bluuebunny/crossref_metadata_2025 huggingface dataset:
BAND SELECTION OF HYPERSPECTRAL IMAGES BASED ON MARKOV CLUSTERING AND SPECTRAL DIFFERENCE MEASUREMENT FOR OBJECT EXTRACTION
Authors: Zhang T. , Li P. , Ding Y. , Luo D. , Ma Z. , Li X. , Wen L.
Published: May 2022

Abstract. For the existing hyperspectral image (HSI) band selection (BS) algorithm does not consider the strong correlation between adjacent bands and does not meet the high-precision extraction of single target, a HSI BS algorithm based on Markov clustering and target land-type spectral difference measurement is proposed in this paper. Specifically, when using Markov clustering for band clustering, the inter band correlation information is embedded and the noise or bad channel band breakpoint is set to adaptively divide the optimal band clustering subset. Then, in each cluster, based on the band difference under the supervision of target category, an evaluation criterion function is designed to select the optimal band combination for single target object extraction. The BS algorithm proposed in this paper is called MCLSD for short. Taking ZY-1 02D HSI in Tongnan of Chongqing as experimental data, taking cultivated land as extraction object and the Random Forest as classifier, the classification accuracy of the selected bands is evaluated. In addition, the MCLSD is compared with the improved sparse subspace clustering (ISSC) (Sun et al., 2015), orthogonal projection band selection (OPBS) (Zhang et al., 2018) and sparse nonnegative matrix factorization (SNMF) (Qin and et al., 2015). Experimental results show that the MCLSD algorithm can select the most suitable band for cultivated land extraction and achieve higher classification accuracy. Especially when the number of bands is less than 5, the MCLSD algorithm has significant advantages over ISSC, OPBS and SNMF. So the MCLSD BS method can meet the demand of the high-precision extraction of target features from HSI data.
OPTIMUM NUMBER AND POSITIONS OF TERRESTRIAL LASER SCANNER TO DERIVE DTM AT FOREST PLOT LEVEL
Authors: Kushwaha S. K. P. , Singh A. , Jain K. , Mokros M.
Published: May 2022

Abstract. Remote sensing of forest landscape is sometimes quite challenging, considering the rugged terrain, remote areas and presence of dense canopy. LiDAR is capable of generating dense three-dimensional (3D) structure of the forest. But when research is focused on a small area that needs dense information Terrestrial Laser Scanner (TLS) proves to be an efficient way of acquiring the information. TLS is an instrument used to acquire data from the ground surface level. Ground information and under canopy structure are acquired with more accuracy and precession with TLS when compared to aerial remote sensing techniques. When a TLS survey is done, scan positions and the number of scans are important parameters to consider to acquire datasets of forest plots. In this research, an analysis is carried out to determine the optimum number of TLS scans and positions that are needed to sufficiently generate the number of ground points to generate a Digital Terrain Model (DTM). The research considers two forest plots of 25m × 25m with different tree densities. Different TLS scanning combinations are used to determine the best scanning pattern for DTM generation at the forest plot level.
PERFORMANCE EVALUATION OF FUSION TECHNIQUES FOR CROSS-DOMAIN BUILDING ROOFTOP SEGMENTATION
Authors: Li H. , Tian J. , Xie Y. , Li C. , Reinartz P.
Published: May 2022

Abstract. Convolutional Neural Networks have been widely introduced to building rooftop segmentation using satellite and aerial imagery. Preparing efficient training data is still among the critical issues on this topic. Therefore, adopting available annotated cross-domain multisource dataset is needed. This paper evaluates the performance of fusing the state-of-art deep learning neural network architectures for cross-domain building rooftop segmentation. We have selected three semantic image segmentation neural networks, including Swin transformer, OCRNet and HRNet. The predictions from these three neural networks are combined with majority voting, max value and union fusion techniques, a refined building rooftop segmentation mask is therefore delivered. The experiments on two benchmark datasets show that the proposed fusion techniques outperform single models and other state-of-art cross-domain segmentation approaches.
MULTI-SOURCE POINT CLOUD SEMANTIC SEGMENTATION USING NEURAL NETWORK
Authors: Montlahuc J. , Polette A. , Tahan A. , Pernot J.-P. , Rivest L.
Published: May 2022

Abstract. The purpose of this study is to enhance point cloud semantic segmentation by using point clouds from multiple distinct technologies on the same capture location and to determine whether employing various technologies throughout the acquisition process yields better performance during classification. The different point clouds were captured in the same geographical location and have previously been aligned and classified by professionals of the field. Three locations have been scanned with airborne lidar, terrestrial lidar and photogrammetry using UAV or helicopter. The use of various sources of capture on the same location opens the door to creating new features, such as the proportion of each source involved in the semantic segmentation of point clouds. This plurality of sources also enables us to spread various features, such as RGB colors, that have been propagated to other sources via the neighborhood. The initial results lean towards capture using different technologies as the overall accuracy increase by two to four points and the mean Matthews correlation coefficient increase by four to seven points. The main drawbacks are the cost of some technologies, as well as the processing time, which is greater than with a single technology.
ESTIMATION OF LANDCOVER TYPES OVER HIMALAYAN REGION WITH THE CLASSIFICATION OF OPTICAL AND MICROWAVE-BASED IMAGE FUSION DATASET
Authors: Singh S. , Tiwari R. K. , Sood V.
Published: May 2022

