scholarly journals Watershed zonation approach for tractably quantifying above-and-belowground watershed heterogeneity and functions

2021 ◽  
Author(s):  
Haruko M. Wainwright ◽  
Sebastian Uhlemann ◽  
Maya Franklin ◽  
Nicola Falco ◽  
Nicholas J. Bouskill ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Remote Sensing Data ◽  
Gaussian Mixture ◽  
Clustering Methods ◽  
Near Surface ◽  
Sensing Data ◽  
East River ◽  
Early Snowmelt ◽  
Data Layers

Abstract. In this study, we develop a watershed zonation approach for characterizing watershed organization and function in a tractable manner by integrating multiple spatial data layers. Recognizing the coupled ecohydrogeological-biogeochemical interactions that occur across bedrock through canopy compartments of a watershed, we hypothesize that (1) suites of above/belowground properties co-varying with each other, (2) hillslopes are representative units for capturing watershed-scale heterogeneity, (3) remote sensing data layers and clustering methods can be used to identify watershed hillslope zones having unique distributions of bedrock-through-canopy properties relative to neighboring parcels, and (4) property suites associated with the identified zones can be used to understand zone-based functions, such as response to early snowmelt or drought, and associated solute exports to the river. We demonstrate this concept using unsupervised clustering methods that synthesizes airborne remote sensing data (LiDAR, hyperspectral, and electromagnetic surveys) along with satellite and streamflow data collected in the East River Watershed, Crested Butte, Colorado, USA. Results show that, (1) hillslope-average elevation and slope are significantly correlated with near-surface bedrock electrical resistivity (top 20 m), (2) elevation and aspect are independent controls on plant and snow signatures, (3) the correlation between hillslope-averaged above- and below- ground properties are significantly higher than pixel-by-pixel correlation and (4) K-means, hierarchical clustering, and Gaussian mixture clustering methods generate similar zonation patterns across the watershed. Using independently collected data, it is shown that the identified zones provide information about zone-based watershed functions, including foresummer drought sensitivity and river nitrogen exports. The approach is expected to be extensible to other sites and generally useful for guiding the selection of hillslope experiment locations and informing model parameterization.

scholarly journals USING SYSTEMS OF PARALLEL AND DISTRIBUTED DATA PROCESSING TO BUILD HYDROLOGICAL MODELS BASED ON REMOTE SENSING DATA

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 111-116
Author(s):  
A. A. Kolesnikov ◽  
P. M. Kikin ◽  
E. A. Panidi ◽  
A. G. Rusina
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Spatial Data ◽  
Distributed Processing ◽  
Remote Sensing Data ◽  
Hydroelectric Power Plant ◽  
Point Clouds ◽  
Hydroelectric Power ◽  
Raster Data ◽  
Sensing Data

Abstract. The article describes the possibilities and advantages of using distributed systems in the processing and analysis of remote sensing data. The preparation and processing of various types of remote sensing data (multispectral satellite images, values of climatic indicators, elevation data), which will then be used to build a simulation model of a hydroelectric power plant, was chosen as the basic task for testing the chosen approach. The existing approaches with distributed processing of spatial data of various types (vector cartographic objects, raster data, point clouds, graphs) are analyzed. The description of the developed approach is given and the rationale for the choice of its components is made. The preprocessing operations that were performed on the used raster data are described. An approach to the problems of raster data segmentation based on libraries for distributed machine learning is considered. Comparison of the speed of working with data for various algorithms of machine learning and processing is given.

scholarly journals A Precision Evaluation Method for Remote Sensing Data Sampling Based on Hexagon Discrete Grid

2020 ◽  
Author(s):  
Yue Ma ◽  
Guoqing Li ◽  
Xiaochuang Yao ◽  
Jin Ben ◽  
Qianqian Cao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Evaluation System ◽  
Evaluation Method ◽  
Remote Sensing Data ◽  
Accuracy Evaluation ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Hexagonal Grids ◽  
Discrete Grid

