Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500m spatial resolution
سنجش از دور و سنجش اجتماعی برای سیستمهای اقتصادی اقتصادی: مطالعه مقایسه ای بین چراغ های شب و رسانه های اجتماعی مبتنی بر مکان در وضوح مکانی 500 متر-2020
With the advent of “social sensing” in the Big Data era, location-based social media (LBSM) data are increasingly used to explore anthropogenic activities and their impacts on the environment. This study converts a typical kind of LBSM data, geo-tagged tweets, into raster images at the 500m spatial resolution and compares them with the new generation nighttime lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly image composites. The results show that the monthly tweet images are significantly correlated with the VIIRS-DNB images at the pixel level. The tweet images have nearly the same ability on estimating electric power consumption and better performance on assessing personal incomes and population than the NTL images. Tweeted areas (i.e. the pixels with at least one posted tweet) are closer to satellite-derived built-up/urban areas than lit areas in NTL imagery, making tweet images an alternative to delimit extents of human activities. Moreover, the monthly tweet images do not show apparent seasonal changes, and the values of tweet images are more stable across different months than VIIRS-DNB monthly image composites. This study explores the potential of LBSM data at relatively fine spatiotemporal resolutions to estimate or map socioeconomic factors as an alternative to NTL images in the United States
Keywords: Nighttime lights imagery | Geo-tagged tweets | Socioeconomic factors | Social sensing
The law of the sea and current practices of marine scientific research in the Arctic
قانون دریا و شیوه های فعلی تحقیقات علمی دریایی در قطب شمال-2020
The rapid changes in both climate and human activity occurring in the Arctic Ocean demands improved knowledge about this region. Combined with eased accessibility due to reduced sea ice cover and new technologies, this has led to increased research activity in the region. These circumstances put pressure on the applicable legal framework, i.e. the United Nations Convention on the Law of the Sea. Therefore, a conversation is needed between legal and marine scientists to promote the alignment between the legal framework and current practices of marine scientific research in the Arctic. This article showcases three current practices of marine scientific research in the Arctic, which are subsequently analysed in light of the existing legal framework, highlighting the legal questions arising from the use of these three technologies. The three technologies analysed here are seabed structures off Svalbard, floating ice-tethered observatories deployed across the marine Arctic, and remote sensing activities paired with in situ measurements.
Keywords: Marine scientific research | Arctic | Law of the sea | Technology | Ocean observatories | Remote sensing
Steady infiltration rate spatial modeling from remote sensing data and terrain attributes in southeast Brazil
سرعت نفوذ ثابت مدل سازی فضایی از داده های سنجش از دور و زمین ورزشی در جنوب شرقی برزیل-2020
This paper aims to describe the development of steady infiltration rate (SIR) spatial prediction models using accessible input data. The models were created from SIR data collected through simulated rainfall at 71 points in part of the Cachimbal stream watershed (a Paraíba do Sul River tributary watershed) in Rio de Janeiro state – Brazil, using as covariates: terrain attributes derived from digital elevation model (DEM), remote sensing data and soil class, physical and chemical attributes maps. Itwas discussed how different land uses and soil degradation levels affect SIR and how NDVI can be used to represent themon SIR modeling. Among the soil physical properties, bulk density (BD) and total sand (TS)were selected as covariates. SIR was higherwhen lower the bulk density and higher the sand content. Soil types play a big role in SIR, highlighting the Gleissolos Háplicos (Gleysols) as the soil class that presented the lower average SIR values and the Latossolos Vermelho Amarelos and Nitossolos Háplicos (Ferralsols and Nitisols) that presented the highest. Topographic position Index (TPI), curvature, and TopographicWetness Index (TWI)were the terrain covariates used in the models. Their usage indicate lower SIR in concave, lower and wetter parts of the landscape. The results demonstrated that is possible to achieve satisfactory results for SIR spatialmodeling using easily accessible data (remote sensing and terrain attributes), but soil information is also necessary to develop better prediction models.