Abstract. Himalayas play a significant role in terms of climate influence, the origin of rivers, hydropower generation, tourism, and forest wealth. The monitoring of the rugged terrain Himalayas via remote sensing is one of the efficient solutions to meet future requirements. In remote sensing, the sensors can be categorized as optical and microwave. The optical-based sensor provides multispectral or hyperspectral information at a very fine spatial resolution but is limited to daytime images without any penetration through the clouds. Whereas, the microwave works more effectively due to day/night image acquisition and cloud penetration capabilities. Therefore, the image fusion of multi-sensors (optical and microwave) datasets is important to extract crucial information about the Earth surface, especially over the Himalayas. However, the main aim of the article is to retrieve the different landcover types using various classifiers i.e., Linear Spectral Mixing (LSM), Random Forest Classifier (RFC), and Support Vector Machine (SVM) on the fused dataset. The dataset has been acquired over a part of Indian Himalayan terrain i.e., Uttarakhand State, India using microwave-based ISRO’s Scatterometer Satellite (SCATSAT-1) and optical-based NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). The results show the effectiveness of the RFC classifier in the mapping of land surface features as compared to other classification algorithms i.e., LSM and SVM. This study not only highlights the potential of the RFC classifier in the extraction of information but also, shows the significance of fusion of optical and microwave datasets in the extraction of important Earth surface features.
MU-NET: A MULTISCALE UNSUPERVISED NETWORK FOR REMOTE SENSING IMAGE REGISTRATION
Authors: Tang T. , Chen T. , Zhu B. , Ye Y.
Published: May 2022

Abstract. Registration for multi-sensor or multi-modal image pairs with a large degree of distortions is a fundamental task for many remote sensing applications. To achieve accurate and low-cost remote sensing image registration, we propose a multiscale unsupervised network (MU-Net). Without costly ground truth labels, MU-Net directly learns the end-to-end mapping from the image pairs to their transformation parameters. MU-Net performs a coarse-to-fine registration pipeline by stacking several deep neural network models on multiple scales, which prevents the backpropagation being falling into a local extremum and resists significant image distortions. In addition, a novel loss function paradigm is designed based on structural similarity, which makes MU-Net suitable for various types of multi-modal images. MU-Net is compared with traditional feature-based and area-based methods, as well as supervised and other unsupervised learning methods on the Optical-Optical, Optical-Infrared, Optical-SAR and Optical-Map datasets. Experimental results show that MU-Net achieves more robust and accurate registration performance between these image pairs with geometric and radiometric distortions.We share the datasets and the code implemented by Pytorch at https://github.com/yeyuanxin110/MU-Net.
URBAN CLASSIFICATION BASED ON TOP-VIEW POINT CLOUD AND SAR IMAGE FUSION WITH SWIN TRANSFORMER
Authors: Xue R. , Zhang X. , Soergel U.
Published: May 2022

Abstract. Urban areas are complex scenarios consisting of objects with various materials. This variety poses a challenge to single-data classification schemes. In this paper, we propose a feature fusion and classification network on RGB top-view point cloud and SAR images with swin-Transformer. In this network, the heterogeneous features are learned separately by an asymmetric encoder, and then they are concatenated along the channel dimension and fed into a fusing encoder. Finally, the fused features are decoded by an UperNet for generating the semantic labels. As data we use high-resolution 3D point cloud provided by Hessigheim benchmark which are complemented by TerraSAR-X images. The overall precision and the mean intersection over union (mIoU) achieves 87.25% and 73.56%, respectively, which outperforms the single-data swin-Transformer by 4.08% and 1.91%, respectively.
A COMPARISON BETWEEN CYCLE-GAN BASED FEATURE TRANSLATION AND OPTICAL-SAR VEGETATION INDICES
Authors: Çolak E. , Sunar F.
Published: May 2022

Abstract. Optical and microwave remote sensing technologies have become key tools for local and global change detection applications. Generally, optical data has been the focus of remote sensing for change detection because of the varied spatial and temporal resolutions that allow for reliable information. However, the dependence of optical data on weather conditions prevents continuous and up-to-date information. On the other hand, Synthetic Aperture Radar (SAR) data can record all-weather and all-time polarization information which is critical for change detection in poor weather conditions; nonetheless, SAR is not precise as optical data for forestry change detection applications as it cannot provide the spectral features of interest. The combined processing of optical and SAR images allow for the retrieval of information of interest with a precision that none of them could achieve alone. In this context, Cycle-Consistent Generative Adversarial Networks (CycleGAN) based deep feature translation method was proposed in this study for change detection. The CycleGAN transfers images from one domain (optical) to another domain (SAR) into the same feature space using a cyclic structure. Thus, it can provide continuous and up-to-date information for change detection while keeping its spectral features. The accuracy of the fake images generated from CycleGAN was evaluated by correlating them with spectral indices (e.g., Normalized Difference Vegetation Index (NDVI), Modified Radar Vegetation Index (mRVI), and Modified Radar Forest Degradation Index (mRFDI)) directly obtained from optical and SAR data. As a result, the best correlation coefficients (R) were found between real NDVI (optical data) and fake NDVI (CycleGAN) with 0.98 and 0.97 for two different dated datasets.
TOWARDS A TREE COVER CHANGE EARLY WARNING SYSTEM BASED ON SENTINEL-1 DATA AND A NEURAL NETWORK ARCHITECTURE
Authors: Fiche A. , Hardy C. , Mora B.
Published: May 2022

