scholarly journals Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1279
Author(s):  
Dominik Scholand ◽  
Britta Schmalz
Keyword(s):  
Remote Sensing ◽  
Success Rate ◽  
Soil Conservation ◽  
Soil Loss ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Mountain Range ◽  
Sensing Data ◽  
P Factor

The P-factor for support practice of the Universal Soil Loss Equation (USLE) accounts for soil conservation measures and leads to a significant reduction in the modelled soil loss. However, in the practical application, the P-factor is the most neglected factor overall due to high effort for determining or lack of input data. This study provides a new method for automatic derivation of the main cultivation direction from seed rows and tramlines on agricultural land parcels using the Fast Line Detector (FLD) of the Open Computer Vision (OpenCV) package and open remote sensing data from Google Earth™. Comparison of the cultivation direction with the mean aspect for each land parcel allows the determination of a site-specific P-factor for the soil conservation measure contouring. After calibration of the FLD parameters, the success rate in a first application in the low mountain range Fischbach catchment, Germany, was 77.7% for 278 agricultural land parcels. The main reasons for unsuccessful detection were problems with headland detection, existing soil erosion, and widely varying albedo within the plots as well as individual outliers. The use of a corrected mask and enhanced parameterization offers promising improvements for a higher success rate of the FLD.

Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding

2017 ◽  
Vol 98 (11) ◽  
pp. 2397-2410 ◽  
Author(s):  
Justin L. Huntington ◽  
Katherine C. Hegewisch ◽  
Britta Daudert ◽  
Charles G. Morton ◽  
John T. Abatzoglou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Natural Resource ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Resource Monitoring ◽  
Sensing Data ◽  
Process Understanding ◽  
Natural Resource Monitoring

Abstract The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

scholarly journals Study of energy-efficient architecture address utilizing topography and geomorphology based on Google Earth and its remote sensing data

2020 ◽  
Vol 206 ◽  
pp. 01020
Author(s):  
Yijin Wang
Keyword(s):  
Remote Sensing ◽  
Energy Saving ◽  
City Planning ◽  
Smart Cities ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Green Energy ◽  
Rational Analysis ◽  
Sensing Data ◽  
Distribution Features

In this paper, we describe the formatting guidelines for ACM SIG Proceedings. With the development of social economy, smart cities, especially green energy-saving buildings, are foremost trend in the future. The location of green buildings has a very important impact on the design and plan of future smart cities. The influence of the natural environment, especially that of the topography and landform on the location of architectural design is very significant. Google Earth (GE) platform can provide sufficient remote sensing data, which greatly interpret and promote surface information. However, just few people have done related research. This article takes Beijing as an example and uses Google Earth platform and the remote sensing data to obtain the 3D digital elevation model (DEM) data; and then Google earth’s geomorphology data are used to analyze the landform features. Finally, by analyzing their characteristics and distribution features, five energy-saving building locations were selected in Beijing. It can be concluded that GE, is an effective and potential platform for providing remote sensing data, and analyzing the DEM and landform. The rational analysis of the building addresses in this paper could help the buildings to avoid potential geological disasters and make full use of natural resources. Moreover, this research on energyefficient building addresses make a suggestion for future smart city planning.

The use of remote sensing data to extract information from agricultural land with emphasis on soil salinity

Soil Research ◽  
2003 ◽  
Vol 41 (7) ◽  
pp. 1243 ◽  
Author(s):  
F. M. Howari
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Large Scale ◽  
Agricultural Land ◽  
Rio Grande Valley ◽  
Remote Sensing Data ◽  
Rio Grande ◽  
The United States ◽  
Sensing Data ◽  
Matching Techniques

The rapid growth of information technologies has provided exciting new sources of data, interpretation tools, and modelling techniques to soil research and education communities at all levels. This paper presents some examples of the capability of remote sensing data such as Landsat ETM+, airborne visible/infrared imaging spectrometer (AVIRIS), colour infrared aerial photos (CIR), and high-resolution field spectroradiometer (GER 3700) to extract surface information about soil salinity. The study used image processing techniques such as supervised classification, spectral extraction, and matching techniques to investigate types and occurrences of salts in the Rio Grande Valley on the United States–Mexico border. Soil salinity groups were established using soil physico-chemical properties and image elements (absorption-reflectivity profiles, band combinations, grey tones of the investigated images, and textures of soil and vegetation covers as they appear in images). The lack of vegetation or scattered vegetation on salt-affected soil (SAS) surfaces makes it possible to detect salt in several locations of the investigated area. The presented remote sensing datasets reveal the presence of gypsum and halite as the dominant salt crusts in the Rio Grande Valley. This information can help agricultural scientists and engineers to produce large-scale maps of salt-affected lands, which will help improve salinity management in watersheds and ecosystems.

scholarly journals Managing Agricultural Water Considering Water Allocation Priority Based on Remote Sensing Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1536
Author(s):  
Biao Luo ◽  
Fan Zhang ◽  
Xiao Liu ◽  
Qi Pan ◽  
Ping Guo
Keyword(s):  
Remote Sensing ◽  
Water Resources ◽  
Grain Yield ◽  
Water Allocation ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Proportional Fairness ◽  
Agricultural Output ◽  
Sensing Data ◽  
Spatial Method

To fairly distribute limited irrigation water resources in arid regions, a water allocation priority evaluation method based on remote sensing data was proposed and integrated with an optimization model. First, the water supply response unit was divided according to canal system conditions. Then, a spatialization method was used for generating spatial agricultural output value (income from planting industry) and grain yield (yield of food crops) with the help of NDVI and the potential yield of farmland. Third, the AHP-TOPSIS method was employed to calculate the water allocation priority based on the above information. Finally, the evaluation results were integrated with a nonlinear multiobjective model to optimally allocate agricultural land and water resources, considering the combined objective of minimum envy and proportional fairness. The method was applied to Hetao irrigation area, an arid agriculture-dominant region in Northwest China. After solving the model, optimization alternatives were obtained, which indicate that: (1) the spatial method of agricultural output value can improve the accuracy by around 16% compared with the traditional method, and the spatial method of grain yield also have good accuracy (MAPE = 14.66%); (2) the rank of water allocation priority can reflect more spatial information, and provide practical decision support for the distribution of water resources; (3) the envy index can better improve the efficiency of an allocation system compared to the Gini coefficient method.

scholarly journals Monitoring Urban Expansion Using Remote-Sensing Data Aided by Google Earth Engine

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Majid Aghlmand ◽  
Gordana Kaplan
Keyword(s):  
Remote Sensing ◽  
Accuracy Assessment ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Support Vector ◽  
Sensing Data ◽  
Google Earth Engine ◽  
Landsat Satellite ◽  
The City

Urbanizationis accompanied by rapid social and economic development, while the process of urbanization causes the degradation of the natural ecology. Direct loss in vegetation biomass from areas with a high probability of urban expansion can contribute to the total emissions from tropical deforestation and land-use change. Monitoring of urban expansion is essential for more efficient urban planning, protecting the ecosystem and the environment. In this paper, we use remote sensing data aided by Google Earth Engine (GEE) to evaluate the urban expansion of the city of Isfahan in the last thirty years. Thus, in this paper we use Landsat satellite images from 1986 and 2019, integrated into GEE, implementing Support vector machine (SVM) classification method. The accuracy assessment for the classified images showed high accuracy (95-96%), while the results showed a significant increase in the urban area of the city of Isfahan, occupying more than 70% of the study area. For future studies, we recommend a more detailed investigation about the city expansion and the negative impacts that may occur due to urban expansion.

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