The encoder section extracts features from the images, which includes convolution layers and reduction layers. The decoder section consists of deconvolution layers and is to integrate features and convert the images to desired DEMs. In addition, skip connection between encoder and decoder section is applied, which offers more low-level features for the decoder section to improve its performance. Monocular Context Camera (CTX) images are used to test and verify the performance of the proposed CNN-based approach. Experimental results show promising performances of the proposed approach. Features in images are well utilized, and topographical details in images are successfully recovered in the DEMs. In most cases, the geometric accuracies of the generated DEMs are comparable to those generated by the traditional technology of photogrammetry using stereo images. The preliminary results show that the proposed CNN-based approach has great potential for 3D reconstruction of the Martian surface.
ESA SENTINEL 2 IMAGERY AND GBGEOAPP: INTEGRATED TOOLS FOR THE DEOSAI NATIONAL PARK MANAGEMENT PLAN
Authors: Melis M. T. , Dessì F. , Loddo P. , Maccioni A. , Gallo M. , Ul Hassan R. , Aurang Zaib M.
Published: August 2020

Abstract. Deosai plateau, in the Gilgit-Baltistan Province of Pakistan, for its average elevation of 4,114 meters, is the second highest plateau in the world after Changtang Tibetan Plateau. Two biogeographically important mountain ranges merge in Deosai: the Himalayan and Karakorum–Pamir highlands. The Deosai National Park, with its first recognition in 1993, encompasses an area of about 1620 km2, with the altitude ranging from 3500 to 5200 meters a.s.l. It is known and visited by tourists for the presence of brown bear, but a large number of species of fauna and flora leave, and can be seen during the summer season. This high-altitude ecosystem is particularly fragile and can be considered a sentinel for the effects of climate changes.Due to its geographic position and high altitude, the area of Deosai has never been studied in all its ecosystem components, producing high resolution maps. The first land cover map of Deosai with 10 meters of resolution is discussed in this study. This map has been obtained from Sentinel-2 imagery and improved through the new tool developed in this study: the GBGEOApp. This application for mobile has been done with three main ambitions: the validation of the new land cover map, its improvement with land use information, and the collection of new data in the field. On the basis of the results, the use of the GBGEOApp, as a tool for validation and increasing of environmental data collection, seems to be completely applicable involving the local technicians in a process of data sharing.
ON THE DEVELOPMENT OF A NOVEL APPROACH FOR IDENTIFYING PERENNIAL DRAINAGE IN SOUTHERN BRAZIL: A STUDY CASE INTEGRATING SENTINEL-2 AND HIGH-RESOLUTION DIGITAL ELEVATION MODELS WITH MACHINE LEARNING TECHNIQUES
Authors: Montibeller A. , Vilela M. , Hino F. , Mallmann P. , Nadas M. , Caruso Jr. F. , Delabary H.
Published: August 2020

Abstract. Riparian vegetation plays a key role in maintaining water quality and preserving the ecosystems along riverine systems, as they prevent soil erosion, retain water by increased infiltration, and act as a buffer zone between rivers and their surroundings. Within urban spaces, these areas have also an important role in preventing illegal occupation in areas of hydrologic risk, such as in floodplains. The goal of this research is to propose a framework for identifying areas of permanent protection associated with perennial drainage, utilizing satellite imagery and digital elevation models (DEM) in association with machine learning techniques. The specific objectives include the development of a decision tree to retrieve perennial drainage over high resolution, 1-meter DEM’s, and the development of digital image processing workflow to retrieve surface water bodies from Sentinel-2 imagery. In-situ information on perennial and ephemeral conditions of streams and rivers were obtained to validate our results, that happened in the first trimester of 2020. We propose a minimum of 7 days without precipitation prior to in-situ validation, for more accurate assessment of streamflow conditions, in order to minimize impacts of surface water runoff in flow regime. The proposed method will benefit decision makers by providing them with reliable information on drainage network and their buffer zones, as well as yield detailed mapping of the areas of permanent protection that are key to urban planning and management.
LAND USE CLASSIFICATION USING DEEP MULTITASK NETWORKS
Authors: Bergado J. R. , Persello C. , Stein A.
Published: August 2020

