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BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access May 24, 2016

Relationship between landform classification and vegetation (case study: southwest of Fars province, Iran)

  • Marzieh Mokarram EMAIL logo and Dinesh Sathyamoorthy
From the journal Open Geosciences

Abstract

This study is aimed at investigating the relationship between landform classification and vegetation in the southwest of Fars province, Iran. First, topographic position index (TPI) is used to perform landform classification using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) with resolution of 30 m. The classification has ten classes; high ridges, midslope ridges, upland drainage, upper slopes, open slopes, plains, valleys, local ridges, midslope drainage and streams. Visual interpretation indicates that for the local, midslope and high ridge landforms, normalized difference vegetation index (NDVI) values and tree heights are higher as compared to the other landforms. In addition, it is found that there are positive and significant correlations betweenNDVI and tree height (r = 0.923), and landform and NDVI (r = 0.640). This shows that landform classification and NDVI can be used to predict tree height in the area. High correlation of determination (R2) 0.909 is obtained for the prediction of tree height using landform classification and NDVI.

1 Introduction

Information on terrain characteristics is very important to explain geographical constraints and map the variability of natural resources in maintaining sustainable vegetation management for assessment of land use capabilities. Furthermore, the study on the relationship between vegetation and landform classification is important because the distribution of vegetation based on the analysis of landform characteristics is an important aspect in the process of understanding ecology [13]. In addition, the existence of landforms such as ridges indicate flood frequency. On the other hand, increasing vegetation decreases floods in the area [4, 5].

Debelis et al. [6] used soil characteristics and vegetation as a function of landform position to develop a system that allows for extrapolations to be made at the landscape scale on the relationship with vegetation. Loučková [7] investigated the association between landforms and vegetation. The results obtained suggest that recently created landform geomorphic forms are key environmental determinants of riparian vegetation distribution patterns. In the research of Cremon et al. [8], the main goal was to investigate the relationship between paleolandforms and vegetation classes mapped based on the integration of optical and synthetic aperture radar (SAR) data using the decision tree analysis. The results obtained indicated that the former was useful for separating between forest and open vegetation classes. Ahmad Zawawi et al. [1] studied the relationship between landform classes and normalized difference vegetation index (NDVI). They found thatNDVI decreases with elevation, and increases with tree height [9].

This study is aimed at investigating the relationship between landform classification and vegetation in the southwest of the Fars province, Iran. It will be demonstrated using multiple regressions that landform classification and NDVI can be used to accurately predict tree height.

2 Study Area

This study was carried out in west of Fars Province, which is located in southern Iran and has an area of about 122,400 km2 and is located at longitude of N 29°24′–29°35′ and latitude of E 50°24′–50°65′ (Figure 1). The altitude of the study area ranges from the lowest of 1,538 m to the highest of 2,830 m. There are three distinct climatic regions in the Fars Province: 1) The mountainous area of the north and northwest with moderate cold winters and mild summers. 2) The central regions with relatively rainy mild winters and hot dry summers. 3) The region is located in the south and southeast, has cold winters with hot summers. The average temperature for the area is 16.8 °C, ranging between 4.7°C and 29.2°C [10].

Figure 1 SRTM DEM of the study area
Figure 1

SRTM DEM of the study area

The study area is a biodiversity of rich mountains, relief and lithology, and other geological characteristics such as sedimentary basin and elevated reliefs [10]. The major land use categories of the area are agriculture, range land, farming and forests. Range lands are found in large parts of the north and south of the study area; forests lands form a belt in the center of the study area; while wood lands are located in small parts of the north and south of the study area (Figure 2).

Figure 2 Land use map of the study area
Figure 2

Land use map of the study area

As the study area is located in a semi-arid region, its river floods in the parts of years. The dominant causes of gully formation in the area are rangeland destruction, land use change fromrangeland to dryland, misdesign and construction of road culverts, road construction in sensitive areas, improper irrigation, and destruction of channels for flood conveyance [10].

3 Materials and methods

3.1 Landform classification

In this study, the topographic position index (TPI) method [11] was used to perform landform classification from a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) of the study area, which was downloaded from http://srtm.csi.cgiar.org. TPI is the difference between the elevation at a cell and the average elevation in a neighborhood surrounding that cell. Negative values indicate the cell is lower than its neighbors, while positive values indicate that the cell is higher than its neighbors. TPI values provide a powerful means to classify the landscape into morphological classes [12].

TPI values can be calculated from two neighborhood sizes. A negative small-neighborhood TPI value and a positive large-neighborhood TPI value is likely to represent a small valley on a larger hilltop. Such a feature may be reasonably classified as an upland drainage. Conversely, a point with a positive small-neighborhood TPI value and a negative large-neighborhood TPI value likely represents a small hill or ridge in a larger valley [13] (Table 1 and Figure 3).

