Unveiling deforestation patterns using satellite imagery.pptx

### Project Overview: Unveiling Deforestation Patterns Using Satellite Imagery

#### Introduction

Deforestation is a critical global issue impacting biodiversity, climate change, and indigenous communities. Traditional methods of monitoring forest cover are often limited by accessibility, cost, and timeliness. This project leverages satellite imagery to unveil deforestation patterns, providing a comprehensive, real-time view of forest changes on a global scale. By utilizing advanced image processing techniques and machine learning algorithms, this project aims to improve the accuracy and efficiency of deforestation monitoring, thereby aiding conservation efforts and policy-making.

#### Objectives

1. **Detect and Monitor Deforestation**: Utilize satellite imagery to identify areas undergoing deforestation.
2. **Analyze Deforestation Patterns**: Understand temporal and spatial patterns of deforestation.
3. **Develop Predictive Models**: Use historical data to predict future deforestation hotspots.
4. **Support Conservation Efforts**: Provide actionable insights to governments, NGOs, and policymakers.

#### Methodology

##### Data Collection

1. **Satellite Imagery Sources**: Use high-resolution images from satellites such as Landsat, Sentinel-2, and MODIS.
2. **Temporal Data**: Gather multi-temporal images to observe changes over time.
3. **Geospatial Data**: Incorporate additional data layers such as topography, land use, and protected areas.

##### Image Processing

1. **Preprocessing**: Correct for atmospheric conditions, sensor errors, and geometric distortions.
2. **Vegetation Indices**: Calculate indices like NDVI (Normalized Difference Vegetation Index) to differentiate vegetation from non-vegetation areas.
3. **Change Detection**: Apply techniques such as image differencing and change vector analysis to identify deforested areas.

##### Machine Learning and Analysis

1. **Training Data**: Use labeled datasets to train machine learning models.
2. **Classification Algorithms**: Implement algorithms like Random Forest, SVM (Support Vector Machine), and deep learning techniques to classify land cover types.
3. **Pattern Analysis**: Analyze spatial and temporal patterns using GIS (Geographic Information Systems) and statistical methods.

##### Predictive Modeling

1. **Historical Data Analysis**: Examine past deforestation trends and contributing factors.
2. **Future Predictions**: Develop models to forecast deforestation under various scenarios using techniques like regression analysis and time-series forecasting.

#### Results

1. **Deforestation Maps**: High-resolution maps highlighting deforested areas and changes over time.
2. **Pattern Insights**: Identification of key drivers and hotspots of deforestation.
3. **Accuracy Assessment**: Validation of results using ground truth data and accuracy metrics such as confusion matrices and Kappa coefficients.

#### Applications

1. **Policy and Plannings for the project report progress and theu

1. Project Phase 2 Review - 1 Dept of Artificial Intelligence and Machine Learning

2. Harnessing the power of Satellite : Unveiling Deforestation patterns and forest cover dynamics Guide - Dr. Rajesh Eswarawaka

3. Objectives To learn, implement and create - Learn About the deforestation patterns, affected areas and forest dynamics through research papers and other projects. About the available datasets and libraries to help in understanding geospatial data and satellite imagery of forest lands Implement A trial code with simple methods to understand and analyze the satellite imagery of forest covers. To experiment with available pre-trained models and assess performance. Methods inclusive of CNN and DL Make changes through hyperparameter tuning and compare performances. Create A system capable of predicting the percentage of depletion of forest cover over a specific region through various models. A visualization of how deforested part of land differs from the land with little to no change in forest covers.

4. Requirements

5. Libraries TensorFlow Keras Rasterio CV2 Numpy Matplotlib Scipy Numba

6. Generic Requirements Dataset The Hansen et al. (2022) Global Forest Change dataset in Earth Engine represents forest change, at 30 meters resolution, globally, between 2000 and 2022. Domain Knowledge Literature review on the project topic along with preparing a research paper on the same. This is inclusive of study of over 10 research papers focussed on various regions across the globe. TPU/GPU Tensor Processing Unit is Google's custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads.

7. Other Requirements GEE Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis. Google Earth Engine is a geospatial processing service. Cloud Storage The storage of geospatial data using Google Cloud Storage and to later initialize and authenticate GEE. Access data by mounting Google Drive. Tensor Processing Unit is Google's custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads.

8. Dataset

9. Dataset Options Hansen Available online as Global Forest Change dataset in Earth Engine, represents forest change(2000-2022) over specific regions depicting tree cover loss. State-Specific Requires a dataset containing the forest cover change data over a duration of 15 to 20 years of a particular state. Requires images in TIFF format. Manual Requires manually looking into known regions affected by deforestation and capture historic data over that particular strip of forest land through Google Earth Pro.

10. State-Specific

11. Hansen

12. Implementation

13. Unpack the dataset Mount GDrive and unpack the dataset zipfile. 2. Define Helper functions and specify the paths for train_images and train_masks. 3. Create tfrecord from files and save them 4. Connect Google CLoud Storage to Google Colab

14. Model Training Usage of TPU - Tensor Processing Unit Initial steps Configure TPU and Connect Google Drive and Google Storage Define Model based on metrics and loss functions Create dataset from the tfrecord file in drive. Create a dictionary describing the features for images and masks Load dataset and read from tfrecord files Training Model building by initializing layers of CNN. Initialize the experimental UNet model and define Conv and Pooling layers. Prepare a custom training loop based on preference. Evaluate the model performance and discover the best learning rate.

15. Pre-Trained Model CNN-based methods, including U-Net, DeepLabV3, ResNet, SegNet, and FCN, stand out as the predominant architecture for deforestation detection, leveraging their roots in image recognition tasks. Designed originally for image recognition, these models exhibit remarkable efficacy in identifying deforested areas using satellite imaging data. By exposing CNNs to a large amount of labeled satellite imagery, these networks learn to extract hierarchical and spatial features, differentiating between forested and deforested regions. This training process enables CNNs to recognize patterns and structures associated with deforestation, facilitating accurate detection. In addition to these well-known architectures, other notable generative models such as Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) have found success in image segmentation and classification tasks, harnessing deep learning for data generation. ResNet, SegNet, and FCN, along with their assorted variants constituting a comprehensive suite of CNN models, have each been featured in no fewer than three studies, collectively contributing to a 5% share per model in the research landscape included in this study.

16. Visualization

17. State-wise

18. Hansen data with Google Cloud

19. Progress Tracking Start Dataset compatible with code Successful Pre-trained model Hyperparameter tuning End Product Arrange suitable Dataset Obtain Google cloud storage Work with Pre-trained model Study traditional approaches Research Analysis on topic Choose specific affected areas Novel Model Creation Model Training Model Testing Evaluate Performance metrics Performance Comparison Accurate prediction and visualization

20. Learn extensively about VAE (Variational AutoEncoders)

21. Thank You