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ECOTERRA - Journal of Environmental Research and Protection

www.ecoterra-online.ro 2015, Volume 12, Issue 3

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Domogled – Valea Cernei National Park monitoring using satellite technology 1Mihai V. Herbei, 2Roxana C. Herbei, 1Cosmin A. Popescu, 1Radu Bertici

1 Faculty of Agriculture, Banat University of Agricultural Sciences and Veterinary Medicine from Timisoara, Timisoara, Romania; 2 Faculty of Mine, University of Petrosani, Petrosani,

Romania. Corresponding author: M. V. Herbei, [email protected]

Abstract. This research studied the Domogled Valea Cernei National Park, Romania, based on Landsat 8 system facilities, supported by specialized software and information technology growing capacity data processing and analysis in the context of the need for land areas by providing real-time data and information relating state or vegetation dynamics. In the present study there were calculated several vegetation indices (VI) on the studied area: Normalized Difference Vegetation Index - NDVI, Normalized Difference Water Index - NDWI, Normalized Difference Moisture Index - NDMI, Normalized Difference Ratio Burning - NDBR. Key Words: vegetation index, Landsat, band math, remote sensing. Aims and backround. The purpose of this study is to present a monitoring way of Domogled Cerna Valley based Landsat 8 satellite images taken from the portal http://landsat.usgs.gov, and to process these images, depending on the spectral bands for the calculation of vegetation indices useful in the management of the studied area. The approach of such monitoring has been studied and applied in practice in various areas using satellite images from various satellite systems, such as Modis, Ikonos, Landsat, Aster, Spot, etc. (Bilgili et al 2014). Monitoring the national parks shows an important objective for each public administration at central and local level, and this can be done using satellite images with high resolution that can cover large areas of interest. Satellite images, integrated into a GIS system and allow interpretation of the studied areas based on spatial analysis functions integrated in specialized software to calculate indices of vegetation (Dimen et al 2013; Herbei & Nemes 2012). Experimental. Established in 1990, Domogled Cerna Valley National Park is located in south-western Romania and covers the area of three districts, namely: Caras-Severin, Mehedinti and Gorj. Domogled Cerna Valley is located in the east of Caras-Severin, occupying the area of 23 185 ha and occupying County West County area of 8220 ha and the western county of Gorj with a surface of 29,806 ha. Geographically Domogled Cerna Valley National Park stretches over Cerna River basin (Figure 1), from the source to the confluence with the river Belareca over Godeanu and Cerna Mountains on the right side, respectively Valcan and County Mountains on the left side. Domogled Cerna Valley National Park is of 61,211 ha and lies between the northern latitudes 44º50'10" and respectively 45°16'50" and eastern longitude of 22º23'50" and respectively 22º51'5". For this study it has been used a Landsat 8 satellite image from 2014 which includes Domogled - Cerna Valley National Park - Romania, and that allowed a monitoring using various types of analysis GIS (Oncia et al 2013). Each Landsat 8 satellite images are taken from eight international database Earth Explorer shows a set of standard parameters to properties of the image acquired, useful in the GIS analysis (Herbei 2013): type and form of the image (Level 1T - terrain corrected, GeoTIFF) resolution (15 meters / 30 meters / 100 meters, panchromatic / multispectral / thermal) map projection (UTM - Polar Stereographic for Antarctica), geodetic datum (WGS 84 North-up map; Cubic convolution), precision (OLI 12 meters circular error 90-percent confidence; TIRS: 41 meters circular error 90-percent Confidence) etc. In the present study there were calculated several vegetation indices (VI) on the studied area: Normalized Difference Vegetation Index - NDVI, Normalized Difference Water Index - NDWI, Normalized Difference Moisture Index - NDMI, Normalized Difference Ratio Burning - NDBR. Normalized indices of differentiation are expressed in



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the form of new images in greyscale result of various arithmetic operations between certain spectral bands. Normalized indices of differentiation are based on its signature amplification corresponding spectral bands a particular element has the highest reflectance or decrease its signature that object in the band where the item has the lowest reflectance. Pixels that compose new image of floating type have the values between -1 and +1 (Mihai 2007).

Figure 1. Geographical Location Domogled Cerna Valley. Results and Discussion. The results of this study are the maps of some vegetation indices, calculated with formulas known from specialty literature (Mihai 2007, 2009). Normalized Difference Vegetation Index (NDVI). Is one of the best known and most used index in the world. NDVI is an index that provides a standardized method of comparing the "greenery" vegetation between satellite images. It uses near infrared and visible red. NDVI index was introduced in the literature by Dr. John Rouse in 1973. The values of NDVI vary with the chlorophyll absorption of radiation in the spectral reflectance of its red and near infrared spectral region. These values are between -1 and +1 (Figure 2) expressing the green vegetation composition. The closer to +1 (light colours) represent thick vegetation and are specific to the dense deciduous forests. Values close to -1 (dark tones) are devoid of vegetation land with soil or rock. It is useful in areas with vegetation mapping, vegetation typology, health of vegetation, land use patterns, etc. It is calculated by formula (1):

NDVI NIR R / NIR R B5 B4 / B5 B4 (1)

Normalized Difference Water Index (NDWI). Was introduced in literature in 1996 by McFeeters. The NDWI index (Figure 3) is useful in mapping water elements, views and content differences in plant water turbidity, alluvial soils or where differentiation water content of the vegetation. It uses the green spectral bands (electromagnetic radiation penetrates water) and near infrared (spectral response increases moisture in soils and plants rocks and the water begins to absorb radiation in surface layers). Shadows (values close to -1) express the water surface. Open tones (close to 1) express dry land. Mid-tones (close to 0) intermediate moisture content plots (Figure 5). It is calculated by formula (2):

NDWI NIR G / NIR G B5 B3 / B5 B3 (2)



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Figure 2. NDVI Map.

