Surviving the Heterogeneity Jungle with Composite Mapping Operators

2010 ◽  
pp. 260-275 ◽  
Author(s):  
Manuel Wimmer ◽  
Gerti Kappel ◽  
Angelika Kusel ◽  
Werner Retschitzegger ◽  
Johannes Schoenboeck ◽  
...  
Keyword(s):  
Composite Mapping

Unconventional Internal Cracks, Part 1: Symmetric Variations of a Straight Crack

1982 ◽  
Vol 49 (1) ◽  
pp. 62-68 ◽  
Author(s):  
C. H. Wu
Keyword(s):  
Mapping Function ◽  
Rational Functions ◽  
Straight Crack ◽  
Composite Mapping ◽  
Elasticity Problems

The composite mapping function of successive mappings by rational functions is rational but may have poles at more than one location. This method may be exploited to generate many, many new but unconventional cracks for which the associated elasticity problems may be solved exactly. Two families of cracks are examined in detail in this paper. A few more cases will be reported in a follow-up paper.

Testing different remote sensing compositing periods for SOC content extraction in areas across Germany

2021 ◽  
Author(s):  
Simone Zepp ◽  
Martin Bachmann ◽  
Markus Möller ◽  
Bas van Wesemael ◽  
Michael Steininger ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Health ◽  
Bare Soil ◽  
Soil Parameters ◽  
High Productivity ◽  
Composite Mapping ◽  
Soil Exposure ◽  
Agricultural Regions ◽  
Soil Reflectance

<p>High spatial and temporal soil information is crucial to analyze soil developments and for monitoring long term changes to avoid soil degradation. A sufficient soil organic carbon (SOC) content is one of the key soil properties to achieve sustainable high productivity of soils, soil health and increased agroecosystem resiliency. For the usage of remote sensing approaches, naturally exposed soils in Germany occur rarely. Mainly agricultural regions can provide areas of exposed soils for short periods of time during a year. The Soil Composite Mapping Processor (SCMaP) is a fully automated approach to make use of per-pixel based bare-soil compositing to overcome the issue of limited soil exposure based on multispectral Landsat (TM 4, ETM 5, ETM+ 7 and OLI 8) imagery for individually determined time periods between 1984 and 2019.</p><p>Due to the high spatial and temporal resolution the SCMaP soil reflectance composites contain a considerable potential to derive detailed soil parameters as the SOC contents of exposed soils to add information to existing soil maps on field scale for areawide applications. Besides the soil reflectance composites several field soil samples provided by different federal authorities build the data base for the SOC modeling. Machine learning (ML) algorithms incl. Partial Least Squares and Random Forest regression with various inputs and set-ups are used and applied for several test areas in Germany. Furthermore, the capabilities of different compositing lengths (5-, 10- and 30-years) to derive spatial SOC contents are tested. The results and the validation of the different ML approaches and compositing lengths will be shown, providing insight into the benefits of this approach.</p>

scholarly journals A spatial model of forest and land fire vulnerability level in the Dairi District, North Sumatra, Indonesia

2021 ◽  
Vol 22 (8) ◽  
Author(s):  
Achmad Siddik Thoha ◽  
Hesty Triani
Keyword(s):  
Land Cover ◽  
Spatial Model ◽  
Important Variable ◽  
Driving Factors ◽  
The Road ◽  
Composite Mapping ◽  
Open Land ◽  
Level Model ◽  
North Sumatra ◽  
Very High

Abstract. Thoha AS, Triani H. 2021. A spatial model of forest and land fire vulnerability level in the Dairi District, North Sumatra, Indonesia. Biodiversitas 22: 3319-3326. Fires often occur every dry season and have a significant impact on ecosystems and human activities. One of the important roles in reducing the risk of forest and land fires is the availability of updated vulnerability level maps in all vulnerable areas. The objective of this study was to determine the relationship between the driving factors for forest and land fires and hotspots and to obtain a spatial model of the distribution of vulnerability to forest and land fires in the Dairi District of North Sumatra Province. This study uses a composite mapping analysis method to obtain a spatial model and the distribution of vulnerable areas to forest and land fires. Six variables in the form of maps were used in building the model, including land cover, population density, distance from the road, distance from the river, and distance from the settlement. This study showed that the most important variable for vulnerability level model of forest and land fires was the distance from the settlement. This study also found that open land, the farthest distance to the road, the farthest distance to the river, the farthest distance to the settlement, and the densest population were the driving factors for increased fire activity. The spatial model of the vulnerability level to forest and land fires in Dairi District was Y = 0.022X1 + 0.214X2 + 0.113X3 + 0.482X4 + 0.169X5. Land cover having high-very high vulnerability level belonged to open land dominated by grass. The largest areas with a high-very high forest fire vulnerability level in Dairi District were spread over Tanah Pinem Sub-district.

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