Forest Health Monitoring Research Topics

Forest Health Monitoring Research Topics is the research for observing the health of forests. It is widely used in many of the applications and it overcomes the existing technology issues to propose this technique. Here we look over the definition, applications, uses and some other concepts or information relevant to this proposed research.

1. Define Forest health monitoring with remote sensing using satellite data?

In the beginning of the research we first see the definition for forest health monitoring with remote sensing using satellite data. It defines the procedure of measuring the vitality, ecological integrity and condition of forest environment over the examination of data obtained from satellite-based remote sensing technologies. This technique consists of using satellite-based remote sensing technologies. This technique involves employing the satellite sensors to gather different kinds of imagery like radar, thermal and optical data that are processed and examined to derive information about the dynamics and health of forested landscapes. Some of the types are Forest health assessment, Biodiversity assessment, Vegetation cover mapping, Fire risk and fire behavior prediction, Biomass estimation, Disturbance detection and Carbon sequestration monitoring.

2. What is Forest health monitoring with remote sensing using satellite data?

Next to the definition we see the in-depth definition for this proposed strategy. It consists of evaluating the dynamics and condition of forest over the satellite imagery. This technique permits the tracing of biomass, biodiversity, disturbances and vegetation cover over large spatial scales. It subsidizes sustainable forest management, climate change mitigation policies and biodiversity conservation. By utilizing techniques like change detection, vegetation indices and image classification, it enables cost-effective and timely monitoring of forest health and assists evidence-based decision making for management and conservation efforts.

3. Where Forest health monitoring with remote sensing using satellite data used?

Afterwards the in-depth definition we discuss where to use this proposed strategy. It assists in evaluating understanding carbon dynamics, monitoring timber resources, managing water resources, habitat quality, identifying forest disturbances, supporting research and education initiatives and evaluating urban expansion. It identifies the applications in climate change research, urban planning, Conservation, education, disaster response, forestry management and urban planning. By offering the precious understandings into the forest environment over large spatial scales, satellite-based controlling enables informed decision making for biodiversity conservation efforts and sustainable land management.

4. Why Forest health monitoring with remote sensing using satellite data technology proposed? , previous technology issues

Here we proposed a forest health monitoring technique to overcome the existing technology issues. The previous technology that monitors forest health will come across some important difficulties like lack of forest parameter estimation, less sensitivity and low accuracy. These difficulties will possibly cause susceptibility of real-time forest health monitoring, some of the major issues are: Real-time monitoring, Noise effect, Overfitting issue, Low sensitivity and modeling challenges.

5. Algorithms / protocols

Our proposed technology utilizes the following methods or techniques to overcome the limitations in the existing technologies. The methods that we utilized are Generative Neural Network with Hyperparameter Optimization, Web-based near kinematic real-time, Sentinel-2 Multispectral Triangular Vegetation Index (STVI), Adaptive median filter and XGBoost conjunction with random forest.

6. Comparative study / Analysis

We have to compare various methods or techniques to tackle the existing technology issues and to enhance its findings. The methods that we utilize to compare are as follows:

• To obtain the data with increased level of sensitivity, the Sentinel-2 Multispectral sensor is utilized. Particularly the Triangular Vegetation Index (TVI) is employed.
• Using Adaptive Median filter to decrease the noise in the data before we take out the features.
• We propose a Dynamic Generative Neural Network with Hyperparameter for AI- based feature extraction and it will also enhance the accuracy and efficiency during forest parameter estimation.
• Integrating XGBoost with Random Forest to improve the system efficiency and strength.
• A web-based near kinematic real-time forest monitoring technique is used to regularly monitor the forest state.

7. Simulation results / Parameters

Now we see the parameters or metrics to be used to compare this research with the existing techniques to enhance its findings for this research. The metrics that we compared are: Throughput (Bits/sec), Packet loss rate and Sending rate (Mbps) with Time (ms) and the Throughput with Delay (ms), and the Fairness index with RTT (ms) and the Average delay (ms) with Number node.

8. Dataset LINKS / Important URL

For Forest health monitoring with remote sensing, we provide several links to go across it that will aids in configure monitoring of forest health related explanations, methods, or any related information:

• https://www.sciencedirect.com/science/article/pii/S1569843222000425
• https://www.mdpi.com/1424-8220/22/19/7440
• https://www.mdpi.com/2072-4292/14/18/4434
• https://www.mdpi.com/2072-4292/14/9/2051
• https://www.mdpi.com/2071-1050/15/3/1858

9. Forest health monitoring with remote sensing using satellite data Applications

Some of the applications for our proposed techniques are Water management, Land use mapping and monitoring, Agriculture, Mining, Urban planning, Forest mapping, Weather forecasting, Climate monitoring and disaster management are some of the  applications that were utilized by this proposed technique.

10. Topology for Forest health monitoring with remote sensing using satellite data

The topology for this proposed technique will consist of data acquisition, preprocessing, feature extraction, model validation & training and real-time monitoring.

11. Environment for Forest health monitoring with remote sensing using satellite data

Let’s see the environment for this proposed strategy, which generally consists of a high-performance computing structure that is capable of managing the large volumes of satellite data and training complex AI models that will consist of servers that are equipped with Cloud-based computing resources or GPUs. Different software libraries and tools will play a major role in this procedure, machine learning model development, feature extraction and permitting satellite image processing. The tools that it contains are ENVI, scikit-learn, TensorFlow, PyTorch, QGIS and specialized remote sensing software packages.

12. Simulation tools

The forest health monitoring technique is proposed in this research and we can evaluate it by employing the subsequent software requirements. It is developed by employing the developmental tool Python 3.11.4 to obtain the possible correct outcome. It incorporates the operating system Windows 10.

