I work at the intersection of biodiversity conservation, forest ecology, climate science, geospatial analysis, and ecological modelling in Bhutan.

My work applies GIS, remote sensing, R, Python, Google Earth Engine, species distribution modelling, camera-trap analytics, machine learning, and reproducible research workflows to translate ecological data into practical conservation and forest-management decisions.

I am a Senior Forestry Officer with the Department of Forests and Park Services, Royal Government of Bhutan.

My work combines field ecology, forestry, geospatial science, statistical modelling, and emerging computational approaches to support evidence-based environmental management.

My main interests include:

• Biodiversity conservation
• Wildlife ecology
• Forest ecology
• Climate-change impact assessment
• Species distribution modelling
• Habitat suitability and connectivity
• Camera-trap ecology
• Forest carbon modelling
• GIS and remote sensing
• Ecological machine learning
• Reproducible environmental data science
• Conservation decision-support systems

I use ecological and spatial modelling to study wildlife distributions, habitat suitability, connectivity, species responses to environmental change, and conservation priorities.

Areas of interest

• Species distribution modelling
• Habitat suitability modelling
• Landscape connectivity
• Movement ecology
• Camera-trap ecology
• Occupancy modelling
• Community ecology
• Spatial prioritization
• Human–wildlife interactions

My forest research focuses on understanding ecological patterns and how forests may respond to environmental and climatic change.

Areas of interest

• Forest carbon modelling
• Forest biomass estimation
• Vegetation ecology
• Climate vulnerability assessment
• Ecosystem integrity
• Climate-driven ecological change
• Forest resilience
• Remote-sensing-based forest analysis

I am interested in connecting ecological science with sustainable forest-resource management and practical decision-making.

Areas of interest

• Non-wood forest products
• Sustainable resource management
• Resource assessment
• Forest bioeconomy
• Community-based natural resource management
• Spatial planning for forest resources

I explore how modern computational approaches can strengthen biodiversity monitoring, ecological research, forestry, and conservation planning.

Areas of interest

• GeoAI
• Machine learning
• AI-assisted ecological analysis
• Cloud geospatial computing
• Scientific automation
• Reproducible analytical pipelines
• Environmental decision-support tools

A reproducible R-based workflow for camera-trap ecological analysis.

The project brings together multiple stages of camera-trap analysis, including:

• Data preparation
• Detection histories
• Occupancy modelling
• Activity-pattern analysis
• Community ecology
• Spatial disturbance analysis
• Biodiversity metrics
• Ecological visualization
• Reproducible reporting

Primary language: R

An R-based analysis workflow for vegetation-community datasets covering trees, shrubs, herbs, regeneration, and environmental relationships.

Applications include:

• Vegetation structure
• Species composition
• Diversity analysis
• Regeneration assessment
• Environmental correlates
• Community-level ecological analysis

Primary language: R

A spatial ecological modelling project focused on Asian elephant habitat suitability and species distribution analysis.

Research themes include:

• Species occurrence modelling
• Environmental predictors
• Habitat suitability
• Anthropogenic influences
• Landscape-scale conservation analysis

Research workflows for analysing elephant movement and spatial ecology.

Potential applications include:

• Movement trajectories
• Habitat use
• Landscape connectivity
• Corridor analysis
• Spatial behaviour
• Conservation planning

A modelling workflow for forest carbon assessment and climate-change scenario analysis in Bhutan using geospatial data, national forest information, Google Earth Engine, and machine-learning approaches.

Research themes include:

• Forest carbon stocks
• Above-ground biomass
• Below-ground biomass
• Climate scenarios
• Remote sensing
• Machine learning
• Spatial prediction

Primary language: Python

Processing workflows and metadata for generating 250 m bioclimatic variables (BIO1–BIO19) for Bhutan from CMIP6 climate projections.

Potential applications include:

• Species distribution modelling
• Climate vulnerability assessment
• Ecological niche modelling
• Biodiversity research
• Forest-climate studies
• Conservation planning

Geospatial workflows for forest stratification and spatial analysis in Bhutan.

Research applications may include:

• Forest classification
• Sampling design
• Forest inventories
• Carbon assessment
• Landscape analysis
• Forest monitoring

Spatial resources and analytical workflows related to Ecosystem Integrity Index assessment in Bhutan's protected-area system.

A platform focused on non-wood forest products and forest-resource information in Bhutan.

The broader objective is to support improved organization, visualization, analysis, and use of forest-resource information for management and decision-making.

Interactive educational resources for learning forest mensuration.

Topics include concepts and methods related to:

• Tree diameter
• Tree height
• Tree form
• Tree volume
• Forest measurement
• Field mensuration methods
• Applied calculations

The goal is to make technical forestry concepts more visual, interactive, and accessible to students.

Resources for learning and applying quantitative methods in ecology.

terra · sf · dismo · gbm · randomForest · brms

geopandas · xarray · rasterio · scikit-learn

Species Distribution Modelling Occupancy Modelling Camera Trap Analytics Remote Sensing Spatial Analysis Machine Learning Climate Scenario Analysis Ecological Data Visualization

A major goal of my work is to make ecological analysis more transparent, reproducible, reusable, and accessible.

I am particularly interested in workflows that combine:

Field observations
        ↓
Data management
        ↓
Quality control
        ↓
GIS & remote sensing
        ↓
Statistical / ecological modelling
        ↓
Visualization
        ↓
Uncertainty assessment
        ↓
Conservation interpretation
        ↓
Decision support

Whenever possible, I aim to structure projects so that analyses can be reproduced rather than existing only as final maps, figures, or reports.

I use GitHub to develop and share:

• Ecological analysis workflows
• Geospatial processing pipelines
• Reproducible research projects
• Environmental datasets and metadata
• Conservation modelling approaches
• Teaching materials
• Interactive learning resources
• Forestry applications
• Experimental conservation-technology workflows

I am continuing to explore:

• GeoAI for conservation
• Advanced machine learning for ecology
• AI-assisted scientific workflows
• Cloud-based geospatial computing
• Automated environmental-data pipelines
• Reproducible research architecture
• Conservation decision-support systems

My repositories and ongoing work broadly fall within:

Biodiversity Conservation
│
├── Wildlife Ecology
│   ├── Species Distribution Modelling
│   ├── Camera Trap Analytics
│   ├── Occupancy Modelling
│   ├── Movement Ecology
│   └── Habitat Connectivity
│
├── Forest Ecology
│   ├── Vegetation Ecology
│   ├── Forest Carbon
│   ├── Forest Stratification
│   └── Ecosystem Integrity
│
├── Climate Science
│   ├── Bioclimatic Variables
│   ├── Climate Scenarios
│   ├── Vulnerability Assessment
│   └── Ecological Responses
│
├── Geospatial Science
│   ├── GIS
│   ├── Remote Sensing
│   ├── Google Earth Engine
│   ├── Spatial Modelling
│   └── GeoAI
│
└── Applied Forestry
    ├── Forest Mensuration
    ├── NWFP Management
    ├── Forest Resource Assessment
    └── Environmental Decision Support

Forestry • Biodiversity • Climate • Geospatial Science • Ecological Modelling • Conservation Technology

Using ecological data and geospatial science to support evidence-based conservation and forest management.