Figure 3 Landform classification map of the study area. Relationship between landform classification and vegetation
Figure 3

Landform classification map of the study area. Relationship between landform classification and vegetation

Table 1

Definitions of landform classes using TPI [13].

ClassesDescription
Canyons, deeply incised streamsSmall Neighborhood TPI: TPI ≤ –1
Large Neighborhood TPI: TPI ≤ –1
Midslope drainages, shallow valleysSmall Neighborhood TPI: TPI ≤ –1
Large Neighborhood TPI: –1 < TPI < 1
upland drainages, headwatersSmall Neighborhood TPI: TPI ≤ –1
Large Neighborhood TPI: TPI ≥ 1
U-shaped valleysSmall Neighborhood TPI: –1 < TPI < 1
Large Neighborhood TPI: TPI ≤ –1
Plains smallNeighborhood TPI: –1 < TPI < 1
Large Neighborhood TPI: –1 < TPI < 1 Slope ≤ 5°
Open slopesSmall Neighborhood TPI: –1 < TPI < 1
Large Neighborhood TPI: –1 < TPI < 1 Slope > 5°
Upper slopes, mesasSmall Neighborhood TPI: –1 < TPI < 1
Large Neighborhood TPI: TPI ≥ 1
Local ridges/hills in valleysSmall Neighborhood TPI: TPI ≥ 1
Large Neighborhood TPI: TPI ≤ –1
Midslope ridges, small hills in plainsSmall Neighborhood TPI: TPI ≥ 1
Large Neighborhood TPI: –1 < TPI < 1
Mountain tops, high ridgesSmall Neighborhood TPI: TPI ≥ 1
Large Neighborhood TPI: TPI ≥ 1

3.2 Vegetation cover classification using NDVI analysis

Vegetation cover is an important factor as it has a strong relation to root strength that represents site quality and land use suitability [1]. One of most important vegetation indices is NDVI, which gives a measure of the amount of vegetation in the study area, differentiating vigorous from less vigorous vegetation [1, 14, 15]. In this study, NDVI was computed from a Landsat ETM+ satellite image (May 2010) using the following equation [1618]:

NDVI=(NIRRed)/(NIR+Red)(1)

where Red and NIR stand for the spectral reflectance measurements acquired in the visible (red) and near-infrared regions respectively. NDVI values vary between −1 to +1, with low NDVI values indicating sparse or unhealthy vegetation, and high values indicating greener plants (Table 2). Water typically has an NDVI value less than 0, bare soils between 0 and 0.1, and vegetation over 0.1 [9, 19].

Table 2

Characteristics of NDVI signatures [1, 9].

NDVIDominant cover
< 0.1Water, pond and streams
0.1 to 0.2Bare areas, soil and rock
0.2 to 0.3Shrubs, grassland, agriculture areas and dry forests
0.3 to 0.6Dense vegetation
0.6 to +1.0Verydensevegetationandtropicalrainforest

3.3 Multiple regressions

The relationships between NDVI and different parameters that characterize vegetation, such as leaf area, percentage of plant fraction and plant biomass, have been highlighted by several authors [2022]. For this study, the relationships between NDVI, tree height and landform classes were determined using multiple regressions [21]. The general form of the regression equations is according to Eq. 2 [23]:

Y=A0+A1X1+A2X2+...+bnXn(2)

where Y is the dependent variable, A0 is the intercept, A1. . . bn are regression coefficients, and X1Xn are independent variables referring to basic soil properties.

4 Results and Discussion

4.1 Landform classification

Using TPI, the landform classification map of the study area was generated. The classification has ten classes; high ridges, midslope ridges, upland drainage, upper slopes, open slopes, plains, valleys, local ridges, midslope drainage and streams (Figure 3). The areas of the landform classes are shown in Figure 4. It is observed that the largest landform is streams, while the smallest is plains.

Figure 4 Areas of the landform classes
Figure 4

Areas of the landform classes

By comparing the landform classification and tree height (Figure 5) maps of the study area, it was found that high ridges, midslope ridges, upland drainage, upper slopes, open slopes, plains and valleys consist of vegetation heights of > 20, 14–20, 11–14, 8–11, 4–8, 2–4 and < 2m respectively. Based on this, it can be concluded that ridge landforms have more vegetation than the other landforms. This is as in ridges, the climate is suitable for growth of vegetation [21].