Abstract. Updated information on urban land use allows city planners and decision makers to conduct large scale monitoring of urban areas for sustainable urban growth. Remote sensing data and classification methods offer an efficient and reliable way to update such land use maps. Features extracted from land cover maps are helpful on performing a land use classification task. Such prior information can be embedded in the design of a deep learning based land use classifier by applying a multitask learning setup—simultaneously solving a land use and a land cover classification task. In this study, we explore a fully convolutional multitask network to classify urban land use from very high resolution (VHR) imagery. We experimented with three different setups of the fully convolutional network and compared it against a baseline random forest classifier. The first setup is a standard network only predicting the land use class of each pixel in the image. The second setup is a multitask network that concatenates the land use and land cover class labels in the same output layer of the network while the other setup accept as an input the land cover predictions, predicted by a subpart of the network, concatenated to the original input image patches. The two deep multitask networks outperforms the other two classifiers by at least 30% in average F1-score.
CUBA SURFACE ESTIMATION FROM SHORELINE MAPPING, GENERATED BY SENTINEL-2 IMAGE PROCESSING
Authors: Ojeda Martínez D. , Martinez Fernández P. , Matamoros Fombellida P. C. , Morejón González M. , Álvarez Portal R. , Labori de la Rosa K.
Published: August 2020

Abstract. The coastal with its geographical elements are modified over time, changes that may occur in the long or short term so, shoreline represented in the cartographic documents does not always match with reality. Also, the coastline does not have a well-defined length as results from the fractal-like properties of coastlines, i.e., the fact that a coastline typically has a fractal dimension so, its representation depends to many factors like the cartographic method used, the resolution of the image and the scale of the map. On the other hand, the shoreline is general use to estimate the surface of the landmass, so is important to get a cartographic product with accuracy in a time. Taking into account these elements, this paper is focus in a surface estimation of total Cuban island and keys. To achieved, an expeditious method was used, from the application of geometric corrections to multispectral SENTINEL-2 images, followed by a band algebra using the NDWI index. After those process the automated extraction of shoreline was conducted and finally, refining the vectors by cartographic editing without compromising the accuracy of the results. As a result, two coast lines were obtained from the territory, one in the rainy season and the other in the dry season. The spatial analyses were oriented to surface determination and they were carried out over shoreline in dry season and rainy season, the results were contrasted with the official data published in 2016.
TEXTURE ANALYSIS FOR LAND USE LAND COVER (LULC) CLASSIFICATION IN PARTS OF AHMEDABAD, GUJARAT
Authors: Nizalapur V. , Vyas A.
Published: August 2020

Abstract. The present study addresses the potential of RADARSAT-2 data for Land Use Land Cover (LULC) Classification in parts of Ahmedabad, Gujarat, India. Texture measures of the original SAR data were obtained by the Gray Level Co-occurrence Matrix (GLCM). Results suggested False Colour Composite (FCC) of Mean, Homogeneity and Entropy showed a good discrimination of different land cover classes. Further, Principal Component Analysis (PCA) was also applied to the eight texture measures and FCC of Principal components is generated. Unsupervised classification is carried out for the above generated FCCs and accuracy assessment is carried out. The result of classification shows that the PCA generated from GLCM texture measures could obtain higher accuracy than using only the classification carried out by texture measures. Overall results of the study suggested possible use of single polarization and single date Radarsat-2 data for LULC classification with better accuracy using PCA generated image.
LAND SUBSIDENCE MONITORING WITH MULTI-TRACK SAR DATA
Authors: Li X. , Yan L. , Huang G.
Published: August 2020

