Abstract

Climatic factors are the most important factor for vegetation changes on the earth's surface. The objective of the study is the application of modern scientific tools such as remote sensing to analyze the vegetation changes over a year concerning climatic changes (2005-2020). The Landsat 5, 7 and 8 satellite image was utilized which was obtained from the Google earth engine for studying vegetation changes. The main type of vegetation changes studied in the study area is high vegetation, moderate vegetation, low vegetation, and barren land. Earth engine was used for NDVI classification of the study area in periods of 2005-2020 and collected rainfall data of respective years from the meteorological lab. An ArcGIS software tool was used to acquire the shapefile from the Survey of India Topo sheet. Correlation analysis between the rainfall and vegetation changes will help to conserve nature and reclamation activities in the study area.

1. Introduction

Detection of forest cover changes is essential for planning and implementing a scientific-based solution for the management of natural resources. This provides information for managing land use and meeting the demands of the human population ¹. Maps and statistical data from the land use land cover are very important for planning, management, and utilization of the land ². Land cover changes can be obtained from processed aerial photographs, Satellite images, and Google Earth ³. Traditional remote sensing approaches require grouping spectral signatures and subsequent accurate discrimination between groups such as land cover types ⁴. Land cover maps are produced by using supervised classification on remote sensing images with sufficient ground truth data. ⁵.

Acquiring ground truth data enough for getting accurate land use and land cover maps is both time-consuming and expensive in practice, especially for the large study area. Google earth's resolution data are useful as a platform for validating datasets used previously with land cover ⁶. Google Earth is a free easy-to-use program owned by Google Inc. Google earth has a potential tool for wider use in scientific study, especially in LULC analysis ⁷. For small study areas in recent times, ground truth data can be collected by drawing sample regions with high-resolution images as reference manually ⁸ or by recording and labeling data during field trips. In this study, we utilized the google earth engine as a platform and used archives of geospatial datasets provided by the google cloud platform for correlating climate factors with vegetation changes. Normalized difference vegetation index (NDVI) is the most popular and most used index especially covering investigation on a global scale ⁹. Forests provide such as wildlife habitats, and intangible benefits such as water resources, air, and livelihood opportunities to tribal and forest dwellers.

The drastic reduction of forest cover or forced degradation of the forest regions mainly due to the change of climate the major factor of global warming. Forest Change detection is the process of identifying changes in forest areas, the density of observing the satellite images at different periods. Climate factors affect the local ecosystem and produce ecosystem changes. Thus, a study focusing on vegetation changes respect with to surface temperatures and rainfall in the Vaigai reserve forest, Tamil Nadu. Rainfall and air temperature measurements from weather stations were conventionally considered the most accurate and reliable source of data in previous. This article correlates the relationship between the forest cover changes data obtain from the google earth engine surface from 2005 to 2020 and the climatic factor of average monthly rainfall.

2. Study Area

This study area is located in the Vaigai reserve forest, Tamil Nadu Forestry Training College (TNFTC) campus, Theni district, Tamil Nadu. The Tamil Nadu Forestry Training College lies in the Theni district of Andipatti Taluk, just two kilometers away from the Vaigai reservoir (Figure 1). Vaigai Dam is the lifeline of Madurai, Dindigul and Theni districts and it is a man-made dam running for more than 2 km to store the water derived from the catchment area adjoining the Palani and Kodaikanal hills. The study area is 201.42 ha, situated between 10.0320° and 10.0382° North Latitude and 77.584° and 77.597° East Longitude and the status of the land is Reserve Forest from 1989. During the year 1989, Vaigai Campus Forest Block in erstwhile Madurai District was declared a Reserve Forest by the Governor in exercising the power conferred by section 16 of Tamil Nadu Forest Act, (Ref. Letter No. II-(2)/EFR/5684/88) and came into effect on and from 26th April 1989. This study was carried out to investigate vegetation changes with the impact of climatic factors, especially rainfall. Comparison of classified images of vegetation map of 2005 to 2020 which is giving the vegetation changes in the study area. Analyzing the rainfall data of the study area from the metrological lab to understanding the climatic variations and rainfall distribution changes.

3. Material and Methods

The base map was prepared from the survey of India topographical sheets 58F/12/SW in 1:25000 scale. A layered map was extracted with the use of the topo sheet in Arc GIS. A GPS hand device was used and verified the ground station with the layer map. To make a change analysis study area, the earth engine was used for NDVI classification of the study area in periods of 2005-2020 and collected rainfall data of respective years from the meteorological lab. Arc GIS software tool was used to acquire a shapefile of the study area from the topo sheet by using the digitization tool. This shapefile data was used for the vegetation index study.

