A SIMPLE METHOD TO DETECT LAND CHANGES SOURCING FROM OVERGRAZING, USING REMOTE SENSING

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1 A SIMPLE METHOD TO DETECT LAND CHANGES SOURCING FROM OVERGRAZING, USING REMOTE SENSING Papadavid G. 1, Themistocleous K. 1, Christoforou M. 2, Carmen B. 1, Tsaltas D. 2, Hadjimitsis D 1. 1 Cyprus University of Technology, Department of Civil Engineering and Geomatics 2 Cyprus University of Technology, Department of Agricultural Sciences, Biotechnology and Food Science Abstract This is a technical paper, in the context of FP 7 CASCADE project, describing an overgrazed area in Cyprus and how remote sensing techniques can assist the procedure for detecting land degradation sourcing from animal overgrazing. Remote sensing is a tool recently introduced to such studies but indeed very useful and vital. Using satellite images it is possible to retrieve consecutive vegetation indices which can identify if there is any further land-vegetation degradation in a specific area of interest. This is crucial in the procedure for monitoring semi or highly overgrazed areas since this change detection can inform policy makers regarding the status of an area, in terms of degradation. In this paper remotely sensed data is analyzed to detect, in specific areas which are known as overgrazed, to detect if there is a change using three main vegetation indices, namely WDVI, NDVI and SAVI. Change detection techniques are applied on these three vegetation indices maps in order to detect any further areas overgrazing. Keywords: overgrazed area, vegetation indices, land changes 1. Introduction 1.1. Area description Cyprus is the third largest island in the Mediterranean region located in the east region, with an area of Km 2 [1]. The island of Cyprus is dominated by two mountain ranges, Troodos which is located at the central and western part and Pentadaktylos at the north part (Kyrenia range). The geological history is characterized by marine sedimentation in a sea that became gradually shallow. The climate is intense Mediterranean, with wet changeable winters from November to March, and long hot, dry summers from May to September, separated by short spring and autumn seasons of rapidly First International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2013), edited by Diofantos G. Hadjimitsis, Kyriacos Themistocleous, Silas Michaelides, George Papadavid, Proc. of SPIE Vol. 8795, SPIE CCC code: X/13/$18 doi: / Proc. of SPIE Vol

2 changing weather. Rainfall varies from year to year and in different parts of the island. Soil studies and soil classification started in Cyprus in 1957 consisted by physical and chemical data of soil properties [1]. The majority of soils of Cyprus display near neutral to alkaline ph values when slurred with water. This reflects the influence of carbonates, as well as colluviums alluvium area and alkaline earth oxides and hydroxides derived from dominate formations generated soil ph > 8. This is significantly more alkaline than the average for the rest of Europe (ph ). Pissouri area is located at the south zone of Cyprus, in the south east part of Paphos district area and consist randi forests (located in Pissouri basin), being characterized as dry lands. The dominated soils in the area are calcaric regosoils, with deep brown color. The soil contains clay round 25%, and mud 40%. In the valley and on hills there is a slight inclination of 20% deeper soils presented (colluviums), with clay content of 30% - 40%, and a mud content of 50%. Many parts of the area are characterized by severe topsoil erosion losses. Thus, the root system in limited by the underline strata. Furthermore, the high CaCO 3 content and clay soils, reduce the infiltration rates and thus increases water erosion. Vegetation is consisted with plants from different habitats such as pine forests, phrygana, maquis and rocky faces. In Pissouri area, despite plants growth inhibition, cause to shallow soils and water erosion, an extra factor, grazing by sheeps and goats is retarding plant growth. Since land is not suitable for crop production, it is used for animal production. Land use changes have resulted in overgrazing. As a result of overgrazing and dry climate of the island, vegetation has significantly diminished in the area. Today, livestock raising present significant pollution sources for both soil and ground water reservoirs through the use of large quantities of liquid and solid waste from stock. The government has allocated permits to shepherds allowing them to use the land for grazing for approximately 600 animals. The actual number of animals found in farms grazing in Randi Forest, are more than 3,700. The non-licensed shepherds own another 1240 animals which are grazing in this area. As a result we have more than 4000 extra animals in this area. The amount of land allocated in the Randi Forest for grazing is around 1000 hectares, which is approximately 70% of Randi Forest. Proc. of SPIE Vol