Abstract. This study aimed at exploring the potential of neural networks composed of convolutional and Long Short-Term Memory (LSTM) layers to handle dense Sentinel-1 data time series to develop a tree cover loss Early Warning System (EWS). The study area was in the Madre de Dios region in Peru that hosts a humid tropical forest. The second objective of this study was to investigate the potential of large free open-source datasets such as those from the GLAD and Geobosques alerts to calibrate and validate the models. The study demonstrated the capacity of the tested NN models to improve the detection of tree cover loss compared to a classical random forest algorithm thanks to their capacity to handle explicitly both spatial and temporal data. The accuracies of the best model compare reasonably well with those from similar studies. However, the observed overestimation of the Cover-loss class in the output map can be mainly linked to the quality of the alert datasets used as input data. Avenues to overcome the identified limits of this preliminary study are presented. This work provides a solid knowledge basis on the potential of a NN-based EWS and opens potential avenues for further improvements.
AUTOMATIC FOREST DEGRADATION MONITORING BY REMOTE SENSING METHODS AND COPERNICUS DATA
Authors: Gašparović M. , Klobučar D. , Gašparović I.
Published: May 2022

Abstract. Nowadays, forests are the most widely spread land cover and therefore play a significant role in ecology and create processes’ dynamics. Forests are threatened by various harmful effects due to biotic (insects, fungi, viruses, weeds, animals) and abiotic (floods, fires, storms, droughts, polluted atmospheres etc.) damages. Also, human damage (anthropogenic impact) is numerous and varied. They are caused by direct human action on the forest and indirect activities and processes (damage due to grazing, consequent devastation and erosion of habitats etc.). Forest devastation and illegal logging are one of the immediate negative human activities that have a detrimental impact on the forest. The research refers to the state forests of two management units, Javornik and Ćorkovača-Karlice in the border area of the Republic of Croatia. Part of the forests (about 3,000 hectares) of these two management units are located in a mine suspected area along the state border with Bosnia and Herzegovina. This research aims to develop an automatic algorithm for forest degradation monitoring by remote sensing methods and Copernicus data. The developed algorithm was based on the Sentinel-2 (S2) optical satellite imagery and Google Earth Engine. The proposed automatic forest degradation monitoring algorithm was based on the ΔNDVI change detection approach. Accuracy assessment was done by independent data in higher, 3-m resolution based PlanetScope imagery. Preliminary results show very similar forest degradation values per all tested forest compartment/subcompartment for automatically generated S2 10-m imagery forest degradation map and 3-m forest maps obtained manually from PlanetScope imagery.
ANALYSIS OF BUCHAREST URBAN DEVELOPMENT BASED ON MULTITEMPORAL SATELLITE-DERIVED DATASETS AND STATISTICAL DATA
Authors: Lazar A. M. , Dana Negula I. F. , Moise C. , Dediu F. , Mihalache C. E. , Badea A.
Published: May 2022

Abstract. Almost worldwide, urban expansion has been increasing at a rapid pace in the last decades. Although metropolitan areas strongly contribute to economic growth, they are also usually characterized by uncoordinated urban development that consequently causes overburdened infrastructure and higher air pollution. Bucharest (the capital of Romania) and the surrounding Ilfov County underwent a significant expansion during different growth stages, roughly tripling their population since 1930.The present study analyzes the evolution of the urban spatial expansion of Bucharest and Ilfov between 1985 and 2015, using the World Settlement Footprint Evolution dataset created by the German Aerospace Center. The research integrates data provided by the National Institute of Statistics in relation to the resident population, population density, the average monthly net income and the gross domestic product. The results reveal that Bucharest - Ilfov development region had an impressive expansion during the investigated time interval in comparison with the other Romanian regions. In 30 years, the built-up area corresponding to Bucharest and the Ilfov County doubled, 2007 being the most prolific for the development of new built-up areas. In what concerns Bucharest city, the most noteworthy urban expansion occurred in District 1. Several urban growth hotspots were also identified and analyzed in detail (e.g., North of the capital).
SENTINEL-5P NO2 DATA: CROSS-VALIDATION AND COMPARISON WITH GROUND MEASUREMENTS
Authors: Tonion F. , Pirotti F.
Published: May 2022

Abstract. Sentinel-5P (S5P) data provide information on atmospheric pollutants daily, and, for higher latitudes, consequent orbits partially overlap the same day. Provided clear atmospheric conditions, these data can provide insights on emission hotspots and on spatial distribution of critical air quality issues. The purpose of this work is to analyse several aspects of NO2 data from S5P over the years 2019, 2020 and 2021, in particular: (i) yearly average values between S5P data and 624 ground measurement stations were tested for correlation; (ii) 387 pairs of images from overlapping orbits on the same day were used to test for correlation on consecutive images with four different methods – simple linear regression over all valid cell values across the two images, over a subset with a low cloud fraction, and linear and tree-based methods using multiple predictors; (iii) local maxima values extracted from yearly NO2 emission maps were analysed to check potential hotspots of NO2 emissions.Results show that ground measurements correlate with S5P values, with r-squared values of 0.37 and 0.43 and RMSE of 7.4 and 8.6 µmol/m2 respectively for 2019 and 2020. Simple linear regression of overlapping consequent images returned average and standard deviation (sd) on r-squared respectively of 0.50(sd=0.21) and for RMSE of 11.3(sd=4.2) µmol/m2. Points from local maxima clearly detected 19 specific positions in large cities or nearby industrial areas, mostly in the north of Italy, with average NO2 values above 90 µmol/m2 in some cases consistently over the three years, proving that S5P imagery is a valid index for spatial distribution of NO2 concentration and air quality.
MAPPING PERMAFROST DISTRIBUTION IN THE PARVATI VALLEY, KULLU USING LANDSAT 8 DERIVED LAND SURFACE TEMPERATURE
Authors: Pradhan I. P. , Shukla D. P.
Published: May 2022

