Assessment of spatial and temporal change in the Yellapur forest division, Uttara Kannada district, Karnataka, using a remote sensing approach

Introduction

Forests are essential ecosystems that play a key role in maintaining ecological balance. They provide habitats for a wide variety of plant and animal species, help regulate the climate by absorbing carbon and contribute to the water cycle through transpiration. Forests are classified into types like tropical, temperate and boreal, depending on their location and climate. In addition to offering resources such as timber, fuel and medicinal plants, they also support the livelihoods of many communities. However, deforestation and degradation continue to threaten biodiversity and reduce carbon storage. Remote sensing, as a discipline, plays a pivotal role in our understanding of the Earth's complex systems and processes. It encompasses the science and technology of acquiring information about objects, areas, or phenomena without direct physical contact (Lillesand et al., 2015) [13] . In the context of forestry, remote sensing techniques are indispensable for gathering data on biophysical parameters crucial for effective forest management. By combining information obtained through forest inventory methods with remote sensing data, researchers and practitioners can gain comprehensive insights into various aspects of forest ecosystems. Land, a finite and vital resource for the economic advancement of nations, faces increasing strain due to population growth. The dynamic condition of land requires immediate attention. Understanding both land use and land cover is paramount for grasping the connection between human actions and the environment, crucial for preventing forest degradation (Dimyati et al., 1996) [7] . Change detection involves identifying differences in the state of an object or phenomenon over time and is essential for understanding the dynamic relationship between human societies and nature. It's important to note that alterations in land use/land cover patterns do not always signify negative changes, such as forest loss due to deforestation. They can also indicate positive changes, like forest expansion, increased water bodies, or expanded plantations (Panigrahy et al., 2010) [19] . Changes in classification may also involve instances where forest cover is not detected due to factors like cloud cover, reduced reflectance caused by leaf fall, or poor image quality. The spectral reflectance from vegetation offers valuable information about the structure and composition of forest stands. Tree volume refers to the amount of wood present in an individual tree, while stand volume refers to the total amount of wood present in the entire forest stand (Lehtonen et al., 2004) [12] . Advancements in remote sensing technologies, particularly through the use of high-resolution satellite imagery, have significantly expanded the capabilities for monitoring forest ecosystems. These technologies provide accurate and detailed measurements of key forest attributes such as canopy cover, vegetation density, tree species distribution, and biomass. The ability to track these changes, whether related to deforestation, forest degradation, or recovery, is crucial for improving our understanding of forest dynamics and trends.

Materials and Methodology Study area

The present study was focused on various forest types within the Yellapur Forest Division in Uttara Kannada District. Located in the eastern part of the district, the Yellapur Forest Division covers a total area of 168,986.66 hectares, including Betta lands (protected forests), which represents 86.97% of the division's geographical area (1,902.28 km²). The division is bounded by longitudes 74°25' to 75°6' East and latitudes 14°44' to 15°7' North. The study area is depicted in Figure 1. The Yellapur Forest Division encompasses three distinct forest types viz dry deciduous, moist deciduous and semi-evergreen.

Fig 1: Study Area map

Experimental Details

For conducting the experiment, satellite images of the Yellapur area for different years were used. The ground truth data was collected based on the forest density classes. Forest inventory was done for biomass and carbon studies. The experiment are as follows (Fig. 2).

Shapefile of Yellapur forest division

A shapefile of the Yellapur forest division includes dry deciduous, moist deciduous and semi-evergreen. It is used to represent geographical features, such as points, lines and polygons, along with their associated attributes. A shapefile is a collection of several files with different extensions, where the primary file stores the geometric data, while other associated files store spatial indices, attribute data and projection information.

Satellite data

Satellite imagery is collected from Landsat 7 for the years 2000 and 2010 (Tables 1). and Landsat 8 for 2023 (Tables 2). Landsat is a series of Earth-observing satellite missions jointly managed by NASA and the U.S. Geological Survey (USGS). The data provides multispectral images of the Earth's surface. These images serve as the primary data source for analyzing changes in land cover over the selected years.

Built-up: areas including cities and towns.

Water Bodies: Rivers, lakes and other water features.

Agriculture land: The land under cultivation.

Preprocessing

Preprocessing ensures that the satellite data is accurate, consistent and ready for further analysis. Preprocessing involves various steps to prepare the raw satellite data for analysis. These steps include:

Radiometric Correction: Adjusts the sensor's recorded values to true reflectance values, correcting for sensor noise and inconsistencies.

Geometric Correction: Ensures that the satellite images align correctly with geographical coordinates. This involves correcting for distortions due to the Earth's curvature, satellite motion and sensor angle.

Cloud Removal: Clouds and their shadows can obscure land cover features, so they are identified and masked out from the images.

Image Enhancement: Techniques like contrast stretching, edge enhancement and filtering are applied to improve the visibility of features of interest.

Calculation of vegetation index (NDVI)

Normalized Difference Vegetation Index (NDVI) is used to quantify vegetation health and cover. NDVI is calculated using the red and near-infrared bands of the spectrum of multispectral sensor of Sentinel-2. The Normalized Difference Vegetation Index (NDVI) is a widely used vegetation index that is calculated using the red and near-infrared (NIR) bands of satellite imagery. The formula for NDVI is:

Supervised classification

Using the ground truth and training inputs, a supervised classification algorithm (Maximum Likelihood) is trained to recognize and categorize different land cover types. The algorithm assigns each pixel in the satellite image to a specific land cover class based on the patterns and characteristics learned from the training data. Maximum Likelihood Classification assumes that the statistics for each class in each band are normally distributed and calculates the probability that a given pixel belongs to a specific class. The result is a classified image where each pixel is labelled with a land cover type.

