Using Geographical Information System (GIS) and Satellite Remote Sensing for Understanding ...

International Journal of Remote Sensing Applications Volume 3 Issue 4, December 2013 doi: 10.14355/ijrsa.2013.0304.01

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Using Geographical Information System (GIS) and Satellite Remote Sensing for Understanding the Impacts of Land Cover on Apiculture over Time Hossam F. Abou-Shaara1,2 1

Plant protection Department, Faculty of Agriculture, Damanhour University, Egypt

Plant Protection Department, College of Food and Agriculture Sciences, King Saud University Saudi Arabia; P.O.Box. 2460, Riyadh 11451, Saudi Arabia 2

entomology_20802000@yahoo.com & haboushaara@ksu.edu.sa Abstract The development of apiculture is strongly related to the suitable land cover, mainly the availability of honey plants.

The land cover is not stable over time due to the human activities. The honey yield per honey bee colony has been reduced greatly in El-Behera Governorate, Egypt over time.

The decline in the cultivated areas is considered to be the main cause for the low honey yield. In this research, the land cover changes were investigated during a long period of time from 1970s till 2000s to understand the possible roles of the land cover changes on apiculture and low honey yield.

The GIS and Landsat satellite images in combination with Google Earth historical images were used for land cover change analysis. The obtained results showed a relative increase in the cultivated areas over time with approximately the presence of the same plant types.

Therefore, the low honey production can not be explained by the decline in the cultivated areas.

Keywords Honey Bees; GIS; Remote Sensing; Beekeeping

Introduction The Geographical Information System (GIS), a computer program for analyzing and mapping geographical datasets, has been used to improve beekeeping in different parts of the world. In Philippines, Estoque and Murayama (2010) have employed the GIS to identify the suitable sites for apiaries. In Iran, the GIS has been employed to identify the suitable rangelands for honey bees (Amiri et al., 2011 & Amiri and Shariff, 2012). In Saudi Arabia, Abou-Shaara et al. (2013) have identified the suitable regions for beekeeping under the conditions of the very low relative humidity and extreme temperatures.

The use of Earth’s surface images taken by satellites or

aircraft to identify earth features and their properties is known as the Remote Sensing (RS).

The possible uses and techniques of the RS were reviewed intensively by Navalgund et al. (2007). The remote sensing by using landsat images and GIS has been used for the identification of the suitable honey plant areas for honey bee colonies in Young-Chon city (Myung-Hee et al., 2001). Odindi et al. (2012) have studied land cover changes over time in Port Elizabeth, South Africa by using remote sensing. So far, relatively few studies have been conducted on apiculture by using GIS and RS. Beekeeping is a very old practice in Egypt. The Northern parts of Egypt have the highest beekeeping activity and honey production (about 60% of the total honey production of Egypt) according to Hussein (2001). The mean honey production per colony in Egypt is about 7.3 kg during 2000s (Hussein, 2001) while that has been reported to be more than 10 kg during 1970s till 1990s in El-Behera governorate which is located in the northern parts of Egypt and has high beekeeping activity. Most of the beekeepers at ElBehera governorate have thought that the decline of the cultivated areas is the main reason for the low honey production per colony during the 2000s. To test this hypothesis, the GIS, RS and historical Google Earth images were employed for the acquisition of information about land cover changes through time from 1970s till 2000s. In the light of the obtained results, the possible reasons for the low honey production were subsequently concluded.

Materials and Methods The studied region, El-Behera governorate, located in 171

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International Journal of Remote Sensing Applications Volume 3 Issue 4, December 2013

the north of Egypt with total area of 9826 km2 (Fig. 1) between 30° 36′ 36″ N and 30° 25′ 48″ E. is the largest agricultural region in Egypt and characterized by high beekeeping activity.

FIG. 1 THE STUDY LOCATION (EL-BEHERA GOVERNORATE)

To follow the historical changes of the land cover, images from Google Earth at different dates; August 1970, July 2000 and July 2013 were used. The ArcGIS 10 was employed to create two polygons covering the used areas of the governorate either for agriculture or other activities. Due to the low quality of the old images the detailed classification of the images was too hard. The areas of the created polygons were calculated and subsequently converted into percentages by dividing the polygon areas by the total governorate area.

Satellite Images Analysis

The ArcGIS 10 was used for the images band composition and data analysis. The images were firstly classified by using Iso cluster unsupervised classification followed by supervised classification according to land cover shape into four main categories (water, plants, artificial and desert). The artificial category includes buildings, factories, the solid lands, etc. To identify the relative biomass of vegetative areas, the Normalized Difference Vegetation Index (NDVI) was calculated by using the ArcGIS. Typically, the ArcGIS uses the equation [NDVI = ((IR - R)/(IR + R)) X 100 + 100] to generate a colormap with a ramp range from 0 to 200. The generated maps during different times were subsequently compared.

To create plant maps, the images were reclassified by considering all land cover categories as NoData except plants. Then, the plant raster datasets were converted into polygons and the plant areas were calculated and compared. Also, a square covering the noticeable landuse activity was added to all the classified images and was zoomed in to allow the visual discrimination between images. The results of the Google Earth images and satellite images were compared and the change of the landuse in relation to apiculture activity was discussed. Fig. 2 shows the steps of the imagery analysis to follow land cover changes over time.

