Land Cover Change Analysis for Urban Deltaic Megaregions

Introduction

This repository contains R scripts, workflows and research outputs developed and generated in the Land Cover Change (LCC) analysis of the Pearl River Delta (PRD) urban megaregion. The results of this analysis have been published in Cannatella, 2023. Please refer to the paper for detailed findings and analysis. The project utilized openly available datasets from various sources. For further information, please see the Data Sources section.

Project Objectives

The primary objectives of this project are as follows:

1. Conduct Fine-Grained Land Cover Change (LCC) Analysis: Utilize open remote sensing-derived data and spatial analysis techniques to conduct a comprehensive analysis of land cover changes in the Pearl River Delta (PRD) urban megaregion at a fine-grained temporal resolution of 1 year.

2. Identify Patterns and Trends: Identify and analyze LCC patterns and trends in land cover change over a specific time span, focusing on urbanization, agricultural expansion, and natural land cover transformations in Low Elevation Coastal Zones (LECz).

3. Assess Impacts on Environment and Society: Assess the environmental and societal impacts of observed land cover changes, including implications for biodiversity, ecosystem services, and human well-being.

4. Provide Insights for Decision-Making: Provide actionable insights and recommendations based on the analysis results to inform land use planning, conservation efforts, and sustainable development initiatives in the PRD region.

5. Contribute to Scientific Knowledge: Contribute to the existing body of scientific knowledge on land cover dynamics in urban deltaic megaregions, particularly in the context of rapid urbanization and socio-environmental changes.

These objectives guide the methodology and analysis approach employed in this project, aiming to contribute to a better understanding of land cover dynamics and their implications for sustainable development.

Data Sources

The analysis of the Pearl River Delta's urbanization dynamics draws upon a diverse array of data sourceswith global coverage:

1. ESA CCI Land Cover Maps (ESA CCI-LC): Provides global land cover datasets at 300 m spatial resolution on an annual basis, from 1992 to 2015.
2. Shuttle Radar Topography Mission (SRTM) Images provides topographic data over 80% of the Earth's land mass, creating the first-ever near-global data set of land elevations.
3. GADM maps and data provides maps and spatial data for all countries and their sub-divisions.

Methodology

Data Collection and Preprocessing

Data for the analysis were obtained from diverse sources, including remote sensing imagery, geospatial datasets, and administrative records. Elevation data were derived from the Shuttle Radar Topography Mission (SRTM) dataset, with a spatial resolution of approximately 30 meters. Raster tiles in .tiff format were downloaded from the SRTM dataset, merged, and subsequently clipped using the bounding box of the administrative boundaries vector file specific to the Pearl River Delta (PRD). This preprocessing step ensured the creation of a seamless and localized elevation dataset tailored to the study area. Additionally, administrative boundaries for the PRD were delineated by merging different shapefiles containing features on administrative boundaries of Hong Kong, Macao, and Mainland cities forming the PRD. By harmonizing and integrating these diverse datasets, the analysis facilitated a comprehensive understanding of the urbanization dynamics within the PRD, enabling informed decision-making and sustainable development strategies. The data were processed and aggregated using QGIS and R packages such as terra and sf, which provide advanced functionalities for the manipulation and analysis of spatial data. This approach allowed for the efficient management of large volumes of geospatial data and the execution of aggregation and analysis operations at a regional level.

• Administrative boundaries: Specific to the PRD were delineated by merging different shapefiles containing features on administrative boundaries of the Hong Kong and Macao Special Administrative Regions (SARs) and mainland cities forming the PRD in QGIS. This process facilitated the creation of a unified administrative boundary dataset tailored to the study area.

• Land Cover Change Maps: Data for the Land Cover Change (LCC) analysis were obtained from external sources and clipped to the administrative boundaries of the Pearl River Delta (PRD). Raster datasets representing land cover maps were downloaded and processed using a custom R script available in the project's repository (e.g., preprocessing_script.R). This script facilitated the preprocessing steps, including clipping the land cover maps to the PRD boundaries and subsequent reclassification based on the classification scheme outlined in Table 1. The resolution of the land cover rasters remained unchanged during the preprocessing steps, as the original resolution was deemed suitable for regional-scale analysis.

• Elevation: Elevation data were derived from the Shuttle Radar Topography Mission (SRTM) dataset, with a spatial resolution of approximately 30 meters. Raster tiles in .tiff format were downloaded from the SRTM dataset, merged, and subsequently clipped using the bounding box of the administrative boundaries vector file specific to the PRD. Elevation data were reclassified into two categories: areas above and below 10 meters above sea level. This reclassification facilitated the identification of low-lying coastal areas susceptible to inundation and other hydrological risks. The preprocessing and reclassification steps were implemented using a custom R script available in the project's repository: scripts/LCC_02_spatitemporalmatrix.R.

Land Cover Change Analysis

Utilize statistical and geospatial techniques to analyze changes in land cover over time.

Visualization

Create visualizations to represent the findings effectively.

Results

Interpret the results to gain insights into the drivers and implications of land cover change within megaregions.

Documentation

Document the entire process, including data sources, methodologies, and findings, to facilitate reproducibility and transparency.

Tools and Technologies

• R Programming Language: Utilized for data manipulation, analysis, and visualization.
• R Packages: Leveraging various R packages such as terra, sp, ggplot2, and leaflet for geospatial analysis and visualization.
• Open Datasets: Incorporating open datasets containing land cover information, possibly sourced from organizations like NASA, USGS, and ESA.
• Version Control: Employing version control systems like Git to manage code and track changes.
• Markdown: Using Markdown for documentation to maintain consistency and readability.

How to contribute

Contributing to the project is encouraged! You can help by fixing bugs, adding features, or improving documentation. If you have ideas, share them by opening an issue. To contribute, fork the repository, make your changes, and submit a pull request. Your contributions are valuable and help improve the project for everyone. We welcome your input and look forward to working together.

For further details and code examples, refer to the project repository and accompanying documentation.