Figure 3. NDWI Map.

Normalized Differentiation Moisture Index of soil and vegetation (NDMI). May be built as follows: light tones (excess moisture), dark tones (low humidity) (Figure 4). It expresses the differentiated moisture content in the landscape elements and particularly of soils, rocks and vegetation (good indicator of the occurrence of droughts). The values greater than 0.1 symbolized by light tones express a high humidity. The low values (close to -1) indicated by dark tones express a low humidity. Hydra is useful in mapping potential. It is calculated by formula (3): NDMI NIR IR / NIR IR B5 B6 / B5 B6 (3)

Normalized Differential Burning Ratio for the vegetation (NDBR). Was first applied by C. Key and B. Nate 1999. It uses the answer spectral bands of unburned vegetation (near infrared) and fire (medium infrared) are the strongest. Reflectance differences signify the presence of chlorophyll, before firing or lack thereof after arson. Open tones (values above 0.1) symbolize the land with a high risk of catching fire (forests, dry scrub) (Figure 5). Shadows symbolize the land without taking fire risk (stone or concrete buildings, highways and roads, railways, etc.). It is calculated by formula (4):



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NDBR NIR MIR / NIR MIR B5 B7 / B5 B7 (4)

Figure 4. NDMI Map.

Figure 5. NDBR Map.

The results were analyzed by GIS (Rădulescu & Rădulescu 2013) tools being made mathematical correlations between elements previously calculated. Finally, was analyzed the relationship of NDVI index with the NDMI, NDBR and NDWI indexes. For the evaluation of the level of correlation there have been used adequate mathematical functions (polynomial) and extracted values of the correlation coefficient R2 which represent the level of the correlation and the accuracy of the results. The results show statistical certainty for all the relations of NDVI index with NDMI, NDBR and NDWI indexes (Figure 6).



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Figure 6. Variation of NDVI according to NDBR (R2 = 0.89687), NDMI (R2 = 0.76947),

NDWI (R2 = 0.9756). Conclusions. In this study there were made several maps regarding a series of vegetation indexes over the area Domogled Cerna Valley National Park, maps that can form the basis for monitoring these areas. The purpose of this vegetation indexes calculation is to accentuate some features of the environment using two spectral bands, where the subject receives opposite responses. In this paper there have been determined: Normalized Difference Vegetation Index – NDVI, Normalized Difference Water Index – NDWI, Normalized Difference Snow Index – NDSI, Normalized Difference Moisture Index – NDMI, Normalized Difference Burning Ratio – NDBR, Normalized Difference Building Index – NDBI. The relations of NDVI index with other indexes that represent the status of vegetation and land cover were assessed through polynomials functions with high reliability results. Acknowledgements. This paper was published under the frame of USAMVBT Research Programme “Local Competition of Research Projects”. References Bilgili B. C., Satir O., Muftuoglu V., Ozyavuz M., 2014 A simplified method for the

determination and monitoring of green areas in urban parks using multispectral vegetation indices. Journal of Environmental Protection and Ecology 15(3):1059-1065.

Dimen L., Borsan T., Bratan C. D., 2013 Using GIS technology for soil erosion analysis. A case study: the hydrographical basin of ‘Buturoi Valley’, Jidvei, the Alba County. Journal of Environmental Protection and Ecology 14(4):1811-1818.

Herbei M. V., 2013 [Sisteme informatice geografice. Aplicatii]. Universitas Press, Petrosani [in Romanian].

Herbei M. V., Nemes I., 2012 Using GIS analysis in transportation network. 12th International Multidisciplinary Scientific GeoConference SGEM 2012, Vol. 2, pp. 1193-1200.



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Mihai B., 2007 [Teledetecţie, Vol 1. Introducere în procesarea digitală a imaginilor]. University Press Bucharest [in Romanian].

Mihai B., 2009 [Teledetecţie, Vol 2. Noţiuni şi principii fundamentale]. University Press Bucharest [in Romanian].

Oncia S., Herbei M. V., Popescu C. A., 2013 Sustainable development of the Petrosani city, the Hunedoara county based on GIS analysis. Journal of Environmental Protection and Ecology 14(1):232-239.

Rădulescu V. M., Rădulescu C., 2013 GDB GIS, an informatic system of the management of prospecting and geological research activities, the component of the digital Mine. Recent Researches in Applied Economics and Management 2:288-293.

*** http://landsat.usgs.gov.

Received: 16 July 2015. Accepted: 20 September 2015. Published online: 31 October 2015. Authors: Mihai Valentin Herbei, Faculty of Agriculture, Banat University of Agricultural Sciences and Veterinary Medicine from Timisoara, 119 Calea Aradului Street, Timisoara, Romania, e-mail: [email protected] Roxana Claudia Herbei, Faculty of Mine, University of Petrosani, 20 University Street, Petrosani, Romania Cosmin Alin Popescu, Faculty of Agriculture, Banat University of Agricultural Sciences and Veterinary Medicine from Timisoara, 119 Calea Aradului Street, Timisoara, Romania Radu Bertici, Faculty of Agriculture, Banat University of Agricultural Sciences and Veterinary Medicine from Timisoara, 119 Calea Aradului Street, Timisoara, Romania This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. How to cite this article: Herbei M. V., Herbei R. C., Popescu C. A., Bertici R., 2015 Domogled – Valea Cernei National Park monitoring using satellite technology. Ecoterra 12(3):73-78.

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