Abstract. In this study, we investigated wide-area land subsidence in Hebei Plain using 78 C-band Sentinel-1A SAR images acquired from May 2017 to May 2018 covering two tracks. High-precision time series retrieval was performed by NSBAS technology based on a single track. The offset deformation values of two tracks calculated in this paper were −0.09 mm/year, and the multi-track deformation rate was successfully merged. Using the cross-validation of redundant observations in the multi-track overlap area to evaluate the accuracy, and it was found that 90% pixel difference between the two track overlap areas was within 9 mm. The standard deviation was 5.38 mm, and the subsidence trend of the overlap area was consistent. Twenty-four subsidence bowls were extracted, and the maximum subsidence rate reached −62 mm/year.
DIMENSIONALITY REDUCTION VIA AN ORTHOGONAL AUTOENCODER APPROACH FOR HYPERSPECTRAL IMAGE CLASSIFICATION
Authors: Ayma V. H. , Ayma V. A. , Gutierrez J.
Published: August 2020

Abstract. Nowadays, the increasing amount of information provided by hyperspectral sensors requires optimal solutions to ease the subsequent analysis of the produced data. A common issue in this matter relates to the hyperspectral data representation for classification tasks. Existing approaches address the data representation problem by performing a dimensionality reduction over the original data. However, mining complementary features that reduce the redundancy from the multiple levels of hyperspectral images remains challenging. Thus, exploiting the representation power of neural networks based techniques becomes an attractive alternative in this matter. In this work, we propose a novel dimensionality reduction implementation for hyperspectral imaging based on autoencoders, ensuring the orthogonality among features to reduce the redundancy in hyperspectral data. The experiments conducted on the Pavia University, the Kennedy Space Center, and Botswana hyperspectral datasets evidence such representation power of our approach, leading to better classification performances compared to traditional hyperspectral dimensionality reduction algorithms.
QUALITY ASSESSMENT OF DIMENSIONALITY REDUCTION TECHNIQUES ON HYPERSPECTRAL DATA: A NEURAL NETWORK BASED APPROACH
Authors: Deepa C. , Shetty A. , Narasimhadhan A. V.
Published: August 2020

Abstract. Dimensionality reduction of hyperspectral images plays a vital role in remote sensing data analysis. The rapid advances in hyperspectral remote sensing has brought in a lot of opportunities to researchers to come up with advanced algorithms to analyse such voluminous data to better explore earth surface features. Modern machine learning algorithms can be applied to explore the underlying structure of high dimensional hyperspectral data and reduce the redundant information through feature extraction techniques. Limited studies have been carried out on dimensionality reduction for mineral exploration. The current study mainly focuses on the application of autoencoders for dimensionality reduction and provides a qualitative (visual) analysis of the obtained representations. The performance of autoencoders are investigated on Cuprite scene. Coranking matrix is used as evaluation criteria. From the obtained results it is evident that, deep autoencoders provide better results compared to single layer autoencoders. An increase in the number of hidden layers provides a better embedding. The neighborhood size K ≥ 40 of deep autoencoders provides a better transformation compared to autoencoders which shows an improved embedding only after K ≥ 80.
SPATIAL-SPECTRAL MANIFOLD EMBEDDING OF HYPERSPECTRAL DATA
Authors: Hong D. , Yao J. , Wu X. , Chanussot J. , Zhu X.
Published: August 2020

Abstract. In recent years, hyperspectral imaging, also known as imaging spectroscopy, has been paid an increasing interest in geoscience and remote sensing community. Hyperspectral imagery is characterized by very rich spectral information, which enables us to recognize the materials of interest lying on the surface of the Earth more easier. We have to admit, however, that high spectral dimension inevitably brings some drawbacks, such as expensive data storage and transmission, information redundancy, etc. Therefore, to reduce the spectral dimensionality effectively and learn more discriminative spectral low-dimensional embedding, in this paper we propose a novel hyperspectral embedding approach by simultaneously considering spatial and spectral information, called spatial-spectral manifold embedding (SSME). Beyond the pixel-wise spectral embedding approaches, SSME models the spatial and spectral information jointly in a patch-based fashion. SSME not only learns the spectral embedding by using the adjacency matrix obtained by similarity measurement between spectral signatures, but also models the spatial neighbours of a target pixel in hyperspectral scene by sharing the same weights (or edges) in the process of learning embedding. Classification is explored as a potential strategy to quantitatively evaluate the performance of learned embedding representations. Classification is explored as a potential application for quantitatively evaluating the performance of these hyperspectral embedding algorithms. Extensive experiments conducted on the widely-used hyperspectral datasets demonstrate the superiority and effectiveness of the proposed SSME as compared to several state-of-the-art embedding methods.
FUSION OF LIDAR AND HYPERSPECTRAL DATA FOR SEMANTIC SEGMENTATION OF FOREST TREE SPECIES
Authors: Tusa E. , Monnet J. M. , Barré J. B. , Dalla Mura M. , Chanussot J.
Published: August 2020