Keywords: Rainfall simulator | Vegetation indexes | Acrisols | Cambisols
Toward a trans-regional vulnerability assessment for Alps. A methodological approach to land cover changes over alpine landscapes, supporting urban adaptation
به سمت ارزیابی آسیب پذیری فرا منطقه ای برای رشته کوه های آلپ. یک رویکرد روششناختی برای تغییر پوشش زمین نسبت به مناظر کوهستانی ، حمایت از سازگاری شهری-2020
The contribution presents a possible assessment methodology for land cover change over ice and snow, between 1990 and 2018 in the Dolomites and the Alpi Giulie. The methodology aims to build surface atlas to assess the land cover changes. The tool is intended as a support for environmental management, forecasting and, as support for territorial government systems in climate- proof planning processes. In the “business as usual” global warming scenario, ice and snow resources will become one of the most affected subjects by Climate Change, with heavy consequences on ecosystems, urban environments and socioeconomic. Current monitoring and assessment systems are fragmented both by survey methodology and by local distribution. The methodology is developed in using GIS, following remote sensing (RS) processes and spatial analysis tools to manage multispectral satellite images. The process uses spectral signatures from satellite images to identify homogeneous areas in material and morphology. The process takes into account the actual systems of assessment and local socioeconomic exposures. The methodology takes a proactive approach to future hazards and impacts considering their management in alpine habitats to support local administrations. The project develops transboundary assessment techniques and aids the adaptation of planning strategies in the context of Climate Change.
Keywords: Urban planning 1 | Transboundary governance 2 | Remote sensing analysis 3 | Climate change 4 | Adaptation strategies 5 | Alps monitoring 6
Background correction method for improving the automated detection of radioisotopes from airborne gamma-ray surveys
روش تصحیح پس زمینه برای بهبود تشخیص خودکار رادیوایزوتوپها از نظرسنجی پرتوهای گاما در هوا-2019
An altitude-based background correction strategy was developed for use in the application of pattern recognition methods to the classification of gamma-ray spectra collected during airborne surveys. Application of this methodology helped to suppress the background spectral variation that serves to obscure the photopeaks associated with low levels of gamma-ray emission. The correction method was implemented by optimizing a database of background gamma-ray spectra collected at various locations and altitudes. Given this background database, a field-collected spectrum was corrected by performing linear regression onto a background spectrum from the database at a matching altitude. The residuals about the regression fit were then digitally filtered and submitted to nonparametric linear discriminant analysis for the purpose of computing classification models for targeted radioisotopes. The resulting classifiers were applied to predict the presence or absence of specific radioisotope signatures in data acquired during airborne surveys. Employing data provided by the U.S Environmental Protection Agency Airborne Spectral Photometric Environmental Collection Technology (ASPECT) program, classification models were computed to detect the presence of cesium-137 (137Cs) and cobalt- 60 (60Co). The optimized classifiers were tested over 12 diverse locations, with nine of these data sets containing the target radioisotopes. Correct classification percentages of 99.4% and 99.8% were obtained for the 137Cs and 60Co classifiers, respectively, on the basis of comparisons to visual inspections of the corresponding spectra.
Keywords: Remote sensing | Cesium-137 | Cobalt-60 | Airborne | Pattern recognition | Gamma-ray spectroscopy
Modelling fuzzy combination of remote sensing vegetation index for durum wheat crop analysis
مدل سازی ترکیب فازی از شاخص پوشش گیاهی سنجش از دور برای تجزیه و تحلیل محصول گندم دوروم-2019
The application of new technologies (e.g. Internet of Things, mechatronics, remote sensing) to the primary sector will reduce the production costs, limit the waste of primary materials, and reduce the release of polluting compounds into the environment. Precision agriculture (PA) has been growing in the last years thanks to industry efforts and development of applications for diagnostic purposes. Many applications in PA use vegetation indices to measure phenology parameters in terms of Leaf Area Index (LAI). In this context, the correlation of some vegetation indices were analyzed with respect to the durum wheat canopy, evaluating two different phenological stages (elongation and maturity). The results show that for the first stage of growth, the Enhanced Vegetation Index (EVI) was the best-correlated vegetation index with LAI, while the Land Surface Water Index (LSWI) was more reliable for the following stage of growth. Considering trials findings, a fuzzy expert system was developed to combine EVI and LSWI, obtaining a new combined index (Case-specific Fuzzy Vegetation Index) that better represents the LAI in comparison with the single indices. Thus, this approach could give place to a better representative vegetation index of a different biological condition of the plant. It may also serve as a reliable method for wheat yield forecasting and stress monitoring.
