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,

Slides:



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

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.
Jet Propulsion Laboratory California Institute of Technology The NASA/JPL Airborne Synthetic Aperture Radar System (AIRSAR) Yunling Lou Jet Propulsion.
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.
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.
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.
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
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
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.
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.
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.
earthobs.nr.no Temporal Analysis of Forest Cover Using a Hidden Markov Model Arnt-Børre Salberg and Øivind Due Trier Norwegian Computing Center.
1 Land Cover Land Use Change Program and LBA Dr. Garik Gutman LCLUC Program Manager NASA Headquarters.
1 Exploiting Multisensor Spectral Data to Improve Crop Residue Cover Estimates for Management of Agricultural Water Quality Magda S. Galloza 1, Melba M.
Change Detection Techniques D. LU, P. MAUSEL, E. BRONDIZIO and E. MORAN Presented by Dahl Winters Geog 577, March 6, 2007.
Remote Sensing Realities | June 2008 Remote Sensing Realities.
 Up to what level of classification can we perform on LISSIII/LISSIV data?  Is any advantage of high spectral resolution of LISSIII over LISSIV. If.
Land Cover Change Monitoring change over time Ned Horning Director of Applied Biodiversity Informatics
Land Cover Change Monitoring change over time Ned Horning Director of Applied Biodiversity Informatics
Digital Image Processing Definition: Computer-based manipulation and interpretation of digital images.
BIOPHYS: A Physically-based Algorithm for Inferring Continuous Fields of Vegetative Biophysical and Structural Parameters Forrest Hall 1, Fred Huemmrich.
Terra Launched December 18, 1999
IGARSS 2011, Vancouver Oliver Lang Parivash Lumsdon
Validation of MODIS Snow Mapping Algorithm Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara.
Assessing the Phenological Suitability of Global Landsat Data Sets for Forest Change Analysis The Global Land Cover Facility What does.
On Estimation of Soil Moisture with SAR Jiancheng Shi ICESS University of California, Santa Barbara.
Chapter 8 Change Detection. n RS & GIS to inventory and monitor natural and cultural phenomena on the surface of the Earth n Some may be static, but many.
Hyperspectral remote sensing
Classification Method Validation for Rice Mapping Using ENVISAT APS Data Erxue CHEN(1), Zengyuan LI(1), BingxiangTan(1) , Wei He(1), Bingbai LI(2) (1)Institute.
Land Cover Classification and Monitoring Case Studies: Twin Cities Metropolitan Area –Multi-temporal Landsat Image Classification and Change Analysis –Impervious.
APPLIED REMOTE SENSING TECHNOLOGY TO ANALYZE THE LAND COVER/LAND USE CHANGE AT TISZA LAKE By Yudhi Gunawan * and Tamás János ** * Department of Land Use.
Recent SeaWiFS view of the forest fires over Alaska Gene Feldman, NASA GSFC, Laboratory for Hydrospheric Processes, Office for Global Carbon Studies
The Pacific GIS/RS User Conference Suva, Fiji Island, November 2012 Sharon R. Boe, SPC/GIZ-SOPAC ) SPC/GIZ Regional REDD+ Project:
Cropland Extent Mapping in South America Global Food Security - Support Analysis m Chandra Giri, Ying Zhong January 19 th, 2016.
REMOTE SENSING DATA Markus Törmä Institute of Photogrammetry and Remote Sensing Helsinki University of Technology
ReCover for REDD and sustainable forest management 1 An overview of the ReCover project, focusing on the Democratic Republic of Congo 04 October 2012,
Landsat Satellite Data. 1 LSOS (1-ha) 9 Intensive Study Areas (1km x 1km) 3 Meso-cell Study Areas (25km x 25km) 1 Small Regional Study Area (1.5 o x 2.5.
Dengsheng Lu Professor Center for Global Change and Earth Observations Michigan State University, East Lansing, Michigan April.
LAND USE/LAND COVER CHANGE IN BEXAR COUNTY, TEXAS Maryia Bakhtsiyarava FNRM 5262.
Detecting Land Cover Land Use Change in Las Vegas Sarah Belcher & Grant Cooper December 8, 2014.
Using SAR Intensity and Coherence to Detect A Moorland Wildfire Scar.
Gofamodimo Mashame*,a, Felicia Akinyemia
Temporal Classification and Change Detection
Active Microwave Remote Sensing
  Assessing the impact of land use and land cover change on streamflow response: case study of Dinder and Rahad, Ethiopia/Sudan Khalid E. A. Hassaballah,
Hyperspectral Sensing – Imaging Spectroscopy
Built-up Extraction from RISAT Data Using Segmentation Approach
Objectives Using a time series of data from radar sensors to detect and measure forest changes Combining different types of data, including: Multi polarisations.
Map of the Great Divide Basin, Wyoming, created using a neural network and used to find likely fossil beds See:
National seminar on ENVIRONMENT AND DEVELOPMENT IN EASTERN INDIA
By Yudhi Gunawan * and Tamás János **
Toshio Okumura (RESTEC), Shin-ichi Sobue (JAXA), Takeo Tadono (JAXA)
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.
Potential Landsat Contributions
Paulo Gonçalves1 Hugo Carrão2 André Pinheiro2 Mário Caetano2
Satellite Sensors – Historical Perspectives
NASA alert as Russian and US satellites crash in space
Planning a Remote Sensing Project
2011 International Geoscience & Remote Sensing Symposium
Forest / Non-forest (FNF) mapping for Viet Nam using PALSAR-2 time series images 2019/01/22 Truong Van Thinh – Master’s Program in Environmental.
Presentation transcript:

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 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,

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

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

Fortaleza, Areas of LULC classes in the study area

Fortaleza, Change detection (%) for the period (Diagonal represents unchanged fraction of each class) Change from Cultivated land WoodlandFallow landBarelandSettlementWater Cultivated land Woodland Fallow land Bareland Settlement

Fortaleza, LULC changes in the period 1979 – 2009

Fortaleza, LULC changes in the period 1979 – 2009

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

Image filtering 11 Fortaleza, 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 Gamma speckle filtering (7x7) HVHHHH/HV Raw Filtered Fortaleza,

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

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,

15 TerraSAR-X land use/cover map

16 Fortaleza, TerraSAR-X land use/cover map

17 Fortaleza, TerraSAR-X land use/cover map

18 Summary of classification accuracies Fortaleza, Class Accuracy (%) Conditional kappaArea (%) Producer’sUser’s Cultivated land Harvested land Fallow land Woodland Bareland Rock Settlement Settlement Water Overall accuracy (%)94 Kappa coefficient0.93

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

20 Fortaleza, Soil chemical properties

21 Fortaleza, Soil penetration resistance

22 Fortaleza, Soil infiltration (Kostiakov model)

23 Fortaleza, Maps of soil penetration resistance

24 Fortaleza, Maps of soil organic carbon

25 Fortaleza, Maps of soil moisture content

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 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, 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 , (Under work). Fortaleza,

26 Thank You! Fortaleza,