Figure 5 The tree height map for the study area
Figure 5

The tree height map for the study area

4.2 Vegetation cover classification using NDVI analysis

As shown in Figure 6(a), the NDVI values for the study area range between −0.58 and 0.69. According to Table 2, NDVI ranging from 0.1 to 0.2, which represents rock, soil and bare areas, is found at the upper slope, open slope and midslope drainage classes. Flat areas and high ridges have higher NDVI values, ranging from 0.2 to 0.3, covering lower tree heights. Higher NDVI values of more than 0.3 are found concentrated at valleys and streams, where tree height is higher (more than 14 m) [1]. The dominant covers based on the NDVI values for the study area are shown in Figure 6(b). It was found that for the local, midslope and high ridge landforms, NDVI values are higher as compared to the other landforms. Furthermore, it was found that the locations with the highest NDVI has the most vegetation (tree height).

Figure 6 (a) NDVI values obtained for the study area (b) Dominant covers map. Relationship between landform classification and vegetation
Figure 6

(a) NDVI values obtained for the study area (b) Dominant covers map. Relationship between landform classification and vegetation

4.3 Multiple regressions

The calculated simple linear correlation coefficients (r) between landform classes, tree height and NDVI are summarized in Table 3. It was found that there is positive and significant correlations between NDVI and tree height (r = 0.923), and landform and NDVI (r = 0.640). This indicates that landform classification and NDVI can be used to predict tree height.

Table 3

Correlation values (r) showing the relationships between the landform classes and vegetation variables analyzed in this study.

ParametersLandformNDVITree height
Landform1.640[*].408
NDVI1.923[**]
Tree height1

In to Table 4, R2 for prediction of tree height through landform and NDVI is 0.909 in the model 1 that as is shown good correlation model. Standard errors of the regression coefficients, which can be used for hypothesis testing and constructing confidence intervals. The standardized coefficient (B) is what the regression coefficients would be if the model were fitted to standardized data, that is, if from each observation, the sample mean is subtracted. In addition, the t statistic tests the hypothesis that a population regression coefficient β is 0, that is, H0: β = 0. It is the ratio of B to its standard error. Based on this, it was determined that the equation for predict tree height using landform and NDVI is as follows:

Treeheight=  35.117  +  98.040  ×  NDVI  0.209  ×  landform(3)
Table 4

MLR model summary for the tree height prediction.

ModelRR2Adjusted R2
10.935[a]0.9090.883

According to Table 5, the highest standardized coefficients (β) for prediction of variables for tree height prediction using NDVI and landform was obtained in NDVI (98.04).

Table 5

Performance indices and coeflcients of variables for different MLR models for 174 the tree height prediction.

ModelUnstandardized Coeffcientst
BStd. Error
Constant−35.1175.174−6.787
1NDVI98.04012.9657.562
Landform−.209.100−2.096

a. Dependent Variable: tree height

5 Conclusion

The landform classes obtained in the southwest of the Fars province. Landform classifications using TPI show that the landform classification map of the study area was ten classes; high ridges, midslope ridges, upland drainage, upper slopes, open slopes, plains, valleys, local ridges, midslope drainage and streams. From the analysis, NDVI values for the study site ranges between −0.58 and 0.69.

The relationship between landform classification and vegetation was investigated. The results obtained showed that the high ridges, midslope ridges, upland drainage, upper slopes, open slopes, plains and valleys classes consist of tree heights of > 20, 14–20, 11–14, 8–11, 4–8, 2–4 and < 2 m respectively. Ridge landforms (high, midslope and local ridges) were found to have highest tree heights and NDVI values. It was found that there are positive and significant correlations between NDVI and tree height (r = 0.923), and landform and NDVI (r = 0.640). This shows that landform classification and NDVI can be used to predict tree height in the area, with high value of R2 of 0.909 obtained this prediction. Using deep understanding of the surface terrain characteristics could be detected potential and specific constraints of the tree.


+98-917-8020115; Address: Darab, Shiraz university, Iran; Postal Code: 71946-84471

References

1 Ahmad Zawawi A., Shiba M., Janatun Naim Jemali N., Landform Classification for Site Evaluation and Forest Planning: Integration between Scientific Approach and Traditional Concept. Sains Malaysiana. 2014, 43(3), 349–358.Search in Google Scholar

2 Hoersch B., Braun G., Schmidt U., Relation between landform and vegetation in alpine regions of Wallis, Switzerland. A multiscale remote sensing and GIS approach. Computers Environment and Urban Systems, 2003, doi: 10.1016/S0198-9715(01)00039-4.10.1016/S0198-9715(01)00039-4Search in Google Scholar

3 Pfeffer K. E., Pebesma J., Burroug P. A., Mapping alpine vegetation using vegetation observations and topographic attributes. Landscape Ecology 2003, 18, 759–776.10.1023/B:LAND.0000014471.78787.d0Search in Google Scholar

4 Burr D., Baker V. R., Carling P., Megaflooding on earth andmars. Cambridge, UK; New York: Cambridge University Press. 2009.10.1017/CBO9780511635632Search in Google Scholar

