scholarly journals Combining Satellite Multispectral Imagery and Topographic Data for the Detection and Mapping of Fluvial Avulsion Processes in Lowland Areas

2020 ◽  
Vol 12 (14) ◽  
pp. 2243 ◽  
Author(s):  
Giulia Iacobucci ◽  
Francesco Troiani ◽  
Salvatore Milli ◽  
Paolo Mazzanti ◽  
Daniela Piacentini ◽  
...  
Keyword(s):  
Methodological Approach ◽  
Field Work ◽  
Network Evolution ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Economic Activities ◽  
Topographic Analysis ◽  
Climate Conditions ◽  
Topographic Data ◽  
Remote Sensing Techniques

Fluvial avulsion is an important process in the dynamics of the riverscapes and plays a key role in the drainage network evolution in lowland areas, also influencing past and present social processes and economic activities. Crevasse splays represent significant geomorphological features for understanding the fluvial morphodynamics in lowland areas dominated by avulsion processes. Within wide floodplains characterized by very low elevation ranges, the detection and accurate mapping of crevasse splay morphology and features, such as crevasse channels, levees, and deposit, can be very challenging considering floodplain extension, anthropic impact on the natural channels network, logistic difficulties, and in some cases, climate conditions that prevent field work. This research aims at improving the detection and mapping of crevasse splays in lowland areas through the combination of different remote sensing techniques based on optical multispectral imagery and topographic data derived from satellite earth observation missions. The Lower Mesopotamia Plain (LMP) offers a unique opportunity to study the avulsion processes because it presents numerous examples of crevasse splays, characterized by different sizes and states of activity. Furthermore, in this area, a strong correlation exists between the formation and development of crevasse splays and the expansion of agriculture and early societies since the Early Holocene. Different supervised classification (SC) methods of Landsat 8 satellite images have been tested together with topographic analysis of the microrelief, carried out based on two different 1-arcsec DEMs (AW3D30 and GDEM2). The results of this study demonstrate that the combination of multispectral imagery analysis and topographic analysis of the microrelief is useful for discerning different crevasse elements, distinguishing between active and relict landforms. The methodological approach proved helpful for improving the mapping of erosional and depositional landforms generated by the avulsion process and, in the study area, provided the best results for the active landforms.

Remote sensing approach for the fluvial avulsion processes detection and mapping

2021 ◽  
Author(s):  
Giulia Iacobucci ◽  
Francesco Troiani ◽  
Salvatore Milli ◽  
Daniela Piacentini ◽  
Paolo Mazzanti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Flood Hazard ◽  
Methodological Approach ◽  
Confusion Matrix ◽  
Arid Zones ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Topographic Analysis ◽  
Main Challenge

<p>The study of the riverscape dynamic in lowland areas is crucial for reconstructing the morphoevolution of the drainage network, especially where human activities have always been strongly connected to the river system. Not surprisingly, the Lower Mesopotamian Plain (LMP) represents the ideal study area, being a large floodplain where the Tigris and Euphrates rivers with their distributaries deposited a large volume of sediments during the Holocene. Here, a complex drainage pattern, characterized by paleochannels, levees and crevasse splays developed, representing the expression of several fluvial avulsion processes during the time. Indeed, the presence of recent and ancient crevasse splays in a given area suggests frequent seasonal floods, but at the same time, their formation and growth represent, in the LMP, an important process that conditioned the location of several human settlements since the 6th millennium BC. In this area, about 200 examples of active and abandoned crevasse splays, with various sizes, have been recognized exclusively through a remote sensing approach. The scarce elevation ranges of the LMP represent the main challenge in the detection and mapping of the crevasse splays features (i.e., channels, levees and deposits), in addition to the definition of the floodplain extension and the anthropic impact on channel networks.</p><p>Therefore, the research aims to integrate multi-sensor remote sensing data such as optical multispectral imagery and digital elevation datasets for improving the detection and mapping of crevasse splays. Landsat 8 imagery is adopted for computing two spectral indices (NDVI and Clay Ratio) and carrying on different supervised classification methods (i.e., Mahalanobis, Maximum Likelihood, Minimum Distance and SAM). Each method has been evaluated through the computation of the confusion matrix, assessing the Overall Accuracy, K coefficient, Producer Accuracy and User Accuracy. Elevation data used in the topographic analysis to determine the local micro-relief geometry are derived from two different global DEMs available at the ground resolution of 1 arcsec (AW3D30 and GDEM2). Topographic analysis has been performed to complete and validate the supervised classification results.</p><p>The outputs successfully demonstrate the potential of the integration of multispectral imagery analysis and topographic analysis from DEM for detecting and mapping with a satisfactory detail the avulsion processes and for distinguishing their state of activity. The methodological approach is a promising technique for flood hazard and risk mapping, as well as for monitoring flood dynamics, especially within arid and semi-arid zones where flawless water management is essential for guaranteeing sustainable crops, livestock and avoiding wasting water.</p>

