Agronomic suitability studies in the Russian Altai using remote sensing and GIS [Elektronische Ressource] / Kamilya Kelgenbaeva. Technische Universität Dresden, Institut für Kartographie

Institut für KartographieKamilya KelgenbaevaAgronomic Suitability Studiesin the Russian AltaiUsing Remote Sensing and GISKartographische BausteineBand 36KB 36 Dresden 2007 Kartographische Bausteine Band 36 Dresden 2007 Cover photograph: Oblique view of the western part of the Uimon Basin from SSE. In the foreground one sees the northern foothills of the Katun Range (culmination point Beloukha, 4506 m), behind the Uimon Basin lies the Terekhta Range, reaching up to 2000 m, i.e. some 1050 m above the average height of the Uimon Basin. Near the N-E trending Katun River, in the centre of the image, the village of Ust-Koksa can be seen. Clearly, the fluvial sediments of the Terekhta River which drains into the basin from North are tracing through the agricultural pattern. Image based on Landsat TM data from 2002 draped over the GTOPO30 DTM. Fakultät Forst-, Geo- und Hydrowissenschaften AGRONOMIC SUITABILITY STUDIES IN THE RUSSIAN ALTAI USING REMOTE SENSING AND GIS Dissertation zur Erlangung des akademischen Grades Doctor-Ingenieur (Dr.-Ing.) an der Fakultät Forst-, Geo- und Hydrowissenschaften der Technischen Universität Dresden Gutachter: Herr Prof. Dr. phil. habil. Manfred Buchroithner (Betreuender Hochschullehrer) Korreferent: Herr Prof. Dr. rer. silv. habil. Franz Makeschin Korreferent: Herr Prof. Dr.-Ing.
Publié le : lundi 1 janvier 2007
Lecture(s) : 47
Source : WWW.QUCOSA.DE/FILEADMIN/DATA/QUCOSA/DOCUMENTS/464/1212669959876-3232.PDF
Nombre de pages : 256
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Institut für Kartographie
Kamilya Kelgenbaeva
Agronomic Suitability Studies
in the Russian Altai
Using Remote Sensing and GIS
Kartographische Bausteine
Band 36KB 36 Dresden 2007
Kartographische Bausteine
Band 36
Dresden 2007

























Cover photograph:

Oblique view of the western part of the Uimon Basin from SSE. In the foreground one sees
the northern foothills of the Katun Range (culmination point Beloukha, 4506 m), behind the
Uimon Basin lies the Terekhta Range, reaching up to 2000 m, i.e. some 1050 m above the
average height of the Uimon Basin. Near the N-E trending Katun River, in the centre of the
image, the village of Ust-Koksa can be seen. Clearly, the fluvial sediments of the Terekhta
River which drains into the basin from North are tracing through the agricultural pattern.
Image based on Landsat TM data from 2002 draped over the GTOPO30 DTM.






Fakultät Forst-, Geo- und Hydrowissenschaften







AGRONOMIC SUITABILITY STUDIES
IN THE RUSSIAN ALTAI
USING REMOTE SENSING AND GIS









Dissertation zur Erlangung des akademischen Grades
Doctor-Ingenieur (Dr.-Ing.)



an der Fakultät Forst-, Geo- und Hydrowissenschaften
der Technischen Universität Dresden



Gutachter: Herr Prof. Dr. phil. habil. Manfred Buchroithner (Betreuender Hochschullehrer)
Korreferent: Herr Prof. Dr. rer. silv. habil. Franz Makeschin
Korreferent: Herr Prof. Dr.-Ing. Dieter Morgenstern



Dresden, Dezember 2007






ii











































Herausgeber: Technische Universität Dresden, Institut für Kartographie
M. F. Buchroithner

Verfasser: Kamilya Kelgenbaeva


Vervielfältigung: Institut für Kartographie, Kartographisch-Technische Einrichtung
© 2007-12-18 beim Autor

ISBN: 978-3-86780-067-9


Bezug über: Technische Universität Dresden, Institut für Kartographie,
Helmholtzstr. 10, 01062 Dresden

E-mail: steffi.sharma@tu-dresden.de
iii


Declaration



I hereby declare that this submission is my own work and that, to the best of my knowledge
and belief, it contains no material previously published or written by another person nor
material which to a substantial extent has been accepted for the award of any other degree
or diploma of the university or other institute of higher learning, except where due
acknowledgment has been made in the text.