5 Chalmers A. C., Erskine W. D., Keene A. F., Bush R.T., Relationship between vegetation, hydrology and fluvial landforms on an unregulated sand-bed stream in the Hunter Valley, Australia. Austral Ecology, 2012, 37(1), 193–203.10.1111/j.1442-9993.2011.02262.xSearch in Google Scholar

6 Diallo O., Diouf A., Hanan N.P., Ndiaye A. Y., Prevost Y., AVHRR monitoring of savana primary production in Senegal, West Africa: 1987-1988. International Journal of Remote Sensing. 1991, 12 (6), 1259–1279.10.1080/01431169108929725Search in Google Scholar

7 Loučková B., Vegetation–landform assemblages along selected rivers in the czech republic, a decade after a 500-year flood event. River Research and Applications. 2012, 28, 1275–1288.10.1002/rra.1519Search in Google Scholar

8 Henrique C. E., Fátima Rossetti D., Zani H., Classification of Vegetation over a Residual Megafan Landform in the Amazonian Lowland Based on Optical and SAR Imagery. Remote Sens. 2014, 6, 10931–10946.10.3390/rs61110931Search in Google Scholar

9 Zawawi A. A., Shiba M., Jemal N. J. N., Landform Classification for Site Evaluation and Forest Planning: Integration between Scientific Approach and Traditional Concept. SainsMalaysiana, 2014, 43(3), 349–358.Search in Google Scholar

10 Soufi M., Morpho-climatic classification of gullies in Fars province, southwest of Iran. Isco 2004 – 13th International Soil Conservation Organisation Conference – Brisbane, 2004.Search in Google Scholar

11 Weiss A. D., Topographic Position and Landforms Analysis. Ecoregional Data Management Team the Nature Conservancy, 2005, 343–4345.Search in Google Scholar

12 Jenness J., Topographic Position Index (tpi_jen.avx) extension for ArcView 3.x”, v. 1.3a. Jenness Enterprises, 2010, http://www.jennessent.com/arcview/tpi.htm.Search in Google Scholar

13 Tagil S., Jenness J., GIS- based automated landform classification and topographic, Landcover, and geologic attributes of landforms around the Yazoren Polje, Turkey. Journal of Applied Sciences, 2008, 8(6), 910–921.10.3923/jas.2008.910.921Search in Google Scholar

14 Miguel A., Alloza G. J. A., Mayor A. J., Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region. Central European Journal of Geosciences. 2014, 6(1), 112–120.Search in Google Scholar

15 Rouse J. W., Haas R. H., Schell J. A., Deering D. W., Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satelite-1 Symposium, 1973, 309–317.Search in Google Scholar

16 Jackson R. D., Slater P. N., Pinter P. J., Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sensing Environ, 1983, 13, 187–208.10.1016/0034-4257(83)90039-1Search in Google Scholar

17 Silveira C. M. S., Tchepel O. A., Influence of the spatial resolution of satellite-derived vegetation parameters on the biogenic Volatile Organic Compounds (VOC) emission modeling. Central European Journal of Geosciences, 2014, 6(1), 104–111.10.2478/s13533-012-0166-zSearch in Google Scholar

18 Tucker C. J., Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, 1979, 8(2), 127–150.10.1016/0034-4257(79)90013-0Search in Google Scholar

19 Calera A., Martínez C., Melia J., A procedure for obtaining green plant cover: Relation to NDVI in a case study for barley. International Journal of Remote Sensing, 2001, 22 (17), 3357–3362.10.1080/01431160010020100Search in Google Scholar

20 Carlson T. N. Y., Ripley D. A., On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment. 1997, 62, 241–252.10.1016/S0034-4257(97)00104-1Search in Google Scholar

21 Franklin S. E., Hall R. J., Moskal L. M., Maudie A. J., Lavigne M. B., Incorporating texture into classification of forest species composition from airborne multispectral images. International Journal of Remote Sensing, 2000, 21, 61–79.10.1080/014311600210993Search in Google Scholar

22 Wylie B. K., Meyer D. J., Tieszen L. L. Y., Mannel S., Satellitemapping of surface biophysical paameters at the biome scale over the North American grassland. A case study. Remote Sensing of Environment, 2002, 79, 266–278.10.1016/S0034-4257(01)00278-4Search in Google Scholar

23 Gunst R. F., Mason R. L., Regression Analysis and Its Application. Marcel Dekker Inc., New York, 1980.Search in Google Scholar

Received: 2015-3-29
Accepted: 2015-11-2
Published Online: 2016-5-24
Published in Print: 2016-5-1

© 2016 M. Mokarram and D. Sathyamoorthy, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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