Beyond the Social Determinants of Health

2020 ◽  
Vol 3 ◽  
pp. 1-11
Author(s):  
Laura Hall ◽  
Urpi Pine ◽  
Tanya Shute
Keyword(s):  
Labour Force ◽  
Methodological Approach ◽  
Active Role ◽  
Indigenous Women ◽  
Economic Activities ◽  
The Earth ◽  
The Social ◽  
Weather Changes ◽  
People First

Abstract This paper will reflect on key findings from a Summer 2017 initiative entitled The Role of Culture and Land-Based Healing in Addressing and Ending Violence against Indigenous Women and Two-Spirited People. The Indigenist and decolonizing methodological approach of this work ensured that all research was grounded in experiential and reciprocal ways of learning. Two major findings guide the next phase of this research, complicating the premise that traditional economic activities are healing for Indigenous women and Two-Spirit people. First, the complexities of the mainstream labour force were raised numerous times. Traditional economies are pressured in ongoing ways through exploitative labour practices. Secondly, participants emphasized the importance of attending to the responsibility of nurturing, enriching, and sustaining the wellbeing of soil, water, and original seeds in the process of creating renewal gardens as a healing endeavour. In other words, we have an active role to play in healing the environment and not merely using the environment to heal ourselves. Gardening as research and embodied knowledge was stressed by extreme weather changes including hail in June, 2018, which meant that participants spent as much time talking about the healing of the earth and her systems as the healing of Indigenous women in a context of ongoing colonialism.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Spatial Dynamics Regarding Geographical Concentration of Economic Activities. Italian NUTS-3 Analysis

2021 ◽  
Author(s):  
Stefano De Falco
Keyword(s):  
Empirical Investigation ◽  
Methodological Approach ◽  
Geographical Area ◽  
Spatial Dynamics ◽  
Agglomeration Economies ◽  
Economic Activities ◽  
Production Sector ◽  
Scientific Debate ◽  
Geographical Concentration ◽  
Italian Regions

AbstractFor several years, the themes concerning agglomeration economies have been approached from different perspectives in the scientific debate, as capable of triggering various positive features. The present research starts precisely where many others arrive, that is, given the value of these externalities, analyzing the spatial distribution of the geographical concentration of economic activities and the related influencing factors. To this end, in this contribution an explanatory investigation is carried out into the spatial dynamics deriving from main productive sectors’ concentration in some Italian regions. The proposed methodological approach is based respectively on the LISA spatial autocorrelation models and on the analysis of non-neighboring clusters to understand if the geographical area of reference and / or the particular production sector are influencing variables. The empirical investigation confirms the presence of a parametric interaction between factors related in some cases on the geographical context and in others on the productive sector.

scholarly journals A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 231
Author(s):  
Can Trong Nguyen ◽  
Amnat Chidthaisong ◽  
Phan Kieu Diem ◽  
Lian-Zhi Huo
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Areas ◽  
Near Infrared ◽  
Urban Landscape ◽  
Bare Soil ◽  
Critical Element ◽  
Landsat 8 ◽  
Climate Conditions ◽  
Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

Methodological approaches to assessing the digitalization scale in types of economic activity in the region and the potential of digital technologies for the real economy

2021 ◽  
Vol 17 (8) ◽  
pp. 1519-1541
Author(s):  
Vitalii V. PECHATKIN ◽  
Liliya M. VIL'DANOVA
Keyword(s):  
Methodological Approach ◽  
Import Substitution ◽  
Digital Technologies ◽  
Industrial Sector ◽  
Digital Transformation ◽  
Real Economy ◽  
Economic Activities ◽  
The Real ◽  
Industrial Enterprises ◽  
Methodological Approaches

Subject. As digital technologies spread across all industries, active processes of digital transformation need to be managed both nationally and regionally. Assessing the extent of digitalization across types of economic activities is the key issue for setting up the socio-economic development strategy of the region and evaluating its efficiency. Objectives. The study is aimed to formulate and test methodological approaches to assessing the digitalization in types of economic activities and the potential of digital technologies for the real economy. Methods. The study relies upon the dialectical method, systems approach, questionnaires, expert approach, interpretation of empirical facts through tables, etc. Results. We devised a methodological approaches to assessing the extent of digitalization in types of economic activities across regions. The approach combines the quantification and evaluation of the process and helps determine the extent of local digital transformation at the regional level. We devised and tested the methodological approach to rating digital technologies, which have the high potential for raising the competitiveness and resilience to competition of the industrial sector in the Russian regions. As opposed to the existing approaches, the approach accounts for the current scale of digital technologies in the national economy, the potential for growth in the demand and supply in the domestic and foreign markets, and the potential for import substitution with respect to foreign technologies and products. Conclusions and Relevance. What makes the proposed methodological approaches more preferable is that they help assess not only the extent of digitalization in types of economic activities and the predominance of certain types in industrial enterprises, but also determine their potential for import substitution in terms of digital security.