Abstract. Permafrost covers much of the Parvati valley, but mapping its distribution is difficult due to the scarcity of ground observed data sets and the region's high spatial heterogeneity. We show permafrost distribution maps for the Parvati valley from 2013 to 2021 based on the Mean Annual Air Temperature. We created maps of Mean Annual Air Temperatures (MAAT) using Landsat 8 Land Surface Temperature (LST) products in Google Earth Engine. Google Earth Engine (GEE) is a cloud-based platform that was utilised to quickly and efficiently obtain the spatial and temporal variations of permafrost distribution. Permafrost is defined as ground that maintains a temperature below zero degrees Celsius for at least two consecutive years. To justify the definition, we examined Mean Biennial Air Temperature (MBAT), Mean Triennial Air Temperature (MTAT), Mean Quadrennial Air Temperature (MQAT), Mean Quinquennial Air Temperature (MQnAT), Mean Septennial Air Temperature (MSpAT), Mean Octennial Air Temperature (MOAT), and Mean Novennial Air Temperature (MNOAT) (MNAT). Our findings demonstrate that the percentage of Permafrost distribution in the Parvati valley is about the same in all situations, accounting for 22 percent of the overall study area excluding glaciers. Our maps were divided into four categories: Continuous Permafrost Zone, Discontinuous Permafrost Zone, Sporadic Permafrost Zone, and No Permafrost Zone.
INTERPRETING CHANGES IN ALBEDO AND MASS BALANCE AT WHITE GLACIER, CANADIAN ARCTIC ARCHIPELAGO
Authors: Ye Y. , Tian Y.
Published: May 2022

Abstract. Alpine glaciers are sensitive to changes in land surface temperature (LST), and measurements of the mass balance are limited, especially for small glaciers. In this study, we investigate the relationship between snow albedo of the melting season (June, July, and August) and annual glacier mass balance of the White Glacier from 2002 to 2018. Since there are many gaps in the albedo data, we use a interpolation method to fill them and then obtain the average value of the melting season. The study results show that surface temperature plays a dominant role in albedo and mass balance changes, and mass balance change and albedo variation show a significant consistency, with an excellent correlation (R2 > 0.93). The acceleration of mass balance shows that the rate of mass reduction slows down, and the albedo change shows that the albedo increases from year to year. The interpolation albedo measurements using MODIS data can provide a useful means to reflect the annual change of glacier mass balance.
EVALUATING THE SEPARABILITY BETWEEN DRY TROPICAL FORESTS AND SAVANNA WOODLANDS IN THE BRAZILIAN SAVANNA USING LANDSAT DENSE IMAGE TIME SERIES AND ARTIFICIAL INTELLIGENCE
Authors: Bendini H. N. , Fonseca L. M. G. , Matosak B. M. , Mariano R. F. , Haidar R. F. , Valeriano D. M.
Published: May 2022

Abstract. The Brazilian Savanna is the second largest biogeographical region in Brazil and present different vegetation types, consisting mostly of tropical savannas, grasslands, and forests. The forest types have different tree cover and floristic composition, which is associated to leaf deciduousness. Considering the importance of Cerrado to biodiversity conservation and the maintaining of environmental services, the development of methods to map the different forest types in Cerrado is important for conservation programmes, subsidize restauration plains, and to allow estimations of carbon sink and stock. Mapping heterogeneous tropical areas, such as the Brazilian Savanna, is very complex due to the natural factors and peculiarities of the vegetation types, and it's still particularly challenging to separate between different forest formations. In this study we tested machine learning approaches based on the use of dense image time series, in order to evaluate the separability Dry Tropical Forests and Savanna woodlands. We considered the Brazilian State of Tocantins as the study area, which is located in the Northern region of the country. RF classification of Landsat dense time series showed an overall accuracy of 0.85005, while the LSTM approach presented an overall accuracy of 0.88601, with the highest f1-score for the savanna woodlands class, suggesting the capability of the recurrent neural networks on handling complex long-term dependencies such as the EVI dense time series data. This study showed the potential for the development of a semi-automatic method for discriminating the different types of forest formations in the Brazilian Savanna, based on remote sensing.
UAV-BASED RIVER PLASTIC DETECTION WITH A MULTISPECTRAL CAMERA
Authors: Cortesi I. , Masiero A. , Tucci G. , Topouzelis K.
Published: May 2022