Abstract. Hyperspectral images (HI) and Light Detection and Ranging (LiDAR) provide high resolution radiometric and geometric information for monitoring forests at individual tree crown (ITC) level. It has many important applications for sustainable forest management, biodiversity assessment and healthy ecosystem preservation. However, the integration of different remote sensing modalities is a challenging task for tree species classification due to different artifacts such as the lighting variability, the topographic effects and the atmospheric conditions of the data acquisition. The characterization of ITC can benefit from the extraction and selection of robust feature descriptors that solve these issues. This paper aims to investigate the integration of feature descriptors from HI and LiDAR by using the intra-set and inter-set feature importance for the semantic segmentation of forest tree species. A fusion methodology is proposed between high-density LiDAR data – (20 pulses m−2) and VNIR HI – (160 bands and 0.80 m spatial resolution) acquired on French temperate forests along an altitude gradient. The proposed scheme has three inputs: the field inventory information, the HI and the LiDAR data. Our approach can be described in nine stages: polygon projection, non-overlapping pixel selection, vegetation and shadow removal, LiDAR feature extraction, height mask, robust PCA (rPCA), feature reduction and classification. The overall accuracy of tree species classification at pixel-level was 68.9% by using random forest (RF) classifier. Our approach showed that 74.0% of trees were correctly assigned overall, by having conifer species such as Norway Spruce (Picea abies) with a producer’s accuracy of 97.4%.
THERMAL AND OPTICAL DATA FUSION SUPPORTING BUILT HERITAGE ANALYSES
Authors: Patrucco G. , Cortese G. , Giulio Tonolo F. , Spanò A.
Published: August 2020

Abstract. The recent developments of passive sensors techniques, that have been able to take advantage of the technological innovations related to sensors technical features, sensor calibration, the use of UAV systems (Unmanned Aerial Vehicle), the integration of image matching techniques and SfM (Structure from Motion) algorithms, enable to exploit both thermal and optical data in multi-disciplinary projects. This synergy boost the application of Infrared Thermography (IRT) to new application domains, since the capability to provide thematic information of the analysed objects benefits from the typical advantages of data georeferencing and metric accuracy, being able to compare results investigating different phenomena.This paper presents a research activity in terrestrial and aerial (UAV) applications, aimed at generating photogrammetric products with certified and controlled geometric and thematic accuracy even when the acquisitions of thermal data were not initially designed for the photogrammetric process. The basic principle investigated and pursued is the processing of a photogrammetric block of images, including thermal IR and optical imagery, using the same reference system, which allows the use of co-registration algorithms. Such approach enabled the generation of radiance maps, orthoimagery and 3D models embedding the thermal information of the investigated surfaces, also known as texture mapping; these geospatial dataset are particularly useful in the context of the built Heritage documentation, characterised by complex analyses challenges that a perfect fit for investigations based on interdisciplinary approaches.
IMCLASS – A USER-TAILORED MACHINE LEARNING IMAGE CLASSIFICATION CHAIN FOR CHANGE DETECTION OR LANDCOVER MAPPING
Authors: Déprez A. , Puissant A. , Malet J.-P. , Michéa D.
Published: August 2020