scholarly journals Моделирование деятельности микропредприятий в Российских регионах

2020 ◽  
pp. 24-37
Author(s):  
Юлия Пиньковецкая
Keyword(s):  
Production Function ◽  
Spatial Data ◽  
Methodological Approach ◽  
Research Process ◽  
Production Functions ◽  
Management Tool ◽  
Economic Activities ◽  
Fixed Assets ◽  
Administrative Units ◽  
The Relationship

Целью исследования являлась оценка двухфакторной производственной функции, характеризующей взаимосвязь обо-рота микропредприятий от величины заработной платы работников и потока инвестиций в основной капитал. Рас-смотрена производственная функция, аналогичная функции Кобба-Дугласа, без ограничений на сумму степеней при факторах. Исследование базировалось на статистических пространственных данных, использовалась информация по 82 регионам России за 2017 г. Производственная функция представляет собой эффективный инструмент управления. Полученные новые знания имеют научное и практическое значение. The goal of the research was to estimate the two-factor production function, which characterizes the relationship between the microenterprise turnover and the employees rate of wages and the flow of investments into the fixed assets. The research examined a production function similar to that of Cobb-Douglas function, without the restrictions on the sum of degrees under factors. The research was based on statistical spatial data; using the information on 82 regions of Russia for 2017. The production function is an effective management tool. The new knowledge obtained is of scientific and practical im-portance. The methodological approach and tools proposed in the article for evaluating the production functions, describing the set of the microenterprises activities in the regions, can be applied in scientific research on the entrepreneurship issues, as well as in justifying the programs of this economy sector devel-opment at the federal and regional levels. The methodology and tools that were used in the research process can be applied in similar studies in the countries with a significant number of territorial (administrative) units. Further research is related to the evaluation of production functions for a set of microenterprises that are specialized in various types of economic activities, as well as those located in municipalities of specific regions.

scholarly journals Water stress coefficient determined by orbital remote sensing techniques

2020 ◽  
Vol 24 (12) ◽  
pp. 847-853
Author(s):  
Élvis da S. Alves ◽  
Roberto Filgueiras ◽  
Lineu N. Rodrigues ◽  
Fernando F. da Cunha ◽  
Catariny C. Aleman
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Irrigation Management ◽  
Simple Algorithm ◽  
Landsat 8 ◽  
Stress Coefficient ◽  
Use Efficiency ◽  
Remote Sensing Techniques ◽  
Water Stress Coefficient ◽  
The Relationship

ABSTRACT In regions where the irrigated area is increasing and water availability is reduced, such as the West of the Bahia state, Brazil, the use of techniques that contribute to improving water use efficiency is paramount. One of the ways to improve irrigation is by improving the calculation of actual evapotranspiration (ETa), which among other factors is influenced by soil drying, so it is important to understand this relationship, which is usually accounted for in irrigation management models through the water stress coefficient (Ks). This study aimed to estimate the water stress coefficient (Ks) through information obtained via remote sensing, combined with field data. For this, a study was carried out in the municipality of São Desidério, an area located in western Bahia, using images of the Landsat-8 satellite. Ks was calculated by the relationship between crop evapotranspiration and ETa, calculated by the Simple Algorithm for Evapotranspiration Retrieving (SAFER). The Ks estimated by remote sensing showed, for the development and medium stages, average errors on the order of 5.50%. In the final stage of maize development, the errors obtained were of 23.2%.

scholarly journals NaturaSat—A Software Tool for Identification, Monitoring and Evaluation of Habitats by Remote Sensing Techniques

2021 ◽  
Vol 13 (17) ◽  
pp. 3381
Author(s):  
Karol Mikula ◽  
Mária Šibíková ◽  
Martin Ambroz ◽  
Michal Kollár ◽  
Aneta A. Ožvat ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Automatic Segmentation ◽  
European Space Agency ◽  
Software Tool ◽  
Monitoring And Evaluation ◽  
Field Work ◽  
Direct Access ◽  
Deep Learning Algorithm ◽  
Space Agency ◽  
Remote Sensing Techniques

The NaturaSat software integrates various image processing techniques together with vegetation data, into one multipurpose tool that is designed for performing facilities for all requirements of habitat exploration, all in one place. It provides direct access to multispectral Sentinel-2 data provided by the European Space Agency. It supports using these data with various vegetation databases, in a user-friendly environment, for, e.g., vegetation scientists, fieldwork experts, and nature conservationists. The presented study introduces the NaturaSat software, describes new powerful tools, such as the semi-automatic and automatic segmentation methods, and natural numerical networks, together with validated examples comparing field surveys and software outputs. The software is robust enough for field work researchers and stakeholders to accurately extract target units’ borders, even on the habitat level. The deep learning algorithm, developed for habitat classification within the NaturaSat software, can also be used in various research tasks or in nature conservation practices, such as identifying ecosystem services and conservation value. The exact maps of the habitats obtained within the project can improve many further vegetation and landscape ecology studies.

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