Presentation is loading. Please wait.

Presentation is loading. Please wait.

Manifestation of Land Use/Land Cover Change Analysis and Its Impacts on Soil Properties in Gadarif Region, Sudan Faculty of Forest, Geo and Hydro Sciences,

Published byBruce Mathews Modified over 9 years ago

Similar presentations

Presentation on theme: "Manifestation of Land Use/Land Cover Change Analysis and Its Impacts on Soil Properties in Gadarif Region, Sudan Faculty of Forest, Geo and Hydro Sciences,"— Presentation transcript:

1 Manifestation of Land Use/Land Cover Change Analysis and Its Impacts on Soil Properties in Gadarif Region, Sudan Faculty of Forest, Geo and Hydro Sciences, Department of Geosciences, Institute for Cartography Khalid Guma Biro, PhD student Supervisor: Prof. Manfred Buchroithner Fortaleza, Ceará, Brazil, 01 – 12 November 2010

2 2 Main topics Use of multi-temporal satellite data for land use/land cover change analysis Impacts of land use/land cover changes on soil properties Soil compaction and infiltration rate characteristics under three different land use types in dryland vertisols Land use analyses in drylands: an object-oriented classification approach using TerraSAR-X data Synthesis: Discussion of interconnections and implications for soil conservation, soil management and sustainable land use Fortaleza, 21.12.2015

3 3 Study area African Sahel (Source: NASA Earth Observatory, 2007) Gadarif Region East Sudan Study site: Area: 40 x 30 km² Average altitude: 600 m

4 4 Analysis of LULC change using optical data Fortaleza, 21.12.2015 Unclassified (raw) images from Landsat and ASTER data Acquisition date SensorGeometric resolution Spectral resolution 05.11.1979MSS57 m4 bands 12.12.1989TM30 m6 bands 22.11.1999ETM+30 m7 bands 17.01.2009ASTER15 m3 bands

5 Fortaleza, 21.12.2015 5 Maximum likelihood classification showing LULC through the period 1979 – 2009

6 Fortaleza, 21.12.2015 6 Areas of LULC classes in the study area

7 Fortaleza, 21.12.2015 7 Change detection (%) for the period 1979-2009 (Diagonal represents unchanged fraction of each class) Change from Cultivated land WoodlandFallow landBarelandSettlementWater Cultivated land76310560 Woodland8146720 Fallow land682104160 Bareland57363130 Settlement11342800

8 Fortaleza, 21.12.2015 8 LULC changes in the period 1979 – 2009

9 Fortaleza, 21.12.2015 9 LULC changes in the period 1979 – 2009

10 10 Land use analysis using TerraSAR-X data Study area Descending Ascending Fortaleza, 21.12.2015 Acquisition date Sensor mode Geometric resolution Incidence Angle 07.11.2009Strip map4 m33.32º 12.11.2009Strip map4 m32.25º Data acquisition

11 Image filtering 11 Fortaleza, 21.12.2015 σ ºdB = βº dB + 10 * Log10 (sin (θloc) (1) (Infoterra, 2008) βºdB = 10 * Log10 (Ks * DN²) (2) Where: σ ºdB = backscattering coefficient in dB θloc = local incidence angle from GIM βºdB = radar brightness in dB Ks = TerraSAR-X calibration factor DN = digital number of TerraSAR-X EEC input image pixel. Normalize image backscattering coefficient

12 12 Gamma speckle filtering (7x7) HVHHHH/HV Raw Filtered Fortaleza, 21.12.2015

13 13 Image segmentation Fortaleza, 21.12.2015 Segmentation levelScale parameterColour/Shape ٭ Image weight Level 12500.9/0.11 : 1 Level 22500.8/0.21 : 1 Level 31500.9/0.11 : 1 Level 41500.8/0.21 : 1 Segmentation parameters (using multiresolution algorithm of eCognition) ٭ Smoothness/compactness were constant at 0.5/0.5 HV HH HH/HV