Abstract. Plastic is the third world’s most produced material by industry (after concrete and steel), but people recycle only 9% of plastic that they have used. The other parts are either burned or accumulated in landfills and in the environment, the latter being the cause of many serious consequences, in particular when considering a long-term scenario. A significant part the plastic waste is dispersed in the aquatic environment, having a dramatic impact on the aquatic flora and fauna. This motivated several works aiming at the development of methodologies and automatic or semi-automatic tools for the plastic pollution detection, in order to enable and facilitate its recovery. This paper deals with the problem of plastic waste automatic detection in the fluvial and aquatic environment. The goal is that of exploiting the well-recognized potential of machine learning tools in object detection applications. A machine learning tool, based on random forest classifiers, has been developed to properly detect plastic objects in multi-spectral imagery collected by an unmanned aerial vehicle (UAV). In the developed approach, the outcome is determined by the combination of two random forest classifiers and of an area-based selection criterion. The approach is tested on 154 images collected by a multi-spectral proximity sensor, namely the MAIA-S2 camera, in a fluvial environment, on the Arno river (Italy), where an artificial controlled scenario was created by introducing plastic samples anchored to the ground. The obtained results are quite satisfactory in terms of object detection accuracy and recall (both higher than 98%), while presenting a remarkably lower performance in terms of precision and quality. The overall performance appears also to be dependent on the UAV flight altitude, being worse at higher altitudes, as expected.
PRELIMINARY CONCERNS ABOUT AGRONOMIC INTERPRETATION OF NDVI TIME SERIES FROM SENTINEL-2 DATA: PHENOLOGY AND THERMAL EFFICIENCY OF WINTER WHEAT IN PIEMONTE (NW ITALY)
Authors: Farbo A. , Sarvia F. , De Petris S. , Borgogno-Mondino E.
Published: May 2022

Abstract. TELECER project is supported through Rural Development Programme regional action of EU CAP and is aimed at providing Precision Agriculture–devoted services for cereals monitoring in the Piemonte Region (NW-Italy) context. In this work authors explored some general and preliminary issues mainly aimed at demonstrating and formalizing those evident relationships existing between NDVI image time series and the main ordinary agronomic parameters, with special focus on phenology and thermal efficiency of crops as related to Growing Degrees Day (GDD). Winter wheat was investigated and relationships calibrated at field level, making possible to spatially characterise environmental and management effects. Two different analysis were achieved: (i) one aimed at mapping crop phenological metrics, as derivable from NDVI S2 time series; (ii) one aimed at locally modelling relationship linking GDD and NDVI to somehow test the thermal efficiency of crops in the different parts of the study area. The first analysis showed that the end of season appears to be the most constant phenological metric in the study area possibly demonstrating a time concentration of harvest operations in the area. Differently, the peak of season and the start of season metrics showed to be largely varying in the study, thus suggesting to be stronger predictors: (i) of crop development; (ii) of the effects induced by local agronomical practices. Several base temperatures were used to compute correspondent GDD. These were tested against NDVI and modelled by a parabolic model at field level. Model coefficients distribution were analysed and mapped the correspondent agronomic interpretation suggested.
FILTERING LPIS DATA FOR BUILDING TRUSTWORTHY TRAINING DATASETS FOR CROP TYPE MAPPING: A CASE STUDY IN GREECE
Authors: Gounari O. , Karakizi C. , Karantzalos K.
Published: May 2022

Abstract. The need for effective crop monitoring in large geographical scales has become increasingly important in recent years and constitutes a technological and scientific challenge for remote sensing applications. In Europe, member states of the European Union collect geospatial data in the framework of the Land Parcel Information System (LPIS) for agricultural management and subsidizing farmers. These data can be exploited as training datasets of machine learning classifiers for crop-type mapping applications. However, the way the LPIS data are being generated, concerning primarily errors in the farmers’ declarations in terms of crop-type labels, exact geometries, etc, constrains their direct use in such classification frameworks. In this study, we present and assess a methodology for filtering LPIS data based on geometric and spectral criteria in order to build a trustworthy training dataset for machine learning crop-type classifiers. A new nomenclature was developed, oriented towards the spectral discrimination of crop-type classes and sub-classes in Greece. The filtering methodology was applied at national scale for the agricultural year of 2019 and resulted in the selection of a sub-set of the LPIS parcels that were assessed as the most suitable and reliable to represent the different levels of the nomenclature. The developed filtering procedure was validated against actual crop-type labels derived from field visits, while the resulted filtered data were successfully utilized on various crop-type mapping experiments in Greece.
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA
Authors: Keraani M. K. , Mansour K. , Khlaifia B. , Chehata N.
Published: May 2022

Abstract. In this paper, we present an approach to land cover mapping from Sentinel-2 (S-2) satellite image time series using deep learning methods in the context of few shots in agricultural areas which aims to learn a classifier to recognize unseen classes during training with limited labelled examples. In many countries, there is a lack of Land Parcel Information Systems (LPIS) and thus of agricultural crop type annotations. Annotations are still based on fastidious digitization of parcels and in-field observations that are available in few numbers. Our idea is to transfer learning from pre-trained models on existing LPIS in France and apply them to a different geographical area in Kairouan in Central Tunisia. We build on work employing multi-headed self-attention mechanisms that have contributed to results that outperform other deep learning algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) in agricultural context using S-2 Time series. We used two transformer-based deep learning models PSE-TAE (Pixel-Set Encoders + Temporal Self-Attention) and PSE-LTAE (Pixel-Set Encoders + Lightweight Temporal Self-Attention). We first studied their generalisation capacity in a few shot context and on different geographical study site. Then, by transferring the knowledge of these models and adapting them to the Tunisian context with the transfer learning techniques we have demonstrated experimentally that the adaptation of these methods is efficient for land cover mapping in agricultural areas with few in-field observations in terms of accuracy with an overall accuracy for both models reaching almost 93% for a detailed classification level with 17 classes.
SEX DISCRIMINATION OF INDIVIDUAL TREES USING UAV IMAGERY
Authors: Miraki M. , Sohrabi H. , Esmailzadeh O.
Published: May 2022