The study area lies on the leeward (rain shadow) side of the Western Ghats, hence, it is receiving less precipitation than the other side of the Western Ghats. The rainfall data taken for the last 50 years showed a rainfall range between 250 mm and 1000 mm (Source: Meteorological Lab, Vaigai dam, Andipatti, Theni). The study area is receiving rainfall during the month of the North-East monsoon. The soil present in the study area is mainly clay loam to sandy clay loam having high to medium calcium carbonate content. The study area, Vaigai reserve forest is roughly 201.42 hectares in size and the forest type is Carnatic umbrella thorn forests which consist of Acacia species mainly Acacia leucophloea, A .nilotica and other species such as Ailanthus excelsa, Albizia amara, Albizia lebbeck, Azadirachta indica, Tamarindus indica, Bambusa bambos, Syzygium cumini, Prosopis julifera, and Lantana camera.

We used a combination of Google Earth Engine open source code for Landsat 8 and ArcGIS to generate Normalized difference vegetation index analysis from satellite imagery and conduct time-series analyses on the multi-year composites ^{10, 11, 12}. Online JavaScript Integrated Development Environment in Google Earth Engine is used for generating NDVI. These NDVI images were composited into the mosaics pattern and the median value of each pixel was taken ¹³.

All the data was downloaded from each time series chart and merged with the study area. Normalized Difference Vegetation Index (NDVI) data from satellite images for the study areas were extracted from Google Earth Engine Code Editor Scripts. Spectral reflectance of the surface area was used to detect the changes in the vegetation. The custom scripts were developed with elements from official Google resources. Image collections with sensors were used for NDVI calculations. Time-series graphs were created using the Arc GIS classified image. ArcGIS is used to reproject the data from the Google earth engine. Monthly rainfall data for the period 2005 to 2020 was acquired from the metrological lab, Vaigai reservoir. Mean annual rainfall as compared with vegetation change of the study area.

4. Result and Discussion

Results of NDVI analysis using the Google earth engine are presented and discussed here. Normalized Differential Vegetation index analysis colour reflection of pixels used for differentiates the vegetation cover of the study area. NDVI ranges were used to classify the vegetation cover (Table 1). The classified NDVI range from 1 to 0 ranges was calculated based on the vegetation cover. The range below 0.3 indicates barren land, the range between 0.3 to 0.5 indicates low vegetation, the range between 0.5 to 0.7 indicates moderate vegetation and the range above 0.7 indicates high vegetation in the study area. A multi-temporal image of the period of 15 years was taken for change detection with a rainfall variant from 2005 to 2020. After the acquired classified image of the study area, the result of colour reflation pixels indicated the area of change. The area of each class acquired from the attribute table, calculated in acres.

Satellite imagery elements were compared with the field study and classified based on the vegetation present in the study area. The data was retrieved from the Google earth engine from January of 2005 to2020 quinquennial basis. Visual image analysis clearly showed there is a change in the vegetation from January 2005 to January 2020. Mean annual rainfall data of the study period was also analyzed and compared with Landsat 8 (OLI/TIRS) data using the Google earth engine.

The NDVI result shown the high vegetation was slightly decreased over a year

The study area has high vegetation, which slightly decreased over a year from 2005 to 2020 from 46.87 hectares to 40.27 acres, moderate vegetation slightly increased from 67.68 to 70.78 hectares, low vegetation also increased from 26.29 to 26.52 and the area of barren land increased slightly from 26.29 to 26.52 hectares.

Fifteen-year period the percentage of vegetation changed from around 3 to 0.23 percent, in this high vegetation area reduced from 23.13 percent to 19.87 percent, moderate and low vegetation drastically increased by around 1.5 percent and barren land increased by 0.12 percent (Table 3).

The present study analyzed the correlation between vegetation changes and average rain rainfall. The analysis in MS excel between the climatic factor of rainfall and the vegetation showed a Significant Correlation. (Table 4). Higher annual rainfall was increasing the dense vegetation and lesser rainfall resulted from moderate and low vegetation growth in the study area. Correlation analysis reveals a positive relationship between dense vegetation and annual rainfall.

A strong Correlation (0.86) was found between rainfall and the dense vegetation from 2005 to 2020. A negative correlation was found between lower rainfall and lower vegetation and Barren land.

5. Conclusion

Rainfall is one of the most important factors for vegetation growth which was justified by the correlation studies with rainfall and NDVI analysis. In this study, we found Google earth engine is a powerful tool to study vegetation changes. The main type of vegetation changes studied in the study area is high vegetation, moderate vegetation, low vegetation and barren land. It was observed that vegetation has changed remarkably from the period 2005-2020. This decrease in vegetation has been a result of rainfall changes in the study area. Therefore, further analysis is required to study other climatic factors such as temperature wind and also anthropogenic factors for forest cover change detection.

References