Abstract. With the increasing availability of satellite imagery at several spatial, spectral and temporal resolutions, the choice of the best image and the most appropriate method for object detection and classification of a broad range of land surface classes or processes is still a difficult task for many users. In order to guide the users, we proposed a user-tailored machine learning method (IMage CLASSification - ImCLASS) to detect and classifiy specific landcover classes.The method assumes a mono-class approach taking several ill-posed problems (e.g. class imbalance, high diversity inside the studied class, similarities with the adjacent samples…) as use cases (landslides, construction works in urban areas, burnt areas, vegetation classes…). It is a generalization of the ALADIM processor already validated in the context of landslide mapping and available as a service on the ESA GeoHazards Exploitation Platform (GEP). The proposed chain is able to combine optical and radar images, uses open source libraries, and is optimized for rapid calculation on HPC environments. The ImCLASS processor is presented and its performance is evaluated on three use cases: landslide detection and mapping after disasters in different regions of the World, urban classes change detection with a focus on construction works in Strasbourg, and crop mapping (vineyard) in the Grand-Est region. First results using either bi-dates or mono-date imagery are presented.
AUTOMATICALLY GENERATED TRAINING DATA FOR LAND COVER CLASSIFICATION WITH CNNS USING SENTINEL-2 IMAGES
Authors: Voelsen M. , Bostelmann J. , Maas A. , Rottensteiner F. , Heipke C.
Published: August 2020

Abstract. Pixel-wise classification of remote sensing imagery is highly interesting for tasks like land cover classification or change detection. The acquisition of large training data sets for these tasks is challenging, but necessary to obtain good results with deep learning algorithms such as convolutional neural networks (CNN). In this paper we present a method for the automatic generation of a large amount of training data by combining satellite imagery with reference data from an available geospatial database. Due to this combination of different data sources the resulting training data contain a certain amount of incorrect labels. We evaluate the influence of this so called label noise regarding the time difference between acquisition of the two data sources, the amount of training data and the class structure. We combine Sentinel-2 images with reference data from a geospatial database provided by the German Land Survey Office of Lower Saxony (LGLN). With different training sets we train a fully convolutional neural network (FCN) and classify four land cover classes (Building, Agriculture, Forest, Water). Our results show that the errors in the training samples do not have a large influence on the resulting classifiers. This is probably due to the fact that the noise is randomly distributed and thus, neighbours of incorrect samples are predominantly correct. As expected, a larger amount of training data improves the results, especially for the less well represented classes. Other influences are different illuminations conditions and seasonal effects during data acquisition. To better adapt the classifier to these different conditions they should also be included in the training data.
UHI ESTIMATION BASED ON ASTER AND MODIS SATELLITE IMAGERY: FIRST RESULTS ON STRASBOURG CITY, FRANCE
Authors: Del Pozo S. , Landes T. , Nerry F. , Kastendeuch P. , Najjar G. , Philipps N. , Lagüela S.
Published: August 2020

Abstract. The increase in urbanization of cities coupled with some effects of climate change is leading to the emergence of urban microclimates. The rising temperatures in cities create the phenomenon known as Urban Heat Island (UHI). This is a difference between the temperature of urban and rural areas that intensifies more during the night and varies according to the season of the year. This paper focuses on the estimation of the UHI and its variations in and around the city of Strasbourg. To this end, thermal remote sensing data from different satellite has been used to isolate and analyse this phenomenon, specifically, Land Surface Temperatures (LSTs) provided by ASTER and MODIS. The LST provided by these sensors has been compared to air temperatures of the last 15 years from meteorological stations distributed throughout the city that have served as ground truth. Forty-seven meteorological stations spread throughout the area of interest provided measurements of air humidity and temperature. This valuable data base has allowed the assessment of the correlations between LST from satellite and ground truth air temperature. Based on satellite data resampled at different spatial units, this work led to the creation of the first UHI map of Strasbourg.
ROLE OF EARTH OBSERVATION DATA AND HYDROLOGICAL MODELING IN SUPPORTING UN SDGs IN NORTH WEST HIMALAYA
Authors: Aggarwal S. P. , Thakur P. K. , Nikam B. R. , Garg V. , Chouksey A. , Dhote P. R. , Bisht S. , Dixit A. , Arora S. , Choudhury A. , Sharma V. , Chauhan P. , Kumar A. S.
Published: August 2020