Kamilya Kelgenbaeva
Dresden, December 2007

iv
Acknowledgments

First of all I would like to thank the Master of All Creatures, GOD, the Almighty for giving
me knowledge, power and all necessary conditions to obtain an education in scientifically
highly developed western european countries and to complete this interesting study.
I am deeply indebted to my supervisor Prof. Dr. Manfred Buchroithner, Director, Institute
for Cartography, Dresden University of Technology, for his excellent scientific supervision,
many scientific critical remarks, understanding and his confidence in me during my research.
Without his highly scientific and personal support, it would not have been possible to achieve
this work.
I am especially thankful to my second (non-official) guide, Dr. Nikolas Prechtel, manager of
the Altai Project for his fruitful and useful scientific discussions and remarks. The provision of
satellite- and other data from the Altai Project and other supports are very much appreciated.
Many thanks to Prof. Dr. Ingeborg Wilfert and Prof. Dr. Wolfgang Koch for their kind
assistance during my research and for the friendly atmosphere and reception at the Institute
for Cartography. Special thanks must go to Prof. Dr. Koch for his interesting and detailed
lectures on theoretical cartography and map production.
I would also like to thank Dr. Alexander Wolodtschenko for his useful discussions on
cartosemiotics related to my topic. I am very thankful to Dr. Olga Wälder for her helpful
discussions on fuzzy logic mathematics and statistics and their application to my thesis.
Additionally, I would like to express my sincere gratitude to Mrs. Steffi Sharma for her great
support and her creation of an open and friendly atmosphere at the Institute for Cartography.
I would like also express my gratitude to the rest of the staff of our Institute: Mrs. Christine
Rülke, André Kunert, Claudia Hänel, Sven Etzold and Klaus Habermann. Sincere many
thanks to Robert Hecht for his useful discussions about the GIS AML program.
My sincere thanks to Prof. Emilia Grigoryeva, Prof. Dr. Lidia Burlakova (Barnaul State
University, Russia) and Prof. Dr. Diego De la Rosa (Seville, Spain) for their kind discussions
and for providing useful information about their methods and models on soil evaluation.
Many thanks for sharing knowledge and fruitful discussions must go to Dr. Vladimir
Butvilovski (TU Dresden), Dr. Irina Rotanova, Prof. Dr. Viktor Rudsky, Dr. Zoya
Lysenkova, Dr. Ljudmila Kasantzeva and Lilia Buchtueva (Barnaul State University,
Russia).
I am grateful to Prof. Dr. Christian Opp, University of Marburg, for his kind consultation
regarding the soils of Altai, and to Dr. Ralf-Uwe Syrbe, Saxonian Academy of Science for
his kind consultations on fuzzy logic, useful discussions and support for my research.
I would also like to thank all my friends and colleagues from Germany, Australia, Russia,
Egypt, India, Sudan, Lebanon, Syria and Kyrgyzstan and elsewhere for their kind support.
v
I am grateful to the German Academic Exchange Service (DAAD) and the Friedrich Ebert
Foundation (FES) for granting me scholarships. My sincere thanks go to Mr. Alfred Post,
the late Chancellor of TU Dresden and Head of the Association of Friends and Sponsors of
TU Dresden e.V. (GFF), for the financial support. I am grateful to Dr. Kubanychbek Kulov,
Director of the Kyrgyz Irrigation Research Institute (Ministry of Water Resources and
Agriculture) for granting me leave for the study period.
Last but not least, I am deeply thankful to the members of my family for their warm
encouragement and support.




Kamilya Kelgenbaeva
vi
Satellitenfernerkundung, GIS, Bodenwissenschaft, Agronomie, Modellierung, Altai-Republik,
Russland
Kamilya Kelgenbaeva – Dissertation, Institut für Kartographie, Technische Universität Dresden

Untersuchungen der Landwirtschaftseignung im Russischen Altai unter Verwendung
von Fernerkundungsdaten und GIS

