Revolutionizing Monitoring and Tracking for Wildlife Conservation

Introduction

Wildlife conservation has become extremely important, with many species facing extinction due to habitat loss, poaching, climate change, and other threats. Wildlife monitoring and tracking are the key processes in conserving endangered species and understanding an ecosystem’s biodiversity.

Manual observations are a common part of traditional wildlife monitoring techniques. They can be expensive, time-consuming, and of limited scope. Artificial Intelligence (AI) can speed up the process, reduce the costs involved, and improve the quality of the data collected for analysis in tracking and monitoring wildlife.

Source: Wallpaper Safari

This article talks about how to automate the process of tracking and monitoring wildlife by analyzing the data from satellite photography, drones, and acoustic sensors with the help of Artificial Intelligence (AI).

Learning Objectives

• Application of AI to automate wildlife monitoring and tracking by analyzing data from various sources, such as satellite imagery, drones, and acoustic sensors.
• Learning different AI or Machine Learning algorithms, such as object detection algorithms, which can be used to identify and classify animals based on their species, would benefit wildlife conservation.
• Using the Normalized Difference Vegetation Index (NDVI) can be helpful in inferring changes in animal populations through the number of plants in an area, thereby quantifying a given species in the area.
• Learn the benefits of using artificial intelligence (AI) to track and manage wildlife, also how it can reduce costs, save time, and aid effective data collection.
• Understand the potential applications of AI in wildlife conservation efforts and how it can help protect endangered species.

This article was published as a part of the Data Science Blogathon.

Table of Contents

1. Different Ways to Monitor and Track Wildlife Using Artificial Intelligence
2. Conclusion

Different Ways to Monitor and Track Wildlife Using Artificial Intelligence

Normalized Difference Vegetation Index(NDVI) using Satellite Imagery Analysis

Wildlife population monitoring can be done using various information provided by satellite images. For instance, alterations in vegetation density from one region to another can be a sign of alterations in the food supply. This might affect animal populations in a particular region. The Normalized Difference Vegetation Index (NDVI), a measurement of the quantity of vegetation in a specific area, is another name for this.

Applications of AI algorithms include satellite imagery analysis and identification of plant density changes. This can then be used to predict changes in animal density in an area. How much vegetation is present in a specific area can be calculated through the NDVI. Reducing vegetation density can indicate less food availability for herbivores in the region. This can be the potential reason for a decline in their population.

Python code snippet for calculating NDVI from satellite imagery:

import rasterio

# Load the red and near-infrared bands
with rasterio.open('satellite_image.tif') as src:
    red = src.read(3)
with rasterio.open('satellite_image.tif') as src:
    nir = src.read(4)

# Calculate NDVI
ndvi = (nir - red) / (nir + red)

Object Detection in Drone Imagery for Wildlife Monitoring and Tracking

Drones are becoming increasingly popular for wildlife monitoring and tracking. Ariel imagery required to monitor animal populations and movements is collected using drones. In addition to this, drones that contain thermal sensors are used to track the movement of animals by detecting the heat signatures of animals, even in dense vegetation.

Using AI algorithms to analyze drone imagery and identify animals. For example, identifying animals with the help of object detection algorithms in drone imagery. In addition, training machine learning algorithms to classify animals based on their shape and size into several meaningful groups. Conservationists can track and monitor the animal populations in their natural habitat by doing this.

Python code snippet for object detection in drone imagery:

import cv2

# Load the drone image
img = cv2.imread('drone_image.jpg')

# Load the object detection model
model = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')

# Define the classes to detect
classes = ['elephant', 'giraffe', 'lion', 'zebra']

# Set the input image size
input_size = (416, 416)

# Preprocess the image
img = cv2.resize(img, input_size)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img.transpose((2, 0, 1))
img = img[np.newaxis, :, :, :]

# Set the model input
model.setInput(img)

# Get the model output
output = model.forward()

# Extract the object detections
detections = []
for i in range(output.shape[0]):
    for j in range(output.shape[1]):
        class_id = np.argmax(output[i, j, 5:])
        confidence = output[i, j, 5 + class_id]
        if confidence > 0.5 and classes[class_id] in classes:
            x = int(output[i, j, 0] * input_size[0])
            y = int(output[i, j, 1] * input_size[1])

Acoustic Sensors for Wildlife Monitoring

Animal vocalizations are recorded in order to identify species and track their movements in a given area through acoustic sensors, like how birds are identified by their distinctive vocalizations. In addition, many mammals also communicate through vocalizations. For instance, wolves communicate with others in their pack through howling sounds.

Source: Nature

Determining animal species by listening to their vocalizations and using AI systems to assess acoustic data. For instance, grouping or classification tasks may be performed using ML algorithms trained on the frequency and duration of the animal’s vocalizations.

Python code snippet for analyzing animal vocalizations:

import librosa
import numpy as np

# Load the audio file
audio, sr = librosa.load('animal_vocalization.wav')

# Extract features from the audio
mfccs = librosa.feature.mfcc(audio, sr=sr, n_mfcc=20)
chroma = librosa.feature.chroma_stft(audio, sr=sr)
spectral_contrast = librosa.feature.spectral_contrast(audio, sr=sr)

# Concatenate the features
features = np.concatenate((mfccs, chroma, spectral_contrast))

# Load the machine learning model
model = sklearn.linear_model.LogisticRegression()

# Train the model
model.fit(X_train, y_train)

# Predict the species of the animal
species = model.predict(features)

Heat Signature Detection in Thermal Imagery

Source: Kyiv Post

Thermal sensors mounted on drones or other platforms can detect the heat signatures of animals in their natural habitats. By analyzing thermal imagery using machine learning algorithms, conservationists can identify the location of animals and monitor their movements over time. The AI’s ability to identify the presence of poachers in protected regions makes it particularly helpful for anti-poaching activities, which are otherwise manual and time taking.

Tracking Wildlife Movement Patterns

Animal movement patterns may be studied over time using AI algorithms. This can provide important details about their behavior and habitat use. Conservationists can better understand the needs of various animal populations and decide how to protect them by analyzing movement patterns using GPS collars or other sensors.

Source: Scout Life Magazine

Conclusion

The process of automating wildlife monitoring and tracking by analyzing the data from different sources, such as satellite imagery, drones, and acoustic sensors, can be done using artificial intelligence (AI). This would help in wildlife conservation. Automating this process reduces conservation efforts by speeding up the process, reducing costs, and increasing the accuracy of the data collected. AI can also reduce conservation efforts by protecting endangered species by identifying variations in their populations.

Key Takeaways

• By processing data from various sources, including satellite imagery, drones, and acoustic sensors, AI could be a way to automate the tracking and monitoring of wildlife.
• Using ML algorithms to classify animals based on shape and size in drone images and identification of species based on their vocalizations in acoustic data.
• Using object detection algorithms for recognizing animals in drone imagery. Using NDVI to forecast changes in animal populations.
• AI can fasten the process of tracking and monitoring animals, cut costs, and improve the accuracy of the information gathered. This can support endangered species protection and conservation efforts.
• How animals’ heat signatures and identifying animals in drone imagery with the help of drones, thermal sensors, and object detection algorithms, respectively.

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