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Nachweis und Bestimmung organischer Verbindungen (2. Band)

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fullscreen: Nachweis und Bestimmung organischer Verbindungen (2. Band)

Multivolume work

Persistent identifier:: 1670259072

Author:: Meyer, Hans

Title:: Lehrbuch der organisch-chemischen Methodik

Type of content:: Lehrbuch

Year of publication:: 1922

Place of publication:: Wien

Publisher of the original:: Verlag von Julius Springer

Identifier (digital):: 1670259072

Language:: German

Additional Notes:: Bände 1-3,2 erschienen von 1922-1940

Document type:: Multivolume work

Volume

Persistent identifier:: 1670273296

Author:: Meyer, Hans

Title:: Nachweis und Bestimmung organischer Verbindungen

Sub title:: mit 11 Abbildungen

Scope:: XII, 426 Seiten

Type of content:: Lehrbuch

DOI:: 10.14463/KXP:1670273296

Year of publication:: 1933

Place of publication:: Berlin

Publisher of the original:: Verlag von Julius Springer

Identifier (digital):: 1670273296

Illustration:: Illustrationen

Signature of the source:: a 368(2)

Language:: German

Usage licence:: Public Domain Mark 1.0

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Chemistry

Chapter

Title:: 493. [...]-Naphthol-6-sulfosäure, SCHAEFFERS Säure.

Document type:: Multivolume work

Structure type:: Chapter

Contents

Table of contents

Full text

In: Wagner W„ Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B
*
554
4. RESULTS
4.1 Results from the RT training
Training set is critical to RT. MODIS 8-day composite surface
reflectance and Surface water percentage data derived from
derived from the 1km USGS land/water map, as shown in
Figure 1, are used as the training datasets.
Figure 2 shows an example of the output regression tree
structure with the M5P algorithm. The tree employs a case's
attribute values to map it to a leaf designating one of the
regression models (Figure 3). The first number in brackets
following each leaf is the number of training instances falling
into this leaf and the second number is the root mean squared
error of the linear model on these training examples divided by
the global absolute deviation.
Figure 1. Water percentage map derived from the 1km USGS
land/water map.
Regression models generated from the M5P regression tree
algorithm are shown in the following:
Figure 2. An example of regression tree structure derived from
the M5P algorithm.
LM1: WF = -162.6945*CH1 - 0.583*CH2 - 320.8135*(CH2-
CH1) + 0.3253*CH2/CH1 + 39.981*NDWI + 39.7147
LM2: WF = -0.0152*CH1 - 0.583*CH2 - 11.6055*(CH2-CH1)
+ 0.1568*CH2/CH1 + 0.9568*NDWI + 3.9752
LM3: WF = -0.0152*CH1 - 0.336*CH2 - 71.4079*(CH2-CH1)
- 0.7859*CH2/CH1 + 19.7358*NDWI + 8.5573
LM4: WF = 0.2117*CH1 - 9.0327*CH2 - 0.9369*(CH2-CH1) -
33.0853*CH2/CH1 - 7.0218*NDWI + 43.6346
LM5: WF = -2.223*CH1 + 0.5925*CH2 - 5.2925*(CH2-CHl)
+ 0.0591 *CH2/CH1 + 0.4396*NDWI + 2.2389
LM6: WF = -4.0638*CH1 + 0.5925*CH2 - 8.7526*(CH2-CHl)
+ 0.0591 *CH2/CH1 +20.3801 *DWI - 0.7155
LM7: WF = -1.7928*CH1 + 0.9452*CH2 - 4.5942*(CH2-CH1)
+ 0.0591 *CH2/CH1 + 0.2035*NDWI + 1.2859
LM8: WF = -0.0818*CH1 - 0.0378 *CH2 - 1.0327*(CH2-CH1)
+ 0.0524*CH2/CH1 - 1.7571 *NDWI + 1.0939
LM9: WF = -0.6528*CH1 + 0.0744*CH2 - 1.2964*(CH2-CH1)
- 0.0338*CH2/CH1 + 0.064*NDWI + 0.4347
LM10: WF = -0.2448*CH1 - 4.3881*CH2 - 0.9276*(CH2-
CH1) - 0.5367*CH2/CH1 + 0.009*NDWI + 2.8895
Each regression model consists of:
• A linear model (LM) number — this serves to identify
the regression model.
• Every enumerated model is composed of a regression
equation.
Figure 2 just shows an example of the output regression
tree structure. The actual tree structure is too complicated to be
shown in a figure.
4.2 Results from the tests with real applications
Since we wish to get the geolocation information, instead of
using surface reflectance data (MOD09), we chose to use the
MODIS LIB calibrated TOA reflectance data (MOD021KM) in
conjunction with the MODIS geolocation fields (MOD03). An
accurate cloud filter for the Imager data is critical for reliable
results. Since our method uses satellite visible and infrared
observations, the water detection will be limited to clear
conditions. MODIS cloud mask (MOD35) data is used to filter
the cloudy conditions. The rules and threshold values obtained
from the training with surface reflectance data are applied to
“re-predict” the New Orleans flooding at the end of August in
2005 due to the landfall of Hurricane Katrina, which caused
over 1500 deaths and total damage costs exceeding $50 billion.
Figure 3 shows the water fraction map on these three days,
calculated by using the CH2 reflectance and (CH2-CH1)
predictors. From these images, we can clearly detect flooded
areas by comparing water fraction maps after flooding with
those before flooding. Figure 4 presents the flood maps on
August 31 and 30 as the difference in water fraction values after
.flooding with those before flooding on August 27. The flooded
regions are identified in red, the original water bodies are
shown in blue, while clouds are marked in grey. We can see
clearly that New Orleans and its surrounded areas were
inundated on August 30 and 31, 2005 after Hurricane Katrina
made landfall on August 29, 2005.
4.3 Evaluations
Since there are no direct ground measurements of water fraction
as the truth data, quantitative evaluations of water fraction
derived from satellite observations are challenging. The use of
higher resolution satellite data is a feasible way to solve this
problem. In this study, TM data with 30-m spatial resolution are
used to evaluate water fraction estimates from MODIS
observations.
The Landsat TM pixels can be assumed to be a pure pixel
composed of land or water. Using a decision tree method to
perform classification to TM data, the fraction of water in a
MODIS grid (1 kmxl km) can be calculated. The water
fractions at the MODIS resolution aggregated from the TM

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