Study area Samples Piezometers Slightly saline Moderately saline Very saline Extreme saline

Study area

Introduction

Abstract Arid and semi-arid Regions are largely affected by salinity. Irrigation regions and oases in Southern Tunisia are as such Typical areas, where the excessive content of salt in the soil is a challenging phenomenon. Salinity engenders several environmental problems such as limiting plant growth and reducing crop productivity. This work benefits from the combination of remote sensing and ground-based geochemical measurements. We use remote sensing to diagnose and monitor arid land degradation and environmental change caused by salinity in Southern Tunisia. Soil samples from the upper 10 cm top-soils were collected in southern Tunisia and analyzed to provide on the ground data. We then classify degraded soils using supervised and unsupervised classifications. A database was built under the shape of WebGIS. It gathers all the information, data, inputs and outputs of this research project. Furthermore, we placed the causal factors underlying dry land degradation into a Geographic information (GIS) database. This permits a rapid diagnosis of the causes of land degradation for the purpose of designing intervention strategies. The present study highlights also that classes of extreme and high saline soils are predominantly represented by gypsum rich soils.

Monitroing of land degradation from space: a focus on soil erosion and salinity in arid/semi‐arid regions

Moncef Bouaziz

Institut für Geographie, Lehrstuhl Physische Geographie, Helmholtzstr. 10 TU-Dresden, 01062 Dresden, GERMANY

Dolomite

Figure 1: Map of global soil degradation (2006 UNEP)

Soil sampling method

Remote sensing data and geochimical

anaysis data

Remote sensing techniques and

statistic applications

ArcGIS database development (using

SQL)

WebGIS development (using php)

Flow diagram of geo-database development

Geo-data base development

Bannari, A., Guedon, A. M., El-Harti, A., Cherkaoui, F. Z., El-Ghmari A., 2008. Characterization of Slightly and Moderately Saline and Sodic Soils in Irrigated Agricultural Land using Simulated Data of Advanced Land Imaging (EO-1) Sensor, Communications in Soil Science and Plant Analysis,39:19, 2795 – 2811. Ben ferjani A, Burollet P, Mejri F. 1990. Petroleum Geology of Tunisia. Entrprise Tunisienne d Activites Petriolieres (ETAP): Tunis. Ben Mohamed, M., 2002. Geothermal utilization in agriculture in Kebeli region, southern Tunisia. GHC Bulletin, 27–32. Bishop, T.F.A., McBratney, A.B., 2001. A comparison of prediction methods for the creation of field-extent soil property maps. Geoderma 103, 149–160. Carré, F., Girard, M.C., 2002. Quantitative mapping of soil types based on regression–kriging of taxonomic distances with landform and land-cover attributes. Geoderma 110, 241–263. Csillag F., Pasztor L., Biehl L.L., 1993. Spectral band selection for the characterization of salinity status of soils, Remote Sens. Environ. 43, 231–242. Douaoui A. K., Hervé Nicolas, Christian Walter, 2006. Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data, Geodema V. 134, 217-230. Everitt, D. E., Gerbermann, A., Alaniz, M., 1988. Detecting saline soils with video imagery, Photogramm. Eng. Remote Sensing 54. 1283–1287. Faust D., 1995. Habilitation. Reliefgenese, Böden und Bodenerosion im südwestlichen Mediterranraum und deren Bedeutung für Fragen der Landnutzung und des Ressourcenschutzes. Beispiele aus Spanien und Tunesien. 295.

Potential contributions of the proposed research •  This research work develops a conceptual model for the quantification and

qualification of land degradation (erosion, salinization, and desertification) based on multi-source remote sensing data

•  Our approach provides a WebGIS data base and reliable method for monitoring of land degradation. It enable us to adequately predict, diagnose the roots of land degradation, and mitigate the problems related to land degradation in arid environments.

Indices NDVI NIR− R / NIR+ R EVI EVI = 2.5*(NIR− R / (NIR+ 6*R− 7.5B+1)) SI1 RG ∗

SI2 G2 + R2 + NIR2 SI3 G2 + R2

SI-11 SWIR1 SWIR2

ASTER-SI (SWIR1− SWIR2)(SWIR1+ SWIR2)

Methods & materials

Monitoring and Geo-database development

Spectral indices

Field work, radiometric

measurements, and soil sample

analysis

Assessment of soil

degradation

Remote mapping of land

degradation

ESP and SAR indices

Analysis and Interpretation

Database development

Classification algorithms

Flow diagram of study

Study  area

Study area

Tunis

Sfax

b)

Gabes

Example of WebGIS developed for the region of Gabes, southern Tunisia.

Average of spectral reflectance patterns of different soil classes from Hyperion image

Fakultät Umweltwissenschaften

30 m

30 m

Sample X.3

Sample X.2 Sample X.1

Sample X.4

Sample X

Mixing of the samples from the 4 corners to represent

one soil sample