Abstract. The sex ratio is the proportion of male to female trees, which has a substantial impact on reproductive success and conservation status. Appropriate sex-related differences in dioecious trees commonly result in leading to a robustly structured population. Fieldwork for sex discrimination is time-consuming and labor-required. Benefiting from the unmanned aerial vehicle (UAV) and SfM techniques, the present study aims to detect male and female Caspian poplar (Populus caspica) trees. In March 2021, a heterogeneous forest in Noor city located in Mazandaran province was photographed, then 3D point clouds were extracted from the images using structure from motion algorithm (SfM) to generate an orthomosaic and a point cloud. The field survey was carried out to record the species, sex, and position of the overstory trees which were identifiable on the orthomosaics. A random forest classification algorithm was applied using R software to classify the trees into male and female. By assessing the producer's accuracy, user's accuracy, and overall accuracy, the classification accuracy for identified trees was computed using 10-fold cross-validation. The results showed an accuracy of 83% for identifying Caspian poplar trees and 52% accuracy for Sex discrimination. Overall, our effort to evaluate sex discrimination of dioecious trees using UAV imagery represents a promising preliminary step in forest data collection.
RELATIONSHIPS BETWEEN VEGETATION INDICES AND RAINFALL AND PET AT DIFFERENT TIME-LAGS: A STUDY AT A MEDITERRANEAN TO ARID GRADIENT
Authors: Mozhaeva S. , Shoshany M.
Published: May 2022

Abstract. The climatic gradient between the Judean Lowland and the Negev Desert in Central Israel represents a transition zone between dense shrublands in the North, and desert fringe Batha and Irano-Turanian vegetation in the South, characterizing wide Mediterranean Type Climate regions around the world. Understanding the expected response of these water-limited ecosystems to climate change presents a significant challenge due to the high geodiversity of Mediterranean environments. Studying relationships between vegetation patterns and climatic parameters is fundamental for this purpose, and remote sensing provides a valuable tool for investigating these relationships over large regions. This study aims at examining the relationships between NDVI extracted from Sentinel2 and rainfall and PET accumulated over 1 to 6 months. The analysis was first conducted for 38 sites (100×100 meters) across the climatic gradient for three years representing high (2016), low (2017), and average (2018) rainfall. Results indicate that the highest correlation between NDVI and climatic parameters is achieved for accumulation interval of two months. Least-squares analysis was then utilized for calculating the per-pixel regression coefficients between NDVI and corresponding rainfall and PET. Classification of the multi-temporal NDVI (2016–2018) and of the linear regressions’ coefficients between NDVI and rainfall and PET at accumulation interval of 2 months yielded both high accuracies. Since these slope and intercept coefficients can be perceived as representing the water-use regime at each pixel, the similarity between the classification results suggests that multi-temporal NDVI typologies correspond water-use regime typologies across desert fringe ecosystems at the margins of Mediterranean regions.
SPATIAL ANALYSIS OF TREE SPECIES BEFORE FOREST FIRES
Authors: Novo A. , González-Jorge H. , Martínez-Sánchez J. , Balado J.
Published: May 2022

Abstract. Spain is included in the top five European countries with the highest number of wildfires. The occurrence and magnitude of forest fires involves aspects of a very diverse nature, from those of a socio-economic, climatic, or physiographic nature, to those concerning fuel or the availability and quantity of resources and means of extinction. The distribution of wildfires in Galicia is not random and that fire occurrence may depend on ownership conflicts also a spatial dependence between productive or non-productive area exists. Satellite data play a major role in providing knowledge about fires by delivering rapid information to map fire-damaged areas precisely and promptly. In addition, the availability of large-scale data and the high temporal resolution offered by the Sentinel-2 satellite enables to classify and determine the land cover changes with high accuracy. This study describes a methodology to detect burned areas and analyse the Land Cover and Land Use (LCLU) classes present in these areas during the period of 5 years (2016–2021) by Sentinel-2 images. The training areas were obtained by photointerpretation and the image classification was performed using the Random Forest algorithm which shows an overall accuracy range between 80–85%. The methodology concluded that Lobios and Muiños were the most affected municipalities by wildfires. Additionally, the spatial analysis determined that the Deciduous Forest mainly composed by Quercus sp. were the most affected in 2017 followed by Coniferous Forest mainly composed by Pinus sp.in 2016. Although, Scrub and Rock are the classes more affected for wildfire during 2016–2020 period.
Integrated drought risk assessment to support adaptive policymaking in the Netherlands
Authors: Mens Marjolein J. P. , van Rhee Gigi , Schasfoort Femke , Kielen Neeltje
Published: May 2022

