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Land Change Analysis Needs AI

Gazelle Ecosolutions

3 min read

What is Land Change Analysis?

In previous blog articles, we had discussed canopy cover and burn analysis as methods to determine how land has changed over time. Broadly, this is referred to as land change analysis. So, what is that anyway? Well, this broad term involves examining and quantifying alterations in the landscape over time. 


Ultimately, this process is actually pretty important for understanding environmental transformations, whether that’s due to natural events like fires and floods or human activities such as agriculture and urban development. By studying these changes, analysts, managers, researchers, policymakers, etc. can have informed opinions for decision making. Maybe this involves land management, conservation efforts, or another tangential area.


Why is Land Change Analysis Important?

Branching off what was just stated, understanding land change is crucial for several reasons, including environmental conservation, sustainable development, and disaster management. Land change data helps identify trends in deforestation, urban sprawl, and habitat loss, allowing for proactive measures to mitigate negative impacts. Additionally, it supports the development of nature-based carbon projects by detecting and monitoring land changes, which is vital for achieving climate goals. In fact, Gazelle operates in that space for our Southern Africa grassland projects and uses this analysis pretty frequently to keep track of how well they’re doing!


How is Land Change Analyzed Now?

Currently, land change is analyzed using a plethora of methods. This includes remote sensing and field surveys that groundtruth and verify what’s actually happening. What is remote sensing? Well, remote sensing involves the use of satellite imagery and aerial photography to monitor large areas over time. For something like land change analysis, techniques like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI) help quantify vegetation cover and detect changes.


Most importantly, advanced tools such as ArcGIS  and Google Earth Engine facilitate these analyses, enabling the classification of land use and land cover (LULC). These tools use supervised or unsupervised classification methods to map and monitor land change dynamics. However, traditional methods often require significant expertise and resources, making them less accessible to smaller organizations.


How Can Land Change Analysis Be Improved?

If you think the existing technology is pretty good, there’s no doubt that–with the integration of artificial intelligence (AI) with remote sensing technologies–land change analysis can be a lot better.. AI can automate the process, making it more efficient and tailored to specific needs. For example, AI algorithms can analyze large datasets, detect patterns, and generate detailed reports, offering precise, real-time insights into land changes.


Customizing LULC algorithms using AI can consider factors like color, area size, ecosystem, and region, enhancing the accuracy and relevance of the analysis. This approach is particularly beneficial for monitoring and managing diverse ecosystems, where traditional methods might fall short.


Gazelle’s Remote-Sensing Methods

Gazelle’s AI-driven remote sensing tool is designed to streamline the land change analysis process. By leveraging advanced technologies like LandTrendr Temporal Segmentation (LTR) and Continuous Change Detection and Classification (CCDC), Gazelle offers a comprehensive suite of tools for observing and monitoring land changes.


LandTrendr Temporal Segmentation (LTR)

LandTrendr uses spectral-temporal segmentation algorithms to analyze time series of satellite imagery. It identifies both gradual processes, like vegetation recovery, and abrupt events, such as fires. This method minimizes inter-annual signal noise and uses medoid composite images to represent annual steps, making it particularly useful in cloudy regions.


Continuous Change Detection and Classification (CCDC)

CCDC models temporal-spectral characteristics, including seasonality and long-term trends, using all available cloud-free imagery within a defined period. It calculates harmonic patterns and allows for synthetic image production based on model predictions. This continuous approach is valuable for monitoring phenologically complex ecosystems.


Spectral Mixture Analysis (SMA)

SMA decomposes mixed pixel values into individual components, offering a more precise understanding of land cover composition. This method is particularly valuable for making concrete measurements of ecosystem responses to land management practices. Gazelle’s SMA methods rely on spectral endmembers representing true land cover types and are being developed to use both field-collected and high-resolution imagery endmembers.


What is Gazelle?

Gazelle is an AI-driven remote sensing tool designed to streamline the process of land change analysis. By leveraging advanced technologies and methodologies, Gazelle offers precise, real-time insights into landscape changes, aiding in better decision-making for land management and conservation efforts. Here’s how Gazelle can help:


  1. Automating Land Change Assessments: Gazelle can automate the land change assessment process, making it more efficient and tailored to specific needs.

  2. Customizing LULC Algorithms: Gazelle provides customized algorithms that consider factors like color, area size, ecosystem, and region, enhancing accuracy and relevance.

  3. Generating Detailed Reports: Gazelle generates comprehensive reports and comparisons of land changes over time, offering valuable insights for project developers and environmental scientists.

  4. Integrating Remote Sensing Data: By integrating various remote sensing data sources, Gazelle offers a holistic view of land change dynamics, enabling better monitoring and management practices.


Gazelle’s innovative approach ensures that land change analysis is accessible, accurate, and efficient, helping stakeholders make informed decisions to promote sustainable land use and conservation efforts.

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