Kurzfassung. Diese Doktorarbeit beschreibt Methoden und geeignete Anpassungen bereits
existierender Lösungen, um auf zwei verschiedenen Wegen die Landeignung für die Tal- und
Beckenregionen der Südsibirischen Altaigebirges innerhalb eines Geoinformationssystems zu
modellieren (GIS). Die Ausgangsmethoden sind: 1) die Bodeneignungsmodelle „Almagra" and
„Cervatana“ (MicroLEIS System), entwickelt für die Mittelmeerregionen (De la Rosa et al. 1992 and
1998) und die „Gewichtsmethode“, welche Burlakova L. M. (1988) speziell für die Altairegion
entwickelte. Letztgenannte Methode basiert auf den gewichteten Mitteln für eine gegebene Anzahl von
Faktoren. 2) Zum Vergleich, die zweite, dritte und vierte Version des gleichen Modells mit drei
unterschiedlichen Typen wurden mit Fuzzy-Logik-Methoden entwickelt. Sie werden benutzt, um
darzustellen, wie unscharfe Mengen zum einen die Berechnung von Gauß-Mitgliedschaftsfunktionen
bestimmter Klassen veranschaulichen können, welche zu anderen Klassen gehören, und wie die
Variablen in einer mathematischen Handhabung angefasst werden können. Außerdem stellt diese
Arbeit Ideen vor, wie die Fernerkundung das Geoinformationssystem (GIS) eingesetzt werden kann,
wenn - wie im vorliegenden Fall - nur unzureichend Geodaten vorhanden sind, (i) um in die
Modellierung der Boden- und Klimabedingungen einzugehen und (ii) um die Charakteristik des
Landmanagements im Untersuchungsgebiet zu kennzeichnen. Drei landwirtschaftliche Agrarkulturen
(Sommerweizen, Sonnenblumen und Kartoffeln) sind für die Altairegion auf regionaler Ebene von
Bedeutung und wurden daher in die vorliegende Untersuchung einbezogen. Die Bewertung erfolgte
nach fünf Eignungskategorien, entsprechend der FAO Klassifikation (1976). Das Uimon-Becken wurde
als Untersuchungsgebiet ausgewählt. Soziale und ökonomische Faktoren wurden bisher
ausgeschlossen, können aber innerhalb einer weiteren Entwicklungsphase hinzugenommen werden.

Satellite Remote Sensing, GIS, Soil Science, Agronomy, Modelling, Altai Republic, Russia
Kamilya Kelgenbaeva – Ph.D. Thesis, Institute for Cartography, Dresden University of Technology

Agronomic Suitability Studies in the Russian Altai Using Remote Sensing and GIS

Abstract. The doctoral thesis describes methodologies and appropriate adaptations of existing
solutions to model land suitability in two ways for the valley and basin areas of the South-Siberian Altai
Mountains within a geo-information system (GIS) environment. Starting-point approaches are: 1) the
Agricultural Soil Suitability Model „Almagra” and Land Capability Model “Cervatana”/MicroLEIS System
(De la Rosa et. al 1992, 1998) developed for Mediterranean regions and a method specifically
compiled by Burlakova L. M. (1988) for the Altai based on the weighted means of a factor set. 2) For
comparison purposes, second, third and fourth versions of the same model are developed using three
different types of Fuzzy Logic approaches. They are used to present how Gauss membership
functions of particular classes can be computed as different classes and how variables taking values
in ranges can be handled in a mathematical way. Furthermore, the paper presents ideas on how
remote sensing might interact with the geo-information system (GIS) where - like in the present case –
the required input geo-data are not fully sufficient to (i) feed the models formalising soil and climatic
conditions, and (ii) to characterise the patterns of land management within the study area. Three
agricultural crops (summer wheat, sunflowers and potatoes) are relevant to the Altai Region at a
regional level and are, therefore considered. A rating is classified using five suitability classes
according to the FAO classification (1976). For the case study the Uimon Basin was chosen. Social
and economic factors are so far excluded but can be added within a further phase of development.
vii

CONTENTS

Acknowledgments IV
Abstract VI
Contents II
Tables IX
Figures XII
Abbreviations XV


1. INTRODUCTION
1.1 Introduction ………………………………………………………………………………………… 1
1.2 Motivation and Aim …………………………... 3
1.3 General Preconsiderations Regarding Land Suitability Modelling ...............………………... 4
2. GENERAL GEOGRAPHIC SETTING OF STUDY AREA
2.1 Location and Topography ..………………………………………………………………............ 10
2.2 Climate ………………………………………………………………………………….................. 10
2.3 Geology including Soils …………………………………………………………………………... 12
2.3.1 Rock as Source Material of Soil ……………………………………………………….. 12
2.3.2 Relief as an Indicator of Soil Properties …………………………………………........ 14
2.3.3 Geological Processes as the Background of Soil Formations ……………………… 16
2.4 Relief and Soil-Formations Processes ……………………………………………….. 23
2.5 Erodability …………………………………………………………………………………............. 25
2.6 Soil ………………………………………........................ 28
2.6.1 Soil Fertility ………………………………………………………………….................... 28
2.6.2 Soil Data ………………………………………………………………………………….. 29
2.7 Salinization ……………………………………. 33
2.8 Drainage ……………………………………………………………………………………………. 33
2.9 Vegetation …………………………………….. 34
2.10 Actula Landuse (2002) …….……………………………………………………………………. 34
2.10.1. Agriculture in General …………….…………………………………………………... 35
2.10.2. Crop Rotation ………………………………………………………………………….. 36
2.11 Population ……………………………………………… 36
2.12 Brief Historic Account of Altai Republic ……………………………………………………….. 37
3. PREPARATION OF GEO-DATA
3.1 Remote Sensing, GIS and Cartography ………………………………………………………... 38
3.1.1 Introduction ……………………………………………………………………………………... 38
3.1.2 Remote Sensing ……………………………………………………………………................ 38
3.1.3 Geographical Information Systems (GIS) ………………………………………………... 40
3.1.4 Cartography …………………………………………………………………………….... 41
3.2 Satellite Imagery and Topographic Data ………………………………………….................... 42
3.3 DTM Generation ……………………………………………………………… 43