Abstract. Adaptive policymaking to prepare for current and future
drought risks requires an integrated assessment of policy actions and
combinations of those under changing conditions. This entails quantification
of drought risks, integrating drought probability and socio-economic
consequences for all relevant sectors that are potentially impacted by
drought. The investment costs of proposed policy actions and strategies
(various actions combined) can then be compared with the expected risk
reduction to determine the cost-effectiveness. This paper presents a method
to quantify drought risk in the Netherlands under changing future conditions
and in response to policy actions. It illustrates how to use this
information as part of a societal cost–benefit analysis and in building an
adaptive long-term strategy. The method has been successfully applied to
support decision making on the Netherlands' national drought risk management
strategy as part of the National Delta Programme for climate change
adaptation.
The application of biochar and oyster shell reduced cadmium uptake by crops and modified soil fertility and enzyme activities in contaminated soil
Authors: Wu Bin , Li Jia , Sheng Mingping , Peng He , Peng Dinghua , Xu Heng
Published: May 2022

Abstract. Soil pollution with cadmium (Cd) has been threatening the human health. In this study, we investigated the possibility of applying biochar and oyster shell to reduce Cd uptake by crops and modify soil quality. A field study based on the rice–oilseed rape rotation was done, and the treatments were comprised without amendments (PA0) and with 15 000 kg ha−1 biochar (PA1), 15 000 kg ha−1 oyster shell (PA2), and 7500 kg ha−1 biochar and 7500 kg ha−1 oyster shell
(PA3). Results revealed that both oyster shell and biochar reduced the
HOAc-extractable Cd in soil. Compared to PA0, the HOAc-extractable Cd in the PA1, PA2, and PA3 treatments was reduced by 4.76 %–20.79 %, 17.86 %–38.61 %, and 5.95 %–10.89 %, respectively. The cooperative application of biochar and oyster shell reduced the Cd accumulation in brown rice and oilseed by 29.67 % and 19.74 %, respectively, compared to control and thus decreased the hazard quotient (HQ) from the consumption of brown rice and oilseed. The addition of biochar slightly increased soil organic matter. In addition, the available P in the PA2 and PA3 treatments was significantly (p<0.05) increased by 200.96 %–295.92 % and 187.46 %–280.04 % compared to the control. Moreover, the cooperative application of biochar and oyster shell enhanced the activities of urease, catalase, and β-galactosidase by 139.44 %–147.56 %, 10.71 %–34.31 %, and 82.08 %–244.38 %, respectively. These results demonstrated that the utilization of biochar and oyster shell might be an effective pathway to reducing Cd uptake by crops and improving soil fertility and enzyme activities.
Glacier–permafrost relations in a high-mountain environment: 5 decades of kinematic monitoring at the Gruben site, Swiss Alps
Authors: Gärtner-Roer Isabelle , Brunner Nina , Delaloye Reynald , Haeberli Wilfried , Kääb Andreas , Thee Patrick
Published: May 2022

Abstract. Digitized aerial images were used to monitor the evolution of
perennially frozen debris and polythermal glacier ice at the intensely
investigated Gruben site in the Swiss Alps over a period of about 50 years.
The photogrammetric analysis allowed for a compilation of detailed
spatio-temporal information on flow velocities and thickness changes. In
addition, high-resolution GNSS (global navigation satellite system) and
ground surface temperature measurements were included in the analysis to
provide insight into short-term changes. Over time, extremely contrasting
developments and landform responses are documented. Viscous flow within the
warming and already near-temperate rock glacier permafrost continued at a
constant average but seasonally variable speed of typically decimetres per
year, with average surface lowering limited to centimetres to a few decimetres
per year. This constant flow causes the continued advance of the
characteristic convex, lava-stream-like rock glacier with its oversteepened
fronts. Thawing rates of ice-rich perennially frozen ground to strong
climate forcing are very low (centimetres per year) and the dynamic response
strongly delayed (timescale of decades to centuries). The adjacent cold
debris-covered glacier tongue remained an essentially concave landform with
diffuse margins, predominantly chaotic surface structure, intermediate
thickness losses (decimetres per year), and clear signs of down-wasting and
decreasing flow velocity. The former contact zone between the cold glacier
margin and the upper part of the rock glacier with disappearing remains of
buried glacier ice embedded on top of frozen debris exhibits complex
phenomena of thermokarst in massive ice and backflow towards the topographic
depression produced by the retreating glacier tongue. As is typical for
glaciers in the Alps, the largely debris-free glacier part shows a rapid
response (timescale of years) to strong climatic forcing with spectacular
retreat (>10 m a−1) and mass loss (up to >1 m w.e. specific mass loss per year). The system of
periglacial lakes shows a correspondingly dynamic evolution and had to be
controlled by engineering work for hazard protection.
Compensating errors in inversions for subglacial bed roughness: same steady state, different dynamic response
Authors: Berends Constantijn J. , van de Wal Roderik S. W. , van den Akker Tim , Lipscomb William H.
Published: May 2022