14 14 Feature space optimization Classification was based on nearest neighbour classifier using feature space optimisation GLCM = Gray level co-occurrence matrix StdDev = Standard deviation Invert expression also used for select class feature Fortaleza, 21.12.2015

15 15 TerraSAR-X land use/cover map

16 16 Fortaleza, 21.12.2015 TerraSAR-X land use/cover map

17 17 Fortaleza, 21.12.2015 TerraSAR-X land use/cover map

18 18 Summary of classification accuracies Fortaleza, 21.12.2015 Class Accuracy (%) Conditional kappaArea (%) Producer’sUser’s Cultivated land95970.9654.75 Harvested land97 0.9635.65 Fallow land95970.964.37 Woodland91860.850.58 Bareland97880.870.74 Rock91870.860.11 Settlement 190860.841.46 Settlement 287940.932.19 Water100 10.15 Overall accuracy (%)94 Kappa coefficient0.93

19 19 Impacts of land use change on soil properties Fortaleza, 21.12.2015 Soil physical properties

20 20 Fortaleza, 21.12.2015 Soil chemical properties

21 21 Fortaleza, 21.12.2015 Soil penetration resistance

22 22 Fortaleza, 21.12.2015 Soil infiltration (Kostiakov model)

23 23 Fortaleza, 21.12.2015 Maps of soil penetration resistance

24 24 Fortaleza, 21.12.2015 Maps of soil organic carbon

25 25 Fortaleza, 21.12.2015 Maps of soil moisture content

26 26 Use of multi-temporal satellite data for land use/land cover change analyses and its impacts on soil properties in the northern part of Gadarif region, Sudan; 30th EARSeL Symposium 31.05 - 01.06.2010 - Paris, France. (submitted to Land Degradation and Development, Revised). Land use analyses in the African Sahel: an object-oriented classification approach using TerraSAR-X data; 38th COSPAR Scientific Assembly, 18 - 25 July 2010,Bremen, Germany. (Submitted to Advances in Space Research, Revised). The effects of different land use types on soil compaction and infiltration rate in the drylands vertisol of Gadarif region, Sudan; Tropentag 2010, ETH Zurich September 14 - 16, 2010. (Under work). Fortaleza, 21.12.2015

27 26 Thank You! Khalidturk76@yahoo.co.uk Fortaleza, 21.12.2015

Download ppt "Manifestation of Land Use/Land Cover Change Analysis and Its Impacts on Soil Properties in Gadarif Region, Sudan Faculty of Forest, Geo and Hydro Sciences,"

Similar presentations

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Satellite Remote Sensing for Land-Use/Land-Cover.

Lab for Remote Sensing Hydrology and Spatial Modeling Dept. of Bioenvironmental Systems Engineering, NTU Satellite Remote Sensing for Land-Use/Land-Cover.
36th COSPAR Scientific Assembly 2006 Beijing July 20, 2006 Hypothesis-Test-Based Landcover Change Detection Using Multitemporal Satellite Images S. P.

36th COSPAR Scientific Assembly 2006 Beijing July 20, 2006 Hypothesis-Test-Based Landcover Change Detection Using Multitemporal Satellite Images S. P.
On Estimation of Soil Moisture & Snow Properties with SAR Jiancheng Shi Institute for Computational Earth System Science University of California, Santa.

On Estimation of Soil Moisture & Snow Properties with SAR Jiancheng Shi Institute for Computational Earth System Science University of California, Santa.
Jet Propulsion Laboratory California Institute of Technology The NASA/JPL Airborne Synthetic Aperture Radar System (AIRSAR) Yunling Lou Jet Propulsion.

Jet Propulsion Laboratory California Institute of Technology The NASA/JPL Airborne Synthetic Aperture Radar System (AIRSAR) Yunling Lou Jet Propulsion.
Www.le.ac.uk www.gionet.eu Forest Monitoring of the Congo Basin using Synthetic Aperture Radar (SAR) James Wheeler PhD Student Supervisors: Dr. Kevin Tansey,

Forest Monitoring of the Congo Basin using Synthetic Aperture Radar (SAR) James Wheeler PhD Student Supervisors: Dr. Kevin Tansey,
Major Operations of Digital Image Processing (DIP) Image Quality Assessment Radiometric Correction Geometric Correction Image Classification Introduction.