13. Result

We propose a technology named forest health monitoring; this overcomes several previous technology issues and is now utilized in various applications. Here the research finds the best outcomes through the comparison among various performance metrics or parameters. This research is executed by implementing the tool Python 3.11.4.

Forest health monitoring Research Ideas:

The following are the research topics that are relevant to the research for health monitoring in forest, these topics will provide us some understandable and effective ideas and it solve the queries that arises with us:

1. Enhancing the Accuracy of Forest Monitoring Through AI to Reduce Carbon Footprint
2. Intercomparison of Earth Observation Data and Methods for Forest Mapping in the Context of Forest Carbon Monitoring
3. Performance Assessment of Recent Tropical Forest Monitoring Products for REDD+ Operational Services
4. Fault Detection in Forest Fire Monitoring Using Negative Selection Approach
5. Advancements in Global Forest Monitoring Research Founded on ALOS-2 Long-Term Pantropical Land Observation
6. A Preliminary Prototype of LoRa-Based Wireless Sensor Network for Forest Fire Monitoring
7. A Method for Forest Fire Monitoring Based on GA-BP
8. Implementation of AES-256 Algorithm for Secure Data Transmission in LoRa-based Forest Fire Monitoring System
9. Isolation Forests for Anomaly Detection in IoT-Enabled Food Quality Monitoring System
10. Monitoring Forest Degradation in the Amazon Basin with Tandem-X High-Resolution Images and Deep Learning Techniques
11. A Review on Forest Fire Detection and Monitoring Systems
12. Emerging Technologies for Prevention and Monitoring of Forest Fire
13. Achievements of the ALOS-2 L-Band SAR Long-Term Pantropical Forest Monitoring Mission
14. AI applications in forest monitoring need remote sensing benchmark datasets
15. Sentinel-1 and TanDEM-X InSAR Coherence for Monitoring Forests Using Deep Learning
16. A Parallel-based Air-ground Integration System for Forest Ecological Monitoring
17. Assessment of approaches for monitoring forest structure dynamics using bi-temporal digital aerial photogrammetry point clouds
18. A Novel Artificial Spider Monkey Based Random Forest Hybrid Framework for Monitoring and Predictive Diagnoses of Patients Healthcare
19. Improving L-Band SAR Forest Monitoring by Big Data Deep Learning Based on ALOS-2 5 Years Pan-Tropical Observations
20. Data Collection Task Planning of a Fixed-Wing Unmanned Aerial Vehicle in Forest Fire Monitoring
21. Information System for Analysis of Forest Plantations and Monitoring of Ecological Condition
22. Use of response guilds of understory birds in threatened subtropical forest to monitor selective logging impact
23. Fraction Images Derived from Landsat Mss, TM and Oli Images for Monitoring Forest Cover at the Rondônia State, Brazilian Amazon
24. Monitoring Forest Biomass Dynamics in the Laurentides Reserve, Canada, Using Lidar Data and Radar Imagery
25. On the feasibility of estimating contemporary effective population size (Ne) for genetic conservation and monitoring of forest trees
26. Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion
27. IC-GAN: An Improved Conditional Generative Adversarial Network for RGB-to-IR image translation with applications to forest fire monitoring
28. Remotely Monitoring Forest Ecosystem Respiration During Fire Recovery Across North America
29. Monitoring of Forest Disturbances: Near-Real-Time Approach in Cloudy Area based on Optical Satellite Imagery
30. Computer Vision and IoT Enabled Bot for Surveillance and Monitoring of Forest and Large Farms
31. A Novel Forest Disater Monitoring Method Based on FCM and Neighborhood Factor Genetic Algorithm Using Multispectral Data
32. Sustainable Forest Monitoring: A Comprehensive Wireless Sensor Solution Against Illegal Logging
33. Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring
34. The potential of combining satellite and airborne remote sensing data for habitat classification and monitoring in forest landscapes
35. Soil carbon stock changes over 21 years in intensively monitored boreal forest stands in Finland
36. Random Forest Based Flood Monitoring Using Sentinel-1 Images: A Case Study of Flood Prone Regions of North-East India
37. Stability in time and consistency between atmospheric corrections: Assessing the reliability of Sentinel-2 products for biodiversity monitoring in tropical forests
38. Convolutional Neural Networks based Enhanced Forest Monitoring System for Early Fire Detection
39. Multi-Path Fusion: A Hierarchical Machine Learning Approach for Combining Diverse Data Sets for a Forest Monitoring New Observing System
40. Monitoring Forest Above-Ground Biomass from Multifrequency Vegetation Optical Depth: A Preliminary Study
41. Google Earth Engine and Sentinel 1/2 data-based forest degradation monitoring of Sundarban Biosphere Reserve
42. A novel approach towards continuous monitoring of forest change dynamics in fragmented landscapes using time series Landsat imagery
43. Stand structure and environment jointly determine negative air ion concentrations in forests: Evidence from concurrent on-site monitoring in four typical subtropical forests during the growing season
44. Better monitoring of forests according to FAO’s definitions through map integration: Significance and limitations in the context of global environmental goals
45. An optimization-based approach for an integrated forest fire monitoring system with multiple technologies and surveillance drones
46. Applying deep learning to real-time UAV-based forest monitoring: Leveraging multi-sensor imagery for improved results
47. Near real-time monitoring of tropical forest disturbance by fusion of Landsat, Sentinel-2, and Sentinel-1 data
48. Multilevel allometric growth equations improve accuracy of carbon monitoring during forest restoration
49. Rethinking forest monitoring for more meaningful global forest landscape change assessments
50. A model for forest type identification and forest regeneration monitoring based on deep learning and hyperspectral imagery