Final land use land cover map

Based on the results of the supervised classification and accuracy assessment, the final land cover map is produced. This map visually represents the spatial distribution of different land cover types for the study years. The map is validated against additional ground truth data or other independent datasets to ensure its accuracy and reliability.

Land cover area calculation

The area covered by each land cover type is calculated from the final land cover map. This involves summing the pixel areas for each class and converting them into meaningful units (hectares). This step provides quantitative information on the extent of different land cover types, which is crucial for various applications like resource management, environmental monitoring and urban planning. The NDVI values ranged from -0.33 to +0.88 for the year 2000 (Fig. 3), 0.17 to +0.76 for 2010 (Fig. 4) and -0.07 to +0.70 for 2023 (Fig. 5). These differences show varying levels of vegetation health and thickness. Higher values mean healthier vegetation and lower values indicate the water body. These changes are linked to factors like urban growth, deforestation and farming activities. 3). The total area under the Yellapur forest division is 197,130 hectares. The LULC map of the Yellapur forest division is depicted in Figure 6. The Land Use Land Cover (LULC) analysis using Landsat-7 data for 2010 provided the distribution and extent of various land cover categories within the study area. Vegetation emerged as the dominant land cover encompassing 1,65,832 hectares constituting 84.12 per cent of the total area including forests, plantations and other green spaces that are vital for ecological balance and biodiversity. Built-up areas account for 15,073 hectares representing 7.65 per cent of the land. Agricultural land covers 14,224 hectares (7.22%) of the area. Water bodies including lakes, ponds and rivers are the smallest land cover category occupying 2,000 hectares which is 1.01 per cent of the area (Table 4). The total area under the Yellapur forest division is 197,130 hectares. The LULC map of the Yellapur forest division is depicted in Figure 7. 5). The total area under the Yellapur forest division totals 197,130 hectares. The LULC map of the Yellapur forest division is depicted in Figure 8. 6).

Spatial and Temporal

The area covered by water bodies, including lakes, rivers and ponds was 2,087 hectares in 2000 accounting for 1.06 per cent of the total geographical area. In the year 2010, the area under water bodies decreased to 2,000 hectares (1.01%) which further decreased to 1,721 hectares (0.87%) in the year 2023. Over the 23 years from 2000 to 2023, the percentage of area covered by water bodies decreased by 0.91 per cent, primarily due to the conversion of land to agricultural fields. Over 23 years from 2000 to 2023 in Yellapur forest division the forest cover lost was around 17090 ha on the other hand there is an increase in agricultural land of about 4607 ha. whereas an increase in the built-up area was 12849 ha due to urbanization, agriculture expansion and deforestation. and vegetation. Large areas of agricultural land have been converted to built-up areas, while some vegetation has also been lost to urban expansion. Additionally, there has been a noticeable shift from water bodies and vegetation to other land use types reflecting the ongoing impacts of human activity on the landscape.

The major findings show that about 901.01 hectares of built-up land changed to agricultural land, while 5,263.92 hectares of vegetation were turned into farmland. A large shift occurred with 15,784.92 hectares of vegetation being converted into builtup areas and 4,904.19 hectares of agricultural land also converted to built-up areas. On the other hand, around 2,420.82 hectares of farmland were converted back to vegetation. These changes reflect the ongoing shifts in land use influenced by urban growth, infrastructure development and the expansion of agriculture (Table 8 and Fig. 10).

Accuracy assessment

An accuracy evaluation was carried out in Google Earth Engine using the. Confusion Matrix () function. The overall accuracy and kappa coefficient were calculated using the. Accuracy () and. kappa () functions, respectively. In 2000, the overall accuracy was recorded at 96.07 per cent, with a Kappa coefficient of 0.92. In 2010, the overall accuracy was 95.29 per cent and the Kappa coefficient was 0.91. By 2023, the overall accuracy reached 95.45 per cent, with a Kappa coefficient of 0.94. These values reflect the observed accuracy and agreement measures for the respective years (Table 9). [22] conducted a decadal change detection analysis spanning from 1985 to 2005 reveal a continuous decline in forested land, accompanied by an increase in croplands and built-up areas. This shift has led to degradation, desertification and biodiversity loss, influenced by both physical and human factors. The study also enhances our understanding of how land use and land cover (LULC) changes are linked to climate change, food production, health, urbanization, coastal zone management and transboundary migration. Additionally, the maps generated from this analysis will help identify areas vulnerable to land degradation, changing precipitation and temperature patterns and shifts in biome boundaries. Bhat et al. (2017) [5] conducted a similar study and highlighted the significant environmental impacts of current urban growth trends on surrounding ecosystems. The rapid expansion of urban and built-up areas has led to dramatic shifts in land use and land cover marked by sharp declines in agricultural lands, fallow areas and vacant spaces. The challenges posed by a densely populated area with high resource consumption and limited land availability have severely affected the city's ability to address the demands of modern growth. To tackle these issues, it is crucial to develop planning policies that promote a sustainable environment. Rational urban planning strategies should be implemented to mitigate the negative effects.

Summary and Conclusion

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