Major Operations of Digital Image Processing (DIP) Image Quality Assessment Radiometric Correction Geometric Correction Image Classification Introduction.
PRESENTATION ON “ Processing Of Satellite Image Using Dip ” by B a n d a s r e e n i v a s Assistant Professor Department of Electronics & Communication.

PRESENTATION ON “ Processing Of Satellite Image Using Dip ” by B a n d a s r e e n i v a s Assistant Professor Department of Electronics & Communication.
Estimating forest structure in wetlands using multitemporal SAR by Philip A. Townsend Neal Simpson ES 5053 Final Project.

Estimating forest structure in wetlands using multitemporal SAR by Philip A. Townsend Neal Simpson ES 5053 Final Project.
Use of Remote Sensing and GIS in Agriculture and Related Disciplines

Use of Remote Sensing and GIS in Agriculture and Related Disciplines
VENUS (Vegetation and Environment New µ-Spacecraft) A demonstration space mission dedicated to land surface environment (Vegetation and Environment New.

VENUS (Vegetation and Environment New µ-Spacecraft) A demonstration space mission dedicated to land surface environment (Vegetation and Environment New.
Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module.

Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module.
Image Classification http://www.crssa.rutgers.edu/courses/remsens/rem_cpe_1/remsensing6/sld001.htm http://faculty.wwu.edu/medlerm/classes/10_11/451/04/Web%20Applications/Unsupervised_Classification/UN_sup_class.htm.

Image Classification
Landscape-scale forest carbon measurements for reference sites: The role of Remote Sensing Nicholas Skowronski USDA Forest Service Climate, Fire and Carbon.

Landscape-scale forest carbon measurements for reference sites: The role of Remote Sensing Nicholas Skowronski USDA Forest Service Climate, Fire and Carbon.
Co-authors: Maryam Altaf & Intikhab Ulfat

Co-authors: Maryam Altaf & Intikhab Ulfat
Www.nr.no earthobs.nr.no Land cover classification of cloud- and snow-contaminated multi-temporal high-resolution satellite images Arnt-Børre Salberg and.

earthobs.nr.no Land cover classification of cloud- and snow-contaminated multi-temporal high-resolution satellite images Arnt-Børre Salberg and.
Ben Gurion University Mission scientists (PI's) : Gérard Dedieu & Arnon Kanieli G. Dedieu 1, O. Hagolle 2, A. Karnieli 3, S. Cherchali 2 P. Ferrier 2 and.

Ben Gurion University Mission scientists (PI's) : Gérard Dedieu & Arnon Kanieli G. Dedieu 1, O. Hagolle 2, A. Karnieli 3, S. Cherchali 2 P. Ferrier 2 and.
Microwave Remote Sensing Group 1 P. Pampaloni Microwave Remote Sensing Group (MRSG) Institute of Applied Physics -CNR, Florence, Italy Microwave remote.

Microwave Remote Sensing Group 1 P. Pampaloni Microwave Remote Sensing Group (MRSG) Institute of Applied Physics -CNR, Florence, Italy Microwave remote.
On Estimation of Surface Soil Moisture from SAR Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara.

On Estimation of Surface Soil Moisture from SAR Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara.
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.

Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Seto, K.C., Woodcock, C.E., Song, C. Huang, X., Lu, J. and Kaufmann, R.K. (2002). Monitoring Land-Use change in the Pearl River Delta using Landsat TM.

Seto, K.C., Woodcock, C.E., Song, C. Huang, X., Lu, J. and Kaufmann, R.K. (2002). Monitoring Land-Use change in the Pearl River Delta using Landsat TM.

Similar presentations

Ads by Google