4. CLIMATIC AND SOIL-FERTILITY INDICATORS
4.1 Agricultural Evaluation of Climate ……………………………………………………………….. 46
4.2 Evaluation Criteria ……………………………………… 46
4.2.1 Definition of Classes and Ranking Tables ……………………………………………. 47
4.2.2 Hydrothermal Coefficients ……………………………… 47
4.2.2.1 Statistical Analysis of Climate Data ………………………………………...... 48
viii
4.2.3 Soil Type ………………………………………………………………………………….. 50
4.2.4 Thickness of Humus Horizon …………………………... 51
4.2.5 Humus …………………………………………………………………………………….. 51
4.2.6 Nitrogen ……………………………… 52
4.2.7 Phosphorus ……………………………………………………………………………..... 53
4.2.8 Potassium ……………………………………………………………………………....... 54
4.2.9 Cation Exchange Capacity ……….………...... 55
4.2.10 Soil pH …………………………………………………………………………………… 56
4.2.11 Flood Frequency …………………………….. 57
4.3 Altai Crops and their Crop Requirements ………………………………………………………. 58
4.3.1 Summer Wheat ………………………………………………………………………...... 58
4.3.2 Sunflowers ………………………………………………………………………………... 60
4.3.3 Potatoes ……………………………... 61

5. DETERMINATION OF LAND SUITABITY
5.1 Discussion of Previous Work …………………………………………………………………….. 64
5.1.1 Soil Evaluation by Burlakova (1974) ………………………………………………….. 64
5.1.2 Yield Forecasting Model of Burlakova (1988) ………………….. 64
5.1.3 Soil Evaluation Model of Raichert (2004) …………………………………………….. 65
5.1.4 Mediterranean Models “MicroLEIS” (Spain) …........................................................ 67
5.1.4.1 Agricultural Soil Suitability Model “Almagra” (1998) .…................................ 68
5.1.4.2 Land Capability Model “Cervatana” (1998) .….............................................. 69
5.1.4.3 Crop Yield Prediction Model “Albero” (1998) .......……………………………. 70
5.1.4.4 Forest Land Suitability Model “Sierra” (1998) ……….…………….. 71
5.1.5 International Review of Previous Work ……………………………………………….. 72
5.1.6 Critical Discussions…………………………………..………………75

6. MODELLING
6.1 Modelling of ALSM Using the Weighted Means Approach ……………............................... 76
6.1.1 Development of ALSM …………………………………………………..……………. 76
6.1.2 Calculation of Altaian Summer Wheat Suitability …………………………………... 77
6.1.3 Calculation of Altaian Sunflowers Suitability ……………………………................. 80
6.1.4 Calculation of Altaian Potatoes Suitability …………………………………………... 81
6.1.5 Generation of Suitability Maps ……………………………………………………….. 82
6.1.5.1 Summer Wheat ………………………………… 82
6.1.5.2 Sunflowers …………………………………………………………………….... 86
6.1.5.3 Potatoes ……………………………………….... 89
6.1.5.4 Combination: One Map with Most Suitable Crops ………………………… 93
6.1.6 Conclusions ………………………………………………………………..… 96

6.2 Modelling of the ALSM Using the Fuzzy Logic - SOM Approach……………………………. 97
6.2.1 Theory Fuzzy Logic …………………………………………………………….....…… 97
6.2.1.2 Fuzzy Sets ………....…………………………………………………………..... 98
6.2.1.3 Membership Functions ………………………………………..………….......... 99
6.2.1.4 Logical Operations .…………………………………………………………...... 101
6.2.1.5 If – Then - Rules …………………………………………… 102
6.2.1.6 Fuzzy Inference Process ……………………………………………. 103
6.2.1.7 Fuzzy Approach to Ecological Modelling and Data Analysis ….…….…...... 107
6.2.2 Modelling of the ALSM Using the Fuzzy Logic SOM Approach .………………….. 108
6.2.2.1 Membership Functions ....….……………………………………………....... 109
6.2.2.2 Hydrothermal Cofficient 1 (HTC ) .………………………………………….. 1101
6.2.2.3 Hydrotient 2 (HTC ) .…………1112
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