Landsat images

Google earth images

ArcGIS

Polygons creation

Bands composition

Images classification Maps comparison

Three categories of Landsat images were used; 1) Landsat with Enhanced Thematic Mapper (ETM+) taken during 2000's, 2) Landsat with Thematic Mapper (TM) taken during 1990's, and 3) Landsat with Multi Spectral Scanner (MSS) taken during 1970's. These images are multi-spectral bands with 30-meter resolution.

(A)

(B)

Calculation of landuse (%) Maps comparison

Images reclassification

Plant maps and areas Results comparison and Discussion

FIG. 2 STEPS OF LAND COVER CHANGE ANALYSIS BY USING GIS AND SATELLITE REMOTE SENSING

(C)

FIG. 3 THREE CHRONOLOGICAL MAPS FOR THE STUDY REGION FROM GOOGLE EARTH (A: 8/1972, B: 7/2000 AND C: 7/2013) WITH MARKING THE USED LAND FOR HUMAN ACTIVITY BY POLYGONS.

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measured total plant areas were 2722.4, 3433.7 and 4128.0 km² for 1970's, 1990's and 2000's, respectively.

This result is in line with the result obtained from Google Earth historical maps.

Results and Discussion The created polygons covering the landuse activity (the upper polygon covers the heavy landuse wile the lower polygon covers the less landuse activity) showed the gradual increase in the landuse over time from August 1972 till July 2013 (Fig. 3). The percentages of the used land for any human activity (e.g. agriculture, buildings, etc) were 58.92, 60.54 and 68.17% for 1972, 2000 and 2013, respectively.

The satellite images taken at different times (during 1970's, 1990's and 2000's) confirmed the gradual increase of landuse over time. As shown in Fig. 4 and Fig. 5, it is clear that the most increase in landuse was for buildings rather than agriculture. However, a noticeable increase in agricultural activity is detected over time from 1970's till 2000's. The increase in landuse and agricultural activity, in general, is very slow. The Normalized Difference Vegetation Index (NDVI) for the satellite images over time reflects the increase of agricultural activity over time especially at the northern and middle regions (Fig. 6). The

(A)

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FIG. 4 A SQUARE COVERING THE MOST DEVELOPED AREAS. THIS SQUARE ARE USED FOR COMPARING THE MAPS AS IN FIG.5.

(B)

(C)

FIG. 5 A SELECTED SQUARE FOR MONITORING THE LANDUSE ACTIVITY OVER TIME. (A: DURING 1970'S, B: DURING 1990'S AND C: DURING 2000'S).

(A)

(B)

(C)

FIG. 6 THE NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) FOR 1970'S(A), 1990'S (B) AND 2000'S (C). THE DARK GREEN AREAS REFLECT THE VEGETATION.

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(A)

(B)

(C)

FIG. 7 PLANT TYPE MAPS OVER TIME (A: FOR 1970'S, B: FOR 1990'S AND C: FOR 2000'S).

The plant type maps (Fig. 7) show the fluctuation of plant types distribution over time. However, the increase of the total cultivated areas during 2000's may ensure the availability of all plant types. It is too hard to identify exactly the nature of the plant types of the used old images. The hypothesis of the decline in the cultivated areas as the main cause of the low honey production nowadays at El-Behera governorate is not true. Also, the hypothesis of the change of the cultivated plant types as a possible cause for low honey production is not acceptable based on the obtained results.

Conclusions The used maps either from Google Earth or satellite images succeeded in monitoring the changes of the landuse activity over time. The low honey production in El-Behera governorate can not be explained by the decline in the cultivated areas. The increase of apiaries number and bee colonies number in approximately the same cultivated area since 1970's is most likely the reason for the low honey production nowadays. The described steps in this research could be used successfully elsewhere for monitoring of historical changes.

Suitability Map for Keeping Honey Bees under Harsh Environmental

Conditions

Using

Geographical

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African Journal of Agricultural Research, 7(2012):89-97.

Amiri, F.; A.B.M. Shariff and S. Arekhi. “An Approach for Rangeland Suitability Analysis to Apiculture Planning in Gharah Aghach Region, Isfahan-Iran”. World Applied Sciences Journal, 12 (2011): 962-972.

Estoque, R.C. and Y. Murayama. “Suitability analysis for beekeeping sites in La Union, Philippines, Using GIS and multi-criteria evaluation techniques”. Research Journal of Applie Sciences, 5(2010): 242-253.

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Myung-Hee, J.; K. Joon-Bum and B. Seong-Baek. “Selection technique for honey Plant complex area using landsat image and GIS”. The 22nd Asian Conference on Remote Sensing, (2001) 5- 9 November, Singapore.

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ACKNOWLEDGEMENT

sensing applications: An overvie”. Journal of Current

I would like to thank the Deanship of Scientific Research and College of Food and Agriculture Science Research Center, KSU for proving the necessary research materials.

Science, 93(2007):1747-1766.

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