With the rapid development of earth observation, satellite navigation, mobile communication and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. Under this circumstance, a new form of spatial data organization emerged-the global discrete grid. This form of data management can be used for the efficient storage and application of large-scale global spatial data, which is a digital multi-resolution the geo-reference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing and application of current spatial data. There are different types of grid system according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, and establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves that the availability hexagonal grid-based remote sensing data of remote sensing images. And among the three sampling methods, the image obtained by the nearest interpolation sampling method has the highest correlation with the original image.

scholarly journals Mapping Flood-Based Farming Systems with Bayesian Networks

Land ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 369 ◽  
Author(s):  
Issoufou Liman Harou ◽  
Cory Whitney ◽  
James Kung’u ◽  
Eike Luedeling
Keyword(s):  
Remote Sensing ◽  
Bayesian Network ◽  
Spatial Data ◽  
Probabilistic Reasoning ◽  
Expert Knowledge ◽  
Agricultural Research ◽  
Remote Sensing Data ◽  
Farming Systems ◽  
Spatially Explicit ◽  
Sensing Data

Many actors in agricultural research, development, and policy arenas require accurate information on the spatial extents of cropping and farming practices. While remote sensing provides ways for obtaining such information, it is often difficult to distinguish between different types of agricultural practices or identify particular farming systems. Stochastic system behavior or similarity in the spectral signatures of different system components can lead to misclassification. We addressed this challenge by using a probabilistic reasoning engine informed by expert knowledge and remote sensing data to map flood-based farming systems (FBFS) across Kisumu County in Kenya and the Tigray region in Ethiopia. Flood-based farming is an important form of agricultural production employed in regions with seasonal water surplus, which can be harvested and used to irrigate crops. Geographic settings for FBFS vary widely in terms of hydrology, vegetation, and local practices of agronomic flooding. Agronomic success is often difficult to anticipate, because the timing and amount of flooding usually cannot be precisely predicted. We generated a Bayesian network model to describe the FBFS settings of the study regions. We acquired three years (2014–2016) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra spectral data as eight-day composite time series and elevation data from the Shuttle Radar Topography Mission (SRTM) to compute 10 spatial data metrics corresponding to 10 of the 17 Bayesian network nodes. We used the spatial data metrics in a fully probabilistic framework to generate the 10 spatial data nodes. We then used these as inputs for the probabilistic model to generate prior and posterior spatial estimates for specific metrics along with their spatially explicit uncertainties. We show how such an approach can be used to predict plausible areas for FBFS based on several scenarios. We demonstrate how spatially explicit information can be derived from remote sensing data as fuzzy quantifiers for incorporating uncertainties when mapping complex systems. The approach achieved a remarkably accurate result in both study areas, where 84–90% of various FBFS fields sampled were correctly mapped as having a high chance of being suitable for the practice.

scholarly journals Using remote sensing data for environmental monitoring of water objects using GIS and machine learning

2021 ◽  
Vol 937 (2) ◽  
pp. 022051
Author(s):  
D Krivoguz ◽  
A Semenova ◽  
S Mal’ko
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Spatial Data ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Environmental Variable ◽  
Machine Learning Algorithms ◽  
Sensing Data ◽  
Water Ecosystems

Abstract The main way to understand variability of any spatial data using remote sensing is calculating spectral indices. For now, some difficulties have receiving water surface temperature due to specific properties for satellite sensors and low spatial resolution. The main sources of receiving salinity data are remote sensing data from ESA SMOS, NASA Aquarius and SMAP satellites. Using different machine learning algorithms, we can get models or equations, representing dependency between studied environmental variable and different spectral channels of remote monitoring data. After receiving and collecting remote sensing data in database this system uses machine learning algorithms to find dependency between collected field data and different spectral bands of the remote sensing data. Our goal was to form an analytical system based on remote sensors and machine learning algorithm to analyse, predict and evaluate water ecosystems for fisheries and environmental protection.

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