Abstract. Subglacial bed roughness is one of the main factors controlling the rate of future Antarctic ice-sheet retreat, and also one of the most uncertain. A common technique to constrain the bed roughness using ice-sheet models is basal inversion, tuning the roughness to reproduce the observed present-day ice-sheet geometry and/or surface velocity. However, many other factors affecting ice-sheet evolution, such as the englacial temperature and viscosity, the surface and basal mass balance, and the subglacial topography, also contain substantial uncertainties. Using a basal inversion technique intrinsically causes any errors in these other quantities, to lead to compensating errors in the inverted bed roughness. Using a set of idealised-geometry experiments, we quantify these compensating errors and investigate their effect on the dynamic response of the ice-sheet to a prescribed forcing. We find that relatively small errors in ice viscosity and subglacial topography require substantial compensating errors in the bed roughness in order to produce the same steady-state ice sheet, obscuring the realistic spatial variability in the bed roughness. When subjected to a retreat-inducing forcing, we find that these different parameter combinations, which per definition of the inversion procedure result in the same steady-state geometry, lead to a rate of ice volume loss that can differ by as much as a factor of two. This implies that ice-sheet models that use basal inversion to initialise their model state can still display a substantial model bias despite having an initial state which is close to the observations.
La pluma se cae de la mano: recuperación de la Memoria de las epide-mias de Málaga del farmacéutico Joaquín García Briz y Galindo
Authors: Barrena Gómez Ana
Published: May 2022

El presente artículo se centra en el estudio de la Memoria de las epidemias de Málaga, documento redactado en 1859 por el farmacéutico malagueño Joaquín García Briz y Galindo para su envío a la Real Academia Nacional de la Farmacia, donde actualmente se custodia. Por medio del mismo, se pretende extraer la perspectiva personal del autor sobre los diferentes brotes epidémicos de fiebre amarilla y cólera morbo asiático que asolaron el país durante esta centuria. Además, analizaremos las fuentes en las que se basa para recomponer y realizar su cometido, así como sus vivencias persona-les, que nos permitirán la construcción y esbozo de su biografía.
A Restricted Co-integration Analysis of Financial Development and Economic Growth: Evidence from Pakistan
Authors: Shah Syed Muhammad Imran , Kazmi Syed Mumtaz Ali Kazmi , Bokhari Syeda Sadia , Samiullah Muhammad Samiullah
Published: May 2022

The current financial crises once again highlighted the importance of financial sector. Credit availability to firms improves the productivity and encourages private investment both through aggregated demand and aggregated supply. Accumulation of capital and its optimal utilization is important for sustainable growth of an economy. The objective of the current study is to find the effect of financial development on economic growth. The study utilized the restricted cointegration analysis to quantify the long run association between the financial development and economic growth. The result indicates that credit, GDP growth and private investment are cointegrated and in addition, credit trigger economic growth in long run via direct and indirect channel. The study suggested that easy access to credit should be given to augment economic growth.
ANALISIS INOVASI PRODUK DALAM MENINGKATKAN VOLUME PENJUALAN KUE SERABI DAN BIR PLETOK PADA KEDAI SERABI BIBA, KOTA KENDARI
Authors: Kambolong Makmur , Putra Abdussamad Muhammad Andika , Ningtyas Candra Puspita
Published: May 2022

Penelitian ini bertujuan untuk mengetahui bagaimana inovasi produk Kedai Serabi Biba dalam meningkatkan volume penjualan kue serabi dan bir pletok. Jenis data dalam penelitian ini adalah data kualitatif, dalam mencari dan menyusun data dari hasil observasi yang telah didapat, wawancara, catatan lapangan, dan dokumentasi secara sistematis. Berdasarkan hasil penelitian dan pembahasan Analisis Inovasi Produk Dalam Meningkatkan  Volume Penjualan Kue Serabi Dan Bir Pletok, maka dapat disimpulkan Kedai Serabi Biba melakukan langkah inovasi pada kue serabi dan bir pletok dengan dasar kualitas produk, produk yang variatif dan gaya dan desain menarik. Inovasi produk yang dilakukan Kedai Serabi Biba yaitu berupa penambahan varian topping pada kue serabi dan bir pletok. Inovasi Produk yang dilakukan ini dapat meningkatkan volume penjualan pada Kedai Serabi Biba. Hal ini dapat membantu Kedai Serabi Biba dalam pencapaian volume penjualan dan dapat menunjang pertumbuhan usaha.
ANALISIS PENGARUH BEBAN KERJA DAN KONFLIK KERJA TERHADAP STRES KERJA KARYAWAN DI PT. SUMATERA INTI KARET
Authors: Wijaya Riani , Manalu Hernawaty
Published: May 2022

Penelitian ini bertujuan menganalisis pengaruh beban kerja dan konflik kerja terhadap stres kerja secara simultan. Populasi dalam penelitian ini adalah karyawan PT. Sumatera Inti Karet yang berjumlah 50 orang dan sampel penelitian ini adalah sesuai dengan jumlah populasi penelitian (sampel jenuh). Metode analisis yang digunakan adalah Regresi Linear Berganda. Instrumen yang digunakan adalah kuesioner untuk mengumpulkan data dari variabel beban kerja, konflik kerja, dan stres kerja. Teknik analisis data yang digunakan adalah Uji Validitas, Uji Reliabilitas, Uji Normalitas, Uji Multikolinearitas, Uji Heterokedastisitas, Uji Analisis Regresi Linear Berganda, Uji-t, Uji F, dan Uji Koefisien Determinasi. Hasil penelitian menunjukan terdapat pengaruh positif dan signifikan antara beban kerja dan konflik kerja secara simultan terhadap stres kerja. Berdasarkan hasil Uji Koefisien Determinasi (R2) diperoleh hasil beban kerja dan konflik kerja berpengaruh terhadap stres kerja sebesar 26,5% dan sisanya dipengaruhi oleh variabel lain seperti faktor eksternal, jiwa sosial, dan faktor lingkungan.