Abstract. The sustainable usage and accurate assessment of water resources in North West Himalaya (NWH) is very important for respective policy makers. NWH receives precipitation from both southwest and northeast monsoon system. The detailed assessment of current and future water resources and hydrological cycle component for NWH river basins using earth observation (EO) satellites and hydrological models is very critical for attaining United Nations sustainable development goals (SDGs) namely, climate action, affordable and clean energy, clean water and sanitation and building resilient infrastructure. Present work highlights the role of various EO sensors and hydrological models and ground based instruments for improved assessment of water resources of NWH river basins. The complete inventory of NWH surface water (including glacier lakes of UK, HP), snow cover, delta SWE and glaciers database was accomplished with Remote Sensing (RS) datasets. Similarly, glacier velocity was estimated for all major glaciers of NWH using feature tracking and differential interferometry (DInSAR) methods. Fully distributed grid based hydrological model was setup for entire NWH and model calibration/validation was done for Beas, Satluj, Upper Ganga and Jhelum river basins. Quantification of relative contribution of snowmelt, glacier melt and rainfall-runoff was estimated for Bhagirathi basin upto Uttarkashi. An extensive network of automatic weather stations (AWS), 27 nos, 10 snow depth sensors, 04 digital water level recorders, two snow pack analysers and 06 long wave solar radiation sensors were installed in various sites of HP and UK for hydro-meteorological data collection, model simulation and validation. A future climate change simulations were done for Beas and Jhelum basins using CORDEX 4.5 and 8.5 scenarios from 2006–2100. Number of flood peaks were found to be increasing in number as well as decrease in total snow fall.
FIELD OPERATIONS AND PROGRESS OF CHINESE AIRBORNE SURVEY IN EAST ANTARCTICA THROUGH THE “SNOW EAGLE 601”
Authors: Cui X. , Guo J. , Li L. , Tang X. , Sun B.
Published: August 2020

Abstract. The Antarctic plays a vital role in the Earth system. However, our poor knowledge of the Antarctic limits predicting and projecting future climate changes and sea level rising due to rapid changing of the Antarctic. Airborne platforms can access most places of this hostile and remote continent and measure subice properties with high resolution and accuracy. China deployed the first fixed-wing airplane of “Snow Eagle 601” for Antarctic expeditions in 2015. Airborne scientific instruments, including radio-echo sounder, gravimeter, magnetometer, laser altimeter etc., were configured and integrated on the airplane. In the past four years, the airborne platform has been applied to survey the Princess Elizabeth Land, the largest data gap in Antarctica, Amery Ice Shelf and other critical areas in East Antarctica, and overall ∼150,000 km flight lines have been completed. Here, we introduced the “Snow Eagle 601” airborne platform and base stations, as well as field operations of airborne survey, including aviation supports, daily cycle of the scientific flight, data processing and quality control, and finally summarized progress of airborne survey in the past four years.
COMPARISON AND EVALUATION OF REMOTE SENSING INDICES FOR AGRICULTURAL DROUGHT MONITORING OVER KAZAKHSTAN
Authors: Sun B. , Qian J. , Chen X. , Zhou Q.
Published: August 2020

Abstract. Drought has a significant impact on the Kazakhstan’s agricultural economy, which is the world's largest wheat flour exporting country. Remote sensing provides an efficient tool for monitoring agricultural drought in a wide range. Hundreds of remote sensing drought indices have been developed during the past decades. Some of them have been widely used over the world as an indispensable indicator in drought monitoring systems. However, the applicability of those indices in Kazakhstan, the largest country in Central Asia has not been tested, especially for agricultural drought monitoring. In this study, the most common remote sensing drought indices in current running systems of drought monitoring are compared and evaluated. The response of those indices to the soil drought is validated based on remote sensing soil moisture data. In addition, the effectiveness of remote sensing indices in agricultural drought monitoring is assessed according to agricultural product yield data from the past 15 years (2004–2018). Results indicate that remote sensing drought indices can generally reflect serious drought events in the study area, but the consistency of different types of indices is poor. Compared with annual statistics of agricultural product yield, remote sensing drought index better reflects the long-term change of agricultural drought in Kazakhstan.