JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Senthil Selvaradjou European Commission Updating traditional soil maps with DSM techniques DG JRC F. Carré,

Slides:



Advertisements
Similar presentations
Digital Soil Mapping Future Directions in Europe
Advertisements

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Senthil Selvaradjou European Commission Updating traditional soil maps with DSM techniques DG JRC.
Tests of Hypotheses Based on a Single Sample
Modeling Impacts of Water Management on Crop Yield, Water Use Efficiency and GHG Emissions for Rice Production in Asia Changsheng Li 1, Xiangming Xiao.
Assessment. Schedule graph may be of help for selecting the best solution Best solution corresponds to a plateau before a high jump Solutions with very.
EVALUATING BIOASSAY UNCERTANITY USING GUM WORKBENCH ® HPS TECHNICAL SEMINAR April 15, 2011 Brian K. Culligan Fellow Scientist Bioassay Laboratory Savannah.
A Review of “Adaptive fingerprint image enhancement with fingerprint image quality analysis”, by Yun & Cho Malcolm McMillan.
INTEGRATED DESIGN OF WASTEWATER TREATMENT PROCESSES USING MODEL PREDICTIVE CONTROL Mario Francisco, Pastora Vega University of Salamanca – Spain European.
A COMPUTER BASED TOOL FOR THE SIMULATION, INTEGRATED DESIGN, AND CONTROL OF WASTEWATER TREAMENT PROCESSES By P. Vega, F. Alawneh, L. González, M. Francisco,
NORM BASED APPROACHES FOR AUTOMATIC TUNING OF MODEL BASED PREDICTIVE CONTROL Pastora Vega, Mario Francisco, Eladio Sanz University of Salamanca – Spain.
Chapter 9 Audit Sampling: An Application to Substantive Tests of Account Balances McGraw-Hill/Irwin ©2008 The McGraw-Hill Companies, All Rights Reserved.
An Overview of Today’s Class
Quantitative Genetics
Chapter 9: Introduction to the t statistic
Plant tissue analysis for testing nutrients deficiency in Banana
Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 8 Tests of Hypotheses Based on a Single Sample.
Behzod Abdullobekov Tajik Agrarian University August 16, 2012, Hungary.
Data Mining Techniques
FEATURE EXTRACTION FOR JAVA CHARACTER RECOGNITION Rudy Adipranata, Liliana, Meiliana Indrawijaya, Gregorius Satia Budhi Informatics Department, Petra Christian.
Chapter 8 Introduction to Hypothesis Testing
Cyber-Infrastructure for Agro-Threats Steve Goddard Computer Science & Engineering University of Nebraska-Lincoln.
Introduction to variable selection I Qi Yu. 2 Problems due to poor variable selection: Input dimension is too large; the curse of dimensionality problem.
Statistical Techniques I EXST7005 Distribution of Sample Means.
Estimation of Statistical Parameters
Random Sampling, Point Estimation and Maximum Likelihood.
Introduction to Fundamental Physics Laboratory Lecture I Dr. Yongkang Le March 5 th, 2010
Chapter 15 Data Analysis: Testing for Significant Differences.
PARAMETRIC STATISTICAL INFERENCE
BACKGROUND LEARNING AND LETTER DETECTION USING TEXTURE WITH PRINCIPAL COMPONENT ANALYSIS (PCA) CIS 601 PROJECT SUMIT BASU FALL 2004.
Hierarchical Distributed Genetic Algorithm for Image Segmentation Hanchuan Peng, Fuhui Long*, Zheru Chi, and Wanshi Siu {fhlong, phc,
Economics 173 Business Statistics Lecture 6 Fall, 2001 Professor J. Petry
Biostatistics Class 6 Hypothesis Testing: One-Sample Inference 2/29/2000.
ECE 8443 – Pattern Recognition LECTURE 07: MAXIMUM LIKELIHOOD AND BAYESIAN ESTIMATION Objectives: Class-Conditional Density The Multivariate Case General.
Virtual Academy for the Semi Arid Tropics Course on Insect Pests of Groundnut Module 5: Sorghum Plant Nutrition After completing this Lesson, you would.
Digital Media Lab 1 Data Mining Applied To Fault Detection Shinho Jeong Jaewon Shim Hyunsoo Lee {cinooco, poohut,
Controlling for Common Method Variance in PLS Analysis: The Measured Latent Marker Variable Approach Wynne W. Chin Jason Bennett Thatcher Ryan T. Wright.