3 Figure 1 Exposure of dissected calcareous sediments (mainly calcarenites in Pissouri area), and drape structure exposed on the freeway near Pissouri Review on Vegetation Indices Vegetation indices are mostly empirical equations describing vegetation parameters during the lifecycle of the crops [2]. Nearly all of the commonly used vegetation indices are only concerned with red/near-infrared spectrum and can be divided to perpendicular (WDVI, SAVI) and ratio based indices (NDVI). The former are generally more influenced by atmospheric effects [3] and hence more sensitive to atmospheric correction algorithms; thus a sound atmospheric correction algorithm should always be used to increase accuracy on data. An overview of the basics of each of the above indices is shown below: NDVI: The most known and widely used ratio-based index is the normalized difference vegetation index (NDVI) [4]. NDVI is very sensitive to soil background at low LAI, and the sensitivity of NDVI to LAI weakens when LAI exceeds a threshold value, which is typically around three [5]. NDVI = (NIR - R)/(NIR + R) Where NIR is the reflectance of near infrared band, RED is the reflectance of visible red band. NDVI is ranging from 1 to +1.The negative value represents non vegetated area while positive value represents vegetated area. Proc. of SPIE Vol

4 SAVI: [6] developed the soil-adjusted vegetation index (SAVI) by shifting the convergent point of iso-vegetation lines from the origin to a point in the quadrant of negative red and NIR values. Many studies have indicated that SAVI not only reduces the effect of soil variability for low LAI, but also increases the sensitivity to high LAI [2, 7]. However, in order to determine the optimal value of the soil background adjustment factor L, prior knowledge about vegetation density or LAI is required, which creates a loop problem since LAI is the unknown target variable. L varies with the reflectance characteristics of soil. The L factor chosen depends on the density of the vegetation. For very low vegetation L factor can be taken as 1.0 while for intermediate it can be taken as 0.5 and for high density The best L value to select is where the difference between SAVI values for dark and light soil is minimal. From previous studies [7] L is set to 0.6 for Cyprus and specifically for short vegetation. SAVI is mathematically expressed as: SAVI = (1 + L)(NIR - R)/(NIR + R + L) Where NIR is the reflectance of near infrared band, RED is the reflectance of visible red band and L is the soil adjustment factor. WDVI: WDVI is a corrected near-infrared reflectance, known as Weighted Difference Vegetation Index. WDVI is calculated by subtracting the contribution of the soil from the measured reflectance. As WDVI is a distance based index, relatively better atmospheric correction needs to be applied to the data, which is not necessary for ratio based indices like NDVI and SAVI. But using spectroradiometer, as mentioned before this limitation is overpassed. Weighted Difference Vegetation Index (WDVI) is defined as follows [8]: WDVI=NIR-γR Where, NIR : reflectance of near infrared band, RED: reflectance of visible red band, γ: slope of the soil line. [9]. The WDVI index has the advantage to reduce to a great extent the influence of soil background on the surface reflectance values. Although simple, WDVI is as efficient as most of the slope based SVI s. The effect of weighting the red band with the slope of the soil line is the maximization of the vegetation signal in the near-infrared band and the minimization of the effect of soil brightness. After creating a set of data from bare soil of the area (red and infrared bands), the slope of the line was set to Materials and methods In this paper authors have identified and described an area that suffers from overgrazing. Their main purpose is to illustrate how remote sensing techniques can be employed to stress out a simple method to detect the impact of grazing on a specific area. The area of interest is located in Pissouri, called as Randi Forest and can be optically distinguished into three sub-areas: the fully vegetated area, the semi-overgrazed area and the overgrazed areas. Proc. of SPIE Vol

5 Plants in the area of interest were identified using the natural key system which is based on morphological characteristics such as structures of stems, roots and leaves, embryology and flowers. Plant parts were collected and photographed from an area of 50 ha. Soil samples were collected at 10 cm depth, from three different sites (A, B and C) in the area of interest. Soil was analyzed for its hydronium activity (ph 1:2 soil-water, soil samples were sieved (x 450 μm) and air-dried) and its hydraulic conductivity (K), using a mini disk infiltrometer (Decagon Devices, Inc, Pullman, WA). Two satellite images of the same period but of different time (March 2003 and March 2012) are used as a tool to identify any changes in the area of interest as a whole but also in the three particular sub-areas. Satellite images were preprocessed. Preproccessing included geographic rectification, radiometric and atmospheric corrections according to standard procedures [2, 3]. After the satellite images are preproccessed and brought into their final form (reflectance), those are transformed into Spectral Vegetation Indices maps. As mentioned three of the basic VI was used in this study, namely NDVI, SAVI and WDVI. The basic idea is to compare identical maps of the same area but of different time so as to detect from the pixel values any differences sourced basically from the overgrazing taking place in the area, which we are aware of it in advance. It is expected that as time passes through the specific area the vegetation should decrease due to overgrazing. It was consider rational to choose satellite images of the month March since is the most "intensive" month according to the farmers, where goats and sheep s are out in the fields for grazing, but also in terms of vegetation the earth's surface is fully covered in Cyprus. Preproccessing and processing of the remotely sensed data is applied using the ERDAS Imagine v.11 software. 3. Results 3.1. Plant species identification Totally 56 species were identified within the area of interest. The hills were covered with shrubs. Various types of shrubs communities are dominated in the thermo-mediterranean semi-arid zones. The area of interest is typical coastal zone with dry grasslands, shrubs and forest openings. Trees, shrubs and grass were observed among the species, where the most dominant ones in the area were Olea europaea, Calycotome villosa, Cistus parviflorus, Genista fasselata, Sinapis alba, Malva sylvestris and wild poaceae species. Eighteen plant families were observed (Anacardiaceae, Apiaceae, Asteraceae, Cistaceae, Compositae, Ericaceae, Fabaceae, Lamiaceae, Malvaceae, Oleaceae, Papillionacea, Pinaceae, Poaceae, Ranumculaceae, Rosaceae, Rubiaceae, Urticaceae and Zygophyllaceae). Plants latin name is shown in table 1. Proc. of SPIE Vol

