scholarly journals A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

2020 ◽  
Vol 12 (1) ◽  
pp. 194
Author(s):  
Yanyan Huang ◽  
Hongli Zhao ◽  
Yunzhong Jiang ◽  
Xin Lu
Keyword(s):  
Remote Sensing ◽  
Network Design ◽  
Spatial Information ◽  
Measurement Data ◽  
Remote Sensing Data ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Satellite Precipitation ◽  
Sensing Data ◽  
Rain Gauge Network

A well-designed rain gauge network can provide precise and detailed rainfall data for earth science research; meanwhile, satellite precipitation data has been developed to generate more real spatial features, which provides new data support for the improvement of ground station network design methods. In this paper, satellite precipitation data are introduced into the design of a rain gauge network and an optimized method for designing a rain gauge network that comprehensively considers the information content, spatiotemporality, and accuracy (ISA) of the data is proposed. After screening the potential stations, the average spatial information index of the rain gauge network, which is calculated from remote sensing data, is used to address the shortcomings of applying spatial information from single-use measurement data. Then, the greedy ranking algorithm is used to rank the order in which the rain gauges are added to the network. The results of the rain gauge network design in the upper reaches of the Chaobai river show that compared with two methods that do not consider spatiality or use only measured data to consider spatiality, the proposed method performs better in terms of the spatial layout and accuracy verification. This study provides new ideas and references for the design of hydrological station networks and explores the use of remote sensing data for the layout of ground-based station networks.

scholarly journals Mapping the Tso Kar basin in Ladakh

2012 ◽  
Vol 8 (2) ◽  
Author(s):  
Shashank Srinivasan
Keyword(s):  
Remote Sensing ◽  
High Altitude ◽  
Human Activities ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Climatic Changes ◽  
Pastoral Community ◽  
Sensing Data ◽  
Management Plans ◽  
Basin Wetlands

High-altitude wetlands are critical ecosystems at risk from global climatic changes and local human activities. Management plans for the conservation of these wetlands require spatial information, from remote sensing data and from local human communities. I describe my research aims and methodology working with the Changpa, a nomadic pastoral community who inhabit the high-altitude regions around the Tso Kar basin wetlands in Ladakh, India.

scholarly journals High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1991
Author(s):  
Chenhui Huang ◽  
Akinobu Shibuya
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Data Augmentation ◽  
Prediction Models ◽  
Spatial Information ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Regression Problem ◽  
Sensing Data ◽  
Multivariate Prediction

Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to merge spatial autocorrelation into a ML model only using a data augmentation strategy. To test the feasibility of this concept, remote sensing data including those from the advanced spaceborne thermal emission and reflection radiometer (ASTER), digital elevation model (DEM), and geophysics (geomagnetic) data were used for the feasibility study, along with copper geochemical and copper mine data from Arizona, USA. We explained why spatial information can be coupled into an ML model only by data augmentation, and introduced how to operate data augmentation in our case. Four tests—(i) cross-validation of measured data, (ii) the blind test, (iii) the temporal stability test, and (iv) the predictor importance test—were conducted to evaluate the model. As the results, the model’s accuracy was improved compared with a traditional ML model, and the reliability of the algorithm was confirmed. In summary, combining the univariate interpolation method with multivariate prediction with data augmentation proved effective for geological studies.

scholarly journals Land use/cover classification in the Brazilian Amazon using satellite images

2012 ◽  
Vol 47 (9) ◽  
pp. 1185-1208 ◽  
Author(s):  
Dengsheng Lu ◽  
Mateus Batistella ◽  
Guiying Li ◽  
Emilio Moran ◽  
Scott Hetrick ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Data Fusion ◽  
Spatial Information ◽  
Classification Tree ◽  
Remote Sensing Data ◽  
Brazilian Amazon ◽  
Classification Performance ◽  
Sensor Data ◽  
Sensing Data

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

scholarly journals Agricultural Drought Monitoring by MODIS Potential Evapotranspiration Remote Sensing Data Application

2020 ◽  
Vol 12 (20) ◽  
pp. 3411
Author(s):  
Kamil Szewczak ◽  
Helena Łoś ◽  
Rafał Pudełko ◽  
Andrzej Doroszewski ◽  
Łukasz Gluba ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Measurement Data ◽  
Remote Sensing Data ◽  
Absolute Error ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Trend Test ◽  
Sensing Data