Dutch plan for finalising Hair software package Alterra – Wageningen University and Research Centre Roel Kruijne Working Group Meeting on Pesticide Statistics,
1 Chapter 7 Sampling Distributions. 2 Chapter Outline  Selecting A Sample  Point Estimation  Introduction to Sampling Distributions  Sampling Distribution.
BPS - 3rd Ed. Chapter 131 Confidence Intervals: The Basics.
Chapter 9 Introduction to the t Statistic. 9.1 Review Hypothesis Testing with z-Scores Sample mean (M) estimates (& approximates) population mean (μ)
Regional Technical Forum Automated Conservation Voltage Reduction Protocol.
Three Frameworks for Statistical Analysis. Sample Design Forest, N=6 Field, N=4 Count ant nests per quadrat.
Copyright © Cengage Learning. All rights reserved. 12 Analysis of Variance.
Inferences Concerning Variances
Statistical Inference Statistical inference is concerned with the use of sample data to make inferences about unknown population parameters. For example,
9-1 Copyright © 2016 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.
MATB344 Applied Statistics I. Experimental Designs for Small Samples II. Statistical Tests of Significance III. Small Sample Test Statistics Chapter 10.
1 of 31 The EPA 7-Step DQO Process Step 6 - Specify Error Tolerances 60 minutes (15 minute Morning Break) Presenter: Sebastian Tindall DQO Training Course.
T tests comparing two means t tests comparing two means.
Hypothesis Testing. Statistical Inference – dealing with parameter and model uncertainty  Confidence Intervals (credible intervals)  Hypothesis Tests.
REAL-TIME CALIBRATION OF ACTIVE CROP SENSOR SYSTEM FOR MAKING IN-SEASON N APPLICATIONS K.H. Holland and J.S. Schepers Holland Scientific and USDA-ARS,
1 of 48 The EPA 7-Step DQO Process Step 6 - Specify Error Tolerances 3:00 PM - 3:30 PM (30 minutes) Presenter: Sebastian Tindall Day 2 DQO Training Course.
Evaluation And Adjustment Of The 2008 Census Age & Sex Data.
The article written by Boyarshinova Vera Scientific adviser: Eltyshev Denis THE USE OF NEURO-FUZZY MODELS FOR INTEGRATED ASSESSMENT OF THE CONDITIONS OF.
1 A latent information function to extend domain attributes to improve the accuracy of small-data-set forecasting Reporter : Zhao-Wei Luo Che-Jung Chang,Der-Chiang.
Statistical Concepts Basic Principles An Overview of Today’s Class What: Inductive inference on characterizing a population Why : How will doing this allow.
Shadow Detection in Remotely Sensed Images Based on Self-Adaptive Feature Selection Jiahang Liu, Tao Fang, and Deren Li IEEE TRANSACTIONS ON GEOSCIENCE.
In Search of the Optimal Set of Indicators when Classifying Histopathological Images Catalin Stoean University of Craiova, Romania
INTRODUCTORY STATISTICS FOR CRIMINAL JUSTICE
Writing a Formal Lab Report
Quantifying Scale and Pattern Lecture 7 February 15, 2005
The break signal in climate records: Random walk or random deviations
Rutgers Intelligent Transportation Systems (RITS) Laboratory
Statistics in Applied Science and Technology
Yenn-Jiang Lin et al. JACEP 2016;2:
REMOTE SENSING Multispectral Image Classification
ECE539 final project Instructor: Yu Hen Hu Fall 2005
Where did we stop? The Bayes decision rule guarantees an optimal classification… … But it requires the knowledge of P(ci|x) (or p(x|ci) and P(ci)) We.
Hypothesis Testing: The Difference Between Two Population Means
1 Chapter 8: Introduction to Hypothesis Testing. 2 Hypothesis Testing The general goal of a hypothesis test is to rule out chance (sampling error) as.
Presentation transcript:

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Senthil Selvaradjou European Commission Updating traditional soil maps with DSM techniques DG JRC F. Carré, H. Reuter, A. Jones, L. Montanarella