Keywords: Precision agriculture | LAI | Remote sensing | Crop management | Landsat images | Ecosystem services
Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing
ارزیابی تناسب اراضی برای پیش بینی عملکرد از این گونه گیاهان با استفاده از سیستم های خبره فازی جغرافیایی و سنجش از دور-2019
Cassava has the potential to be a promising crop that can adapt to changing climatic conditions in Indonesia due to its low water requirement and drought tolerance. However, inappropriate land selection decisions limit cassava yields and increase production-related costs to farmers. As a root crop, yield prediction using vegetation indices and biophysical properties is essential to maximize the yield of cassava before harvesting. Therefore, the purpose of this research was to develop a yield prediction model based on suitable areas that assess with land suitability analysis (LSA). For LSA, the priority indicators were identified using a fuzzy expert system combined with a multicriteria decision method including ecological categories. Furthermore, the yield prediction method was developed using satellite remote sensing datasets. In this analysis, Sentinel-2 datasets were collected and analyzed in SNAP® and ArcGIS® environments. The multisource database of ecological criteria for cassava production was built using the fuzzy membership function. The results showed that 42.17% of the land area was highly suitable for cassava production. Then, in the highly suitable area, the yield prediction model was developed using the vegetation indices based on Sentinel-2 datasets with 10m resolution for the accuracy assessment. The vegetation indices were used to predict cassava growth, biophysical condition, and phenology over the growing seasons. The NDVI, SAVI, IRECI, LAI, and fAPAR were used to develop the model for predicting cassava growth. The generated models were validated using regression analysis between observed and predicted yield. As the vegetation indices, NDVI showed higher accuracy in the yield prediction model (R2=0.62) compared to SAVI and IRECI. Meanwhile, LAI had a higher prediction accuracy (R2=0.70) than other biophysical properties, fAPAR. The combined model using NDVI, SAVI, IRECI, LAI, and fAPAR reported the highest accuracy (R2=0.77). The ground truth data were used for the evaluation of satellite remote sensing data in the comparison between the observed and predicted yields. This developed integrated model could be implemented for the management of land allocation and yield assessment in cassava production to ensure regional food security in Indonesia.
Keywords: Land suitability | Cassava | Yield prediction | Fuzzy expert systems | Remote sensing
Predicting ground-level PM2:5 concentrations in the Beijing-Tianjin- Hebei region: A hybrid remote sensing and machine learning approach
پیش بینی غلظت PM2:5 در سطح زمین در منطقه پکن، Beijing-Tianjin- هبی: یک روش سنجش از دور و یادگیری ماشین هیبریدی-2019
An accurate estimation of PM2.5 (fine particulate matters with diameters 2.5 mm) concentration is critical for health risk assessment and generating air pollution control strategies. In this study, a hybrid remote sensing and machine learning approach, named RSRF model is proposed to estimate daily ground-level PM2.5 concentrations, which integrates Random Forest (RF), one of machine learning (ML) models, and aerosol optical depth (AOD), one of remote sensing (RS) products. The proposed RSRF model provides an opportunity for an adequate characterization of real-time spatiotemporal PM2.5 distributions at uninhabited places and complex surfaces. It also offers advantages in handling complicated non-linear relationships among a large number of meteorological, environmental and air pollutant factors, as well as ever-increasing environmental data sets. The applicability of the proposed RSRF model is tested in the Beijing-Tianjin-Hebei region (BTH region) during 2015e2017. Deep Blue (DB) AOD from Aqua-retrieved Collection 6.1 (C_61) aerosol products of Moderate Resolution Imaging Spectroradiometer (MODIS) is validated with Aerosol Robotic Network. The validation results indicate C_61 DB AOD has a high correlation with ground based AOD in the BTH region. The proposed RSRF model performed well in characterizing spatiotemporal variations of annual and seasonal PM2.5 concentrations. It not only is useful to quantify the relationships between PM2.5 and relevant factors such as DB AOD, meteorological and air pollutant variables, but also can provide decision support for air pollution control at a regional environment during haze periods.
