Forest Health Monitoring Research Topics

Forest Health Monitoring Research Topics is the research for observing the health of forests to find the wellness of the trees, biodiversity levels, etc. Here we look over the definition, applications, uses and some other concepts or information relevant to this proposed research.

  1. Define Forest Health Monitoring

At the first stage we see the definition for forest health monitoring, it defines the efficient valuation of forest ecosystems to calculate their whole strength and condition. It contains the examination and observation of different measures like species diversity, ecological processes and tree health to find the possible informed management decisions and threats.

  1. What is Forest Health Monitoring?

Afterwards the definition we converse about the in-depth description of forest health monitoring. It contains the usual valuation of forest ecologies to measure their whole comfort, containing the ecological functions, health of trees and biodiversity levels. This procedure assists to find the factors that affect sustainable management practices, forest health and permitting timely intervention.

  1. Where Forest Health Monitoring used?

Next to the in-depth description we discuss where to utilize this. It is employed in different frameworks like research initiatives, conservation efforts and sustainable forest management. It is worked by non-profit organizations, researchers to evaluate the condition of forests, government agencies, develop strategies for tackling threats such as diseases, invasive species, detect changes over time and climate change affects.

  1. Why Forest Health Monitoring technology proposed? , previous technology issues

Here we proposed the forest health monitoring technology to tackle the issues in previous methods, labour intensive, manual surveys which were time consuming and frequently limited in spatial coverage. By using the improvements in GIS, data analytics and remote sensing, this proposed technique provides an effective, comprehensive technique to handle forest ecologies, cost-effective, permit for timely identification of modifications and informed decision-making.

  1. Algorithms / protocols

For our proposed research we include the following methods or algorithms to overcome the existing technology. The algorithms that we utilized are Simple Linear iterative Clustering and Point net Architecture, Breaks for Additive Seasonal and Trend with Convolutional Gated Recurrent Unit (BAST-C-GRU) and St-series-based Digital Surface Model (DSM) was St-s-DSM.

  1. Comparative study / Analysis

In this research we compare several methods or techniques to find the best outcome for this research. The major objective of this research is to constantly identify the forest health in an efficient way. Some of the highlights for this research are as follows:

  • Improving the precise segmentation to rightly find the health of forest by employing pre segmentation through Watershed Segmentation algorithm and post segmentation over Simple Linear Iterative Clustering and Point net architecture (SLIC) in this session to evade the undesired characteristics to only segment the forest coverage areas.
  • The novel St-series based DSM also known as St-s-DSM is employed to mapping the plants occupied areas in precisely and individually mapping the plants wealth in the forest coverage areas.
  • To make sure the forest health for precisely identifying and categorizing the forest covered regions by incorporating the novel techniques for Additive Seasonal and Trend with Convolutional Gated Recurrent Unit (BAST-C-GRU).
  • At last to monitor the health of the forest constantly by utilizing the NRT-MONITOR algorithm in real time monitoring which assists to maintain the forest natural wealth in upcoming days.
  1. Simulation results / Parameters

Our proposed technique is evaluated by using the parameters or performance metrics and it is compared with some other existing technology to obtain the best outcome. The metrics that we compared are as follows: F-measure, detection rate, precision, recall and Accuracy with the Number of epochs.

  1. Dataset LINKS / Important URL

Below we provide some important links that are very useful to know the details about our proposed forest Health Monitoring. The subsequent link is used to go through the details or information related to this proposed research.

  1. Forest Health Monitoring Applications

Let’s note the applications for this proposed health monitoring for forest. These consist of permitting the effects of diseases, pests and invasive species on forest ecologies, monitoring the impact of climate change and behavior of humans on the forest health and locating the modifications in the human quality and biodiversity. These applications notify the management policies which are targeted to safeguarding and improving the flexibility of forest.

  1. Topology for Forest Health Monitoring

Now we look for the topology to be utilized for our proposed strategy. It consists of creating a network of tracking sites intentionally dispersed along different forest areas and kinds. This technique makes sure the comprehensive handling of measuring different parameters on the basis of ecology like species composition, environmental conditions and tree health, to obtain the understanding into the whole health and dynamics of the forest ecosystem.

  1. Environment for Forest Health Monitoring

Environment that is utilized for health monitoring in forests that incorporate the ecological, physical and biological conditions over the forest environments. It comprises factors like vegetation composition, climate, soil characteristics and disturbances like diseases and Pests. Tracing this environment offers understanding into the strength and health of forest, assisting in management conservation efforts and decisions.