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Why is it important to be able to update traditional soil maps?  Local knowledge on soils contained in traditional soil maps  Usually, no associated guidelines on soil distribution rules  Soil surveyors are now retiring and field expertise will be lost soon  Due to lack of formalism of soil distribution, soil maps contain uncertainties which need to be removed

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Objectives Soil type map  ‘Extract the soil distribution rules’ Soil covariates (RS images, DEM…)  Update the soil map Original soil type map New soil type map

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Two applications  Updating potential soil erosion assessment  Updating the Asian part of the FAO Soil Map (1988) Soil map Soil covariates New soil type map Soil erosion map (t o ) Soil covariates t o Soil covariates t 1 MODEL  No change in time New soil erosion map (t 1 ) MODEL  Change in time

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Methodology  Based on the DRIS (Diagnosis Recommendation Integrated System) Approach (Beaufils, 1973) Purpose: to evaluate through indices the effect of each nutrient on the nutritional balance of the plant (agronomic issue) { 0 = excess} Premices (a) Ratios among nutrients are usually better indicators of nutrient deficiencies than isolated concentrations values (b) Some nutrient ratios are more important or significant than others (c) Maximum yield are only reached when important nutrient ratios are near the ideal or optimum values (obtained from high yielding-selected populations) (d) As a consequence, the variance of an important nutrient ratio is smaller in a high yielding (reference population) than in a low yielding populations and the relations between variances of high and low yielding populations can be used in the selection of significant nutrient ratios (e) The DRIS indices can be calculated individually, for each nutrient, using the average ratio deviation obtained from the comparison with the optimum value of a given nutrient ratio

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Methodology  Main steps of DRIS Approach  Dividing the population into two groups: high yield (reference population) and low yield  Calculation of norms using the variance largest ratio among high and low yielding populations  Calculation of nutrient indices based on the comparison between actual nutrient ratio and optimal nutrient ratios Consider 3 nutrients (A), (B) and (N) where Z = 2 (n-1) Mean ratios of the reference population  Equilibrium Index of the System (EIS) EIS ~ 0 (optimum state of the system

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. DRIS for updating soil erosion map

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. The original soil erosion map Soil Map of erosion of Tamil Nadu region (NBSS & LUP, 1997)

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Legend transformation

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. The nutrients equivalent Soil erosion is a function of

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Division of the population High yield ~ none to slight erosion {class 1} Low yield ~ From slight to severe erosion {classes 2 to 4}

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Erosion factor n (EFn) Index (EFn) Calculation of the EIS Erosion factor 2 (EF2) Index (EF2) Erosion factor 1 (EF1) Index (EF1)EIS  EF i n

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Map of the EIS Reclassification of the EIS according to the original classes of soil erosion Introduction of classes for quantifying ‘continuously’ soil erosion

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. New quantitative map of soil erosion

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Detecting some changes in soil erosion  Introduction & replacement of ‘dynamic’ parameters like landcover and climate  Conservation of the ‘optimal’ ratios  New indices calculation  New EIS map and derivation of a new soil erosion  Comparison of the two different maps (original and new maps) and detection of the changes

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. DRIS for updating soil map

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. The FAO soil map of South Asia

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Where are the differences in approaches between erosion and soil classes map? For soil classes map, there is no semi-quantitative values as for soil erosion  The variable is categorical  For each soil type, we use a DRIS model {presence of the soil type is the population of reference}  For each soil type, we calculate the EIS Soil Type n EIS (T3) Soil Type 2 EIS (T2) Soil Type 1 EIS (T1) Min(EIS)Corresponding soil type

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. The updated soil map

JRC Ispra - IES NCSS, 07/06/06Selvaradjou et al. Conclusions  The DRIS approach allows for updating soil maps based on expert knowledge contained in the original soil map  Since it is updating and not drastically changing the soil map, there is no criticism of the expert knowledge. DRIS consists in ‘harmoinizing’ the expert knowledge over the map  The information of the expert knowledge and the rules of the soil distribution is not directly accessible  This approach is computer demanding but it has been automated (ArcInfo algorithms)  We are now comparing this approach to classic DSM soil inference systems