Keywords: Remote sensing | Aerosol optical depth | Machine learning | PM2.5 | Random forest
Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction
اهمیت انتخاب متغیر پیش بینی کننده مکانی در برنامه های یادگیری ماشین - انتقال از تولید مثل داده ها به پیش بینی مکانی-2019
Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data but are unable to make spatial predictions beyond the locations of the training samples. We assume that not only spatial validation strategies but also spatial variable selection is essential for reliable spatial predictions. We introduce two case studies that use remote sensing to predict land cover and the leaf area index for the “Marburg Open Forest”, an open research and education site of Marburg University, Germany. We use the machine learning algorithm Random Forests to train models using non-spatial and spatial cross-validation strategies to understand how spatial variable selection affects the predictions. Our findings confirm that spatial cross-validation is essential in preventing overoptimistic model performance. We further show that highly autocorrelated predictors (such as geolocation variables, e.g. latitude, longitude) can lead to considerable overfitting and result in models that can reproduce the training data but fail in making spatial predictions. The problem becomes apparent in the visual assessment of the spatial predictions that show clear artefacts that can be traced back to a misinterpretation of the spatially autocorrelated predictors by the algorithm. Spatial variable selection could automatically detect and remove such variables that lead to overfitting, resulting in reliable spatial prediction patterns and improved statistical spatial model performance. We conclude that in addition to spatial validation, a spatial variable selection must be considered in spatial prediction models of ecological data to produce reliable results.
Keywords: Cross-validation | Environmental monitoring | Machine learning | Overfitting | Random Forests | Remote sensing
Land use/land cover dynamics using landsat data in a gold mining basin-the Ankobra, Ghana
پویایی استفاده از زمین / پوشش زمین با استفاده از داده های زمین در یک حوضه استخراج طلا - آنکارا ، غنا-2019
The Ankobra River basin, which forms part of the Southern-Western River System of Ghana, has become a hub for both large and small-scale mining activities. Some of the activities of these mines threatens the sustainability of its resources in the basin. A study in the basin which focuses on changes in LULC patterns in the basin over the past years is therefore paramount to understand the changes that has taken place and its potential impact on resources in the basin. This study assessed the pattern of Land use/cover changes, and the possible drivers of change, in the basin. The study used multi-spectral Landsat images of 30 m resolution for the years 1991, 2002, 2008 and 2016. The Semi-Automatic Classification Plugin (SCP) in QGIS was used for atmospheric correction, image classification using the spectral angle mapping algorithm, and post processing. The overall difference between the reference and the classified map of 2016 was 5.5% with the overall quantity, exchange and shift components as 1.5%, 3% and 1% respectively. Findings from the study show that, the Ankobra River basin has witnessed noticeable changes over the 25-year study period. Closed forest which occupied 40.4% of the total basin area in 1991 reduced drastically to 22.8% in 2016. The dominant LULC change patterns in the basin are from Closed Forest to Open Forest; Open Forest to Farmland, Settlement/Bare land and Mining Area; and the increase in surface area of Water consequently resulting from the increase in Mining Area. Mining activities, particularly illegal mining was identified to be the dominant driver of deforestation in the Ankobra Basin between the years 2008 and 2016 where mining activities in the basin sharply increased.
Keywords: Land use/land cover (LULC) | Remote sensing | Ankobra River Basin | Galamsey | Supervised classification | Deforestation