6 3.2. Soil analysis Hydronium activity (ph) measurements of all three soil samples indicated that soil in the area of interest tends to be alkaline (ph ). Hydraulic conductivity (K) was found to be 2.5 x 10-5, 1.2 x 10-5 and 2.7 x 10-5 for sites A, B and C respectivly. Table 1 Plants identified in Pissouri area A/A Plant Species 1 Allium roseum 2 Anthemis parvifolia 3 Anthemis plutonia 4 Arbutus andrachne 5 Asfodelos sp 6 Asperula cypria 7 Avena fatua 8 Avena sterilis 9 Bromus arvensis 10 Bromus sterilis 11 Bromus sterilis 12 Bromus tectorum 13 Calycotome villosa 14 Centaurea cyprium 15 Centaurea pallescens 16 Ceratonia siliqua 17 Chrysanthemum sp. 18 Cirsium arvense 19 Cistus parviflorus 20 Cota amblyolepis 21 Daucus carota 22 Erptium sp 23 Fagonia cretica 24 Genista fasselata 25 Hordeum murinum 26 Inula viscosa 27 Lolium rigidum 28 Mafricaria chamomile 29 Malva sylvestris 30 Nomea mucronats 31 Notoposis sp 32 Olea europaea 33 Onopordum cyprium 34 Phagnalon grecum 35 Phagnalon rupestre 36 Phalaris minor 37 Pinus brutia 38 Pistacia lentiscus 39 Pistacia terebinthus 40 Plantago lanceolata 41 Quercus alnifolia 42 Ranumculus sp 43 Rhamnos oleoides 44 Sarcopoterium spinosum 45 Scila morissi 46 Scorpioros muricatus 47 Senecio glaucus 48 Setaria glauca 49 Sinapis alba 50 Tanacetum balsamita 51 Taraxacum aphrogenes 52 Taraxacum officinalis 53 Thymus capitatus 54 Tribulus terrestris 55 Trifolium sp 56 Urtica urens 3.3. Change detection on VI maps NDVI, SAVI and WDVI are the spectral vegetation indices that were selected in order to be applied on remotely sensed data to detect any further vegetation reduction and thus soil degradation. Such indices are found to be widely used in various models. The results of this method are shown in Tables 2 for the AOI as a whole and in Tables 3, 4 and 5 for the sub-areas. This simple method allows users to detect if there has been any further land degradation among at least two different dates by using satellite images. Basically, it is possible, using these VI to identify if the vegetated cover of an area has decreased due to overgrazing, since it is in advance known that the area suffers from this phenomenon. The VI maps (Figure 2) that have been created after pre and post processing of the satellite images can be used to infer the VI values from specific pixels for all three sub-areas and the AOI as a whole, in attempt to find out what happened to area s status after specific time. Proc. of SPIE Vol

7 Reflectance map Ii N NDVI map I SAVI map r.-, 1 w. :. ". lk _ 4 z 2D Vie 0.1, WDVI map E Figure 2. Example of Reflectance and VI maps of the AOI Table 2 Values of VI for the two dates of the Area of Interest (AOI) Vegetation Indices Satellite Images March 2003 March 2012 AOI NDVI SAVI WDVI Value St. Dev Value St. Dev In Table 2 the results of the applied method for the AOI (three sub-areas are included) as a whole are shown. What can be inferred from the Table 2 is the fact that all three VI maps show a significant decrease of the area s vegetation from 2003 to It is obvious that NDVI can detect this change better than the other SAVI and WDVI. NDVI has shown a ruction of area s Vegetation of 32% while SAVI and WDVI a reduction of 10% and 13% correspondingly. Proc. of SPIE Vol