  1. Simulation tools

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

  1. Results

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. 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 and mapping in forest, these topics will provide us some understandable and effective ideas and it solve the queries that arises with us:

  1. Towards operational UAV-based forest health monitoring: Species identification and crown condition assessment by means of deep learning
  2. Spectral aspects for monitoring forest health in extreme season using multispectral imagery
  3. Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests
  4. Analysis of the Effectiveness of the Red-Edge Bands of GF-6 Imagery in Forest Health Discrimination
  5. UAV-Based Forest Health Monitoring: A Systematic Review
  6. The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis
  7. A near-real-time approach for monitoring forest disturbance using Landsat time series: stochastic continuous change detection
  8. Citizen Science and Monitoring Forest Pests: a Beneficial Alliance?
  9. A Systematic Review on the Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Health Attributes, Indicators, and Measures
  10. Drought stress and pests increase defoliation and mortality rates in vulnerable Abies pinsapo forests
  11. Integrating Nasa’s Gedi and Landsat 8 Oli Data for Regional Aboveground Biomass Mapping In Forested Areas Impacted by Hurricane Ian in Florida
  12. Tropical Forests Mapping with Tandem-X and Deep Learning Methods
  13. Forest Mapping Using a VGG16-UNet++& Stacking Model Based on Google Earth Engine in the Urban Area
  14. Intercomparison of Earth Observation Data and Methods for Forest Mapping in the Context of Forest Carbon Monitoring
  15. Mapping Boreal Forest Species and Canopy Height using Airborne SAR and Lidar Data in Interior Alaska
  16. ForestMap: Mapping Forest Attributes Across the Globe – First Case Study
  17. Tree Species Mapping of a Hemiboreal Mixed Forest Using Mask R-CNN
  18. Large-Scale Forest Height Mapping from TanDEM-X, ICESat-2 and Landsat 8 Data using a Machine-Learning Method
  19. A Novel Feature Evaluation Method in Mapping Forest AGB by Fusing Multiple Evaluation Metrics Using PolSAR Data
  20. Integrating Low-Cost UAV and GCP-based Structure-from-Motion Techniques to Generate Very High-Resolution Orthoimage for Bamboo Forest Mapping and Individuals Segmentation
  21. Deep Learning Models in Forest Mapping Using Multitemporal SAR and Optical Satellite Data
  22. High-resolution Mapping of Forest Canopy Height by Integrating Sentinel and airborne LiDAR data
  23. Mapping Tropical Successional Forest Stages using Multifrequency Sar
  24. Mapping Forest Disturbance Types in China with Landsat Time Series
  25. Large-Scale Forest Height Mapping in the Northeastern U.S. using L-Band Spaceborne Repeat-Pass SAR Interferometry and GEDI LiDAR Data
  26. Fusion of Tandem-X and Gedi Data for Mapping Forest Height in the Brazilian Amazon
  27. Large-Scale Forest Height Mapping by Combining TanDEM-X and GEDI Data
  28. Mapping Forest Age in North and South Carolina using Time Serial Observations and Field Inventory Data
  29. Mapping Forest Vertical Structure Attributes with GEDI, Sentinel-1, and Sentinel-2
  30. Mapping Forest Canopy Height at Large Scales Using ICESat-2 and Landsat: An Ecological Zoning Random Forest Approach
  31. A Novel Semisupervised Contrastive Regression Framework for Forest Inventory Mapping With Multisensor Satellite Data
  32. Semantic and Depth Learning for Autonomous Forest Mapping
  33. Forest Vegetation Optical Depth Mapping Using GNSS Signals at SMAPVEX’22
  34. Deep Forest Based Paddy Phenology Mapping Using Dual-Pol SAR Data
  35. Forest Biomass Mapping Using Continuous InSAR and Discrete Waveform Lidar Measurements: A TanDEM-X/GEDI Test Study
  36. A UGV-Based Forest Vegetation Optical Depth Mapping Using GNSS Signals
  37. Fusion of Sentinel 1 and Alos Palsar Data to Separate Palm Oil Plantations from Forest Cover Mapping using Pauli Decomposition Approach
  38. Mapping and Monitoring Forest Plantation using Fraction Images Derived from Multi-Annual Landsat TM Datasets
  39. Instance segmentation and stand-scale forest mapping based on UAV images derived RGB and CHM
  40. Classifying forest cover and mapping forest fire susceptibility in Dak Nong province, Vietnam utilizing remote sensing and machine learning
  41. Forest ecosystem on the edge: Mapping forest fragmentation susceptibility in Tuchola Forest, Poland
  42. Mapping tropical forest degradation with deep learning and Planet NICFI data
  43. Urban forest biotope mapping: A new approach for sustainable forest management planning in Mexico City
  44. Global mapping of forest clumping index based on GEDI canopy height and complementary data
  45. Mapping planted forest age using LandTrendr algorithm and Landsat 5–8 on the Loess Plateau, China
  46. Automatized Sentinel-2 mosaicking for large area forest mapping
  47. A LiDAR biomass index-based approach for tree- and plot-level biomass mapping over forest farms using 3D point clouds
  48. Signature extension as a machine learning strategy for mapping plantation forest genera with Sentinel-2 imagery
  49. Mapping historical forest biomass for stock-change assessments at parcel to landscape scales
  50. Mapping evergreen forests using new phenology index, time series Sentinel-1/2 and Google Earth Engine