The current Polish Agricultural Drought Monitoring System (ADMS) adopted Climatic Water Balance (CWB) as the main indicator of crop losses caused by drought conditions. All meteorological data needed for CWB assessment are provided by the ground meteorological stations network. In 2018, the network consisted of 665 stations, among which in only 58 stations full weather parameters were registered. Therefore, only these stations offered a possibility to estimate the exact values of potential evapotranspiration, which is a component of the CWB algorithm. This limitation affects the quality of CWB raster maps, interpolated on the basis of the meteorological stations network for the entire country. However, the interpolation process itself may introduce errors; therefore, the adaptation of satellite data (that are spatially continuous) should be taken into account, even if the lack of data due to cloudiness remains a serious problem. In this paper, we involved the remote sensing data from MODIS instrument and considered the ability to integrate those data with values determined by using ground measurements. The paper presents results of comparisons for the CWB index assessed using ground station data and those obtained from potential evapotranspiration as the product from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing instrument. The comparisons of results were performed for specific points (locations of ground stations) and were expressed by differences in means values. Analysis of Pearson’s correlation coefficient (r), Mann–Kendal trend test (Z-index), mean absolute error (MAE) and root mean square error (RMSE) for ten years’ series were evaluated and are presented. In addition, the basic spatial interpretation of results has been proposed. The correlation test revealed the r coefficient in the range from 0.06 to 0.68. The results show good trend agreement in time between two types of CWB with constantly higher values of this index, which is estimated using ground measurement data. In results for 34 (from 43 analyzed) stations the Mann–Kendal test provide the consistent trend, and only nine trends were inconsistent. Analyses revealed that the disagreement between the two considered indices (determined in different ways) increased significantly in the warmer period with a significant break point between R7 and R8 that falls at the end of May for each examined year. The value of MAE varied from 80 mm to 135 mm.

On using smoothing spline and residual correction to fuse rain gauge observations and remote sensing data

2014 ◽  
Vol 508 ◽  
pp. 410-417 ◽  
Author(s):  
Chengcheng Huang ◽  
Xiaogu Zheng ◽  
Andrew Tait ◽  
Yongjiu Dai ◽  
Chi Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Rain Gauge ◽  
Smoothing Spline ◽  
Sensing Data ◽  
Residual Correction

scholarly journals UAV STRATEGIES VALIDATION AND REMOTE SENSING DATA FOR DAMAGE ASSESSMENT IN POST-DISASTER SCENARIOS

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 121-128 ◽  
Author(s):  
A. Calantropio ◽  
F. Chiabrando ◽  
G. Sammartano ◽  
A. Spanò ◽  
L. Teppati Losè
Keyword(s):  
Remote Sensing ◽  
Emergency Response ◽  
Spatial Information ◽  
Central Italy ◽  
Remote Sensing Data ◽  
3D Models ◽  
Point Of View ◽  
Sensing Data ◽  
Rapid Mapping ◽  
Fundamental Information

<p><strong>Abstract.</strong> The recent seismic swarms, occurred in Italy since August 2016, outlined the importance of deepen Geomatics researches for the validation of new strategies aimed at rapid-mapping and documenting differently accessible and complex environments, as in urban contexts and damaged built heritage. In the emergency response, the crucial exploitation of technological advances should obtain and efficiently organize high-scale reliable geospatial data for the early warning, impact, and recovery phases. Fulfilling these issues, among others, the Copernicus EMS, has played by now an important role in immediate and extensive damage reconnaissance, as in the case of Centre Italy. Nevertheless, the use of remote sensing data is still affected by a problem of point-of-view, scale and detectable detail. Nadir images, airborne or satellite, in fact, strongly limited the confidence level of these products. The subjectivity of the operator involvement is still an open issue, both in the first fieldwork assessment, and in the following operational approach of interpretative damage detection and rapid mapping production. To overcome these limits, the introduction of UAV platforms for photogrammetric purposes, has proven to be a sustainable approach in terms of time savings, operators’ safety, reliability and accuracy of results: the nadir and oblique integration can provide large multiscale models, with the fundamental information related to the façades conditions. The presented research, conducted within the Central Italy earthquakes events, will focus on potentialities and limits of UAV photogrammetry in the two documented sites: Pescara del Tronto and Accumoli. Here, the aim is not limited to describe a series of strategies for georeferencing, blocks orientation and multitemporal co-registration solutions, but also to validate the implemented pipelines as a workflow that could be integrated in the operative intervention for emergency response in early impact activities. Thus, it would be possible to use this 3D metric products as a reference-data for significative improvements of reliability in typical visual inspection and mapping, flanking the traditional nadir airborne- or satellite-based products. The UAV acquisitions performed in two damaged villages are displayed, in order to underline the implication of the spatial information embedded in DSM reconstruction and 3D models, supporting more reliable damage assessments.</p>