8 Table 3 Values of VI for the two dates of the Vegetated Area Vegetation Satellite Images Indices March 2003 March 2012 Vegetated Area Value St. Dev. Value St. Dev. NDVI SAVI WDVI Table 3 shows the VI values of the two VI maps of 2003 and 2012 of the sub-area named as Fully Vegetated Area in the AOI. No significant change is observed in this sub-area since vegetation cover has not suffered any reduction due to overgrazing. All three VI has shown that the vegetation level of this area is stable. Table 4 Values of VI for the two dates of the Semi Overgrazed Area Vegetation Satellite Images Indices March 2003 March 2012 Semi-OG Area Value St. Dev. Value St. Dev. NDVI SAVI o WDVI Table 4 shows the VI values of the two VI maps of 2003 and 2012 of the sub-area named as Semi-overgrazed Area in the AOI. In this area a significant change of vegetation has taken place between 2003 and All three VI illustrate that vegetation of the semi- overgrazed are has been decreased due to overgrazing. Again NDVI is the vegetation index that shows this reduction the best. Table 5 Values VI for the two dates of the Overgrazed Area Vegetation Satellite Images Indices March 2003 March 2012 OG Area Value St. Dev. Value St. Dev. NDVI SAVI WDVI Table 5 shows the VI values of the two VI maps of 2003 and 2012 of the sub-area named as Overgrazed Area in the AOI. This area is considered as overgrazed before 2003 and still is suffering from overgrazing. It is obvious that there is no significant reduction on the vegetation level from 2003 but if one looks at the values of 2003 of the three VI can immediately realize that vegetation level in 2003 was very low since the area was characterized as overgrazed since then. Proc. of SPIE Vol

9 These VI values which are very close for 2012 and 2003 can be interpreted as almost no vegetation in the area which is the true even in March where vegetation is at its highest level. Conclusions The paper described an area of interest that suffers from overgrazing in Cyprus. Vegetation and Soil characteristics have been illustrated but also the methods of defining these characteristics. Then a simple method for detecting change in vegetation level has been applied in order to find out had happened to the AOI from 2003 to In this method the results have indicated that NDVI is the Vegetation Index that better detects any changes of vegetation level and that the only sub area that suffered the most from overgrazing for this specific time was the semi-overgrazed area. REFERENCES [1] Hadjiparaskevas C. Soi l su rvey i n Cypru s. In : Zdru li P., Stedu to P. (ed.), Lacirign ola C., Mon tan arella L Soil resources of Southern and Eastern Mediterranean countries. Bari : CIHEAM,. p (Option s Méditerran éen n es : Série B. Etu des et Rech erch es; n. 34) [2] Papadavid G., Hadjimitsis D.G., Michaelides S., Effective irrigation management using the existing network of meteorological stations in Cyprus. Advances in Geosciences Journal, 9, [3] Hadjimitsis d. G., Papadavid g., Agapiou a., Themistocleous k.,hadjimitsis m. G., Retalis a., Michaelides s., chrysoulakis n., toulios l., and clayton c. R. I., Atmospheric correction for satellite remotely sensed data intended for agricultural applications: impact on vegetation indices, Nat. Hazards Earth Syst. Sci., 10, [4] Rouse, J.W., Haas Jr., R.H., Schell, J.A., Deering, D.W., Monitoring Vegetation Systems in the Great Plains with ERTS. In: NASA SP-351, 3 rd ERTS-1 Symposium, Washington, DC, pp [5] Huete, A.R., Soil influences in remotely sensed vegetation-canopy spectra. In: Asrar, G. (Ed.), Theory and Application of Optical Remote Sensing. Wiley, New York, pp [6] Barnes, E.M., K.A. Sudduth, J.W. Hummel, S.M. Lesch, D.L. Corwin, C. Yang, C.S.T. Daughtry, and W.C. Bausch, Remote- and ground-based sensor techniques to map soil properties, Photogrammetric Engineering & Remote Sensing, 69(6): [7] Papadavid G. and Hadjimitsis D.G Spectral signature measurements during the whole life cycle of annual crops and sustainable irrigation management over Cyprus using remote sensing and spectro-radiometric data: the cases of spring potatoes and peas, Proc. SPIE 7472, SPIE Remote Sensing Europe, Berlin. 2009, DOI: / [8] Clevers J.G.P.W., The application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture, Remote Sensing of Environment, 29, Proc. of SPIE Vol

10 [9] Elvidge, C.D., and Chen, Z., Comparison of broadband and narrow-band red and near infrared vegetation indices, Remote Sens. Environ. 54, Acknowledgment The authors would like to acknowledge the support from the FP-7 funded project CASCADE - Catastrophic shifts in drylands: how can we prevent ecosystem degradation? Proc. of SPIE Vol