An estimation of regional geologic structures from the geomorphology to characterize the Wadi systems, southeast Sinai, Egypt

2016 ◽  
Vol 4 (3) ◽  
pp. T323-T336
Author(s):  
Okechukwu Livinus Obiegbu ◽  
Andreas Laake ◽  
Peter Brabham
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
High Spectral Resolution ◽  
Fault System ◽  
Magnetic Data ◽  
Near Surface ◽  
Sensing Data ◽  
Rgb Images ◽  
Multispectral Remote Sensing

Complex regional geologic structural controls have generated a lot of interest in the engineering, oil, and gas industries within the past few years. Digital elevation models (DEMs), multispectral remote sensing images using ArcGIS software, in combination with data cube and geomorphologic characterization, provide important markers that aid in spatial information analysis for the study area. We have validated the characterization and classification of DEMs using spatial statistics by mineral spectroscopy of multispectral remote sensing data. Our characterization was initiated by a joint interpretation of DEMs and multispectral remote sensing data in association with stratigraphic and geologic information. We have combined Landsat ETM+ images from visible (VIS), near-infrared (NIR), and mid-infrared (MID IR) to create red-green-blue (RGB) images, superimposed with high-spectral-resolution 15 m panchromatic band 8. Principal component analysis (PCA) further enhanced the image results. To characterize the geomorphology and near surface, specific bands used included RGB Landsat 742 and 321 data sets, whereas false-color Landsat RGB images (742 and 432) provided spatial data in delineating areas of lineations and fault systems. The tectonic lineaments extracted from the escarpments of the DEM and magnetic data provided structures related to tectonic forces to better understand the major faults, lineations, and geomorphology. Results of this study showed a strikingly reliable interpretative result of these faults that controlled the low-lying areas. These faults and lineations are high-permeability zones that can be saturated by water during active rainfall and flash-flood periods thereby disrupting the equilibrium of various fault zones in the area and raising tectonic activities within the active fault system. Such saturation presents a major environmental hazard for the study area. Generally, the use of Landsat data combined with PCA indicates promising evidence of possible plays within the huge sedimentary deposits and raised concerns about safety and hazard issues.

scholarly journals Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4716
Author(s):  
Wanxue Zhu ◽  
Ehsan Eyshi Rezaei ◽  
Hamideh Nouri ◽  
Ting Yang ◽  
Binbin Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Soil Salinity ◽  
Latent Variables ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Yellow River ◽  
Chemical Properties ◽  
Remote Sensing Data ◽  
Sensing Data

Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0–10 cm and 10–20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes.

scholarly journals DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1088
Author(s):  
Hanqing Bao ◽  
Dongping Ming ◽  
Ya Guo ◽  
Kui Zhang ◽  
Keqi Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Social Sensing ◽  
Sensing Data ◽  
Functional Zone ◽  
Functional Zones

The urban functional zone, as a special fundamental unit of the city, helps to understand the complex interaction between human space activities and environmental changes. Based on the recognition of physical and social semantics of buildings, combining remote sensing data and social sensing data is an effective way to quickly and accurately comprehend urban functional zone patterns. From the object level, this paper proposes a novel object-wise recognition strategy based on very high spatial resolution images (VHSRI) and social sensing data. First, buildings are extracted according to the physical semantics of objects; second, remote sensing and point of interest (POI) data are combined to comprehend the spatial distribution and functional semantics in the social function context; finally, urban functional zones are recognized and determined by building with physical and social functional semantics. When it comes to building geometrical information extraction, this paper, given the importance of building boundary information, introduces the deeper edge feature map (DEFM) into the segmentation and classification, and improves the result of building boundary recognition. Given the difficulty in understanding deeper semantics and spatial information and the limitation of traditional convolutional neural network (CNN) models in feature extraction, we propose the Deeper-Feature Convolutional Neural Network (DFCNN), which is able to extract more and deeper features for building semantic recognition. Experimental results conducted on a Google Earth image of Shenzhen City show that the proposed method and model are able to effectively, quickly, and accurately recognize urban functional zones by combining building physical semantics and social functional semantics, and are able to ensure the accuracy of urban functional zone recognition.

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