What challenges do remote sensing data pose in studying deforestation's impact on ecosystems?

Deforestation is a major environmental problem that affects biodiversity, climate, and human well-being. Remote sensing is a powerful tool that can help monitor and analyze deforestation patterns and impacts using satellite or aerial imagery. However, remote sensing data also pose some challenges for studying deforestation's impact on ecosystems. In this article, you will learn about some of these challenges and how Geographic Information Systems (GIS) can help overcome them.

1 Data quality and availability

Remote sensing data can vary in quality and availability depending on the source, resolution, frequency, and coverage of the imagery. For example, some satellites may have high spatial resolution but low temporal resolution, meaning they capture detailed images but not very often. Other satellites may have low spatial resolution but high temporal resolution, meaning they capture coarse images but more frequently. Some regions may have more or less cloud cover, which can affect the visibility and accuracy of the imagery. Therefore, choosing the right data source and processing the data to reduce noise and errors are important steps for studying deforestation.

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Emma L Underwood

👩💻Ecological Modeller | 🌍GIS Specialist | 🌿Biodiversity Conservation
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In my experience of working in tropical ecosystems, patchiness, revisit time and cloud cover are the biggest factors impacting remote sensing success. In trying to overcome this, I have looked at LiDAR coverage but found that there are certain regions (e.g., Madagascar, which is my current area of study) where no data exists. Equally, there are existing datasets with global coverage which by default, includes my area of interest, but cannot provide the detail required. For example, the European Space Agency (ESA) 2020-2021 global land cover dataset is impressive, with fine spatial resolution (10m) but unfortunately, my area of study is covered by a single land cover class (forest).

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2 Data analysis and interpretation

Remote sensing data can provide a lot of information, but also require a lot of analysis and interpretation to extract meaningful insights. For example, different types of vegetation may have different spectral signatures, which are the patterns of reflectance or emission of electromagnetic radiation. These signatures can help identify and classify different land cover types, such as forest, grassland, or cropland. However, spectral signatures can also change over time due to seasonal variations, phenological stages, or disturbances. Therefore, comparing and contrasting different images and time periods can help detect and quantify deforestation and its impact on ecosystems.

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Emma L Underwood

👩💻Ecological Modeller | 🌍GIS Specialist | 🌿Biodiversity Conservation
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If you want to look at temporal land cover change and are familiar with the area in question, or have some ground-validated field data to confirm specific classes, the best approach would still be to use a supervised image classification approach, where you as the user can train the image on the spectral signatures. It is still possible to compare images over time with differing spectral signatures this way. Although I have still not fully overcome the issue of differing land cover categories sharing overlapping spectral signatures within the same image e.g., shadow and water!

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3 Data integration and visualization

Remote sensing data can be integrated and visualized with other types of data, such as socio-economic, demographic, or biophysical data, to better understand the drivers and consequences of deforestation. For example, combining remote sensing data with census data can help assess the relationship between population density and forest loss. Similarly, combining remote sensing data with climate data can help evaluate the effect of deforestation on carbon emissions and temperature changes. However, integrating and visualizing different data sources can also pose some challenges, such as data compatibility, scale, and projection. Therefore, using Geographic Information Systems (GIS) can help manage, analyze, and display different data layers and create informative maps and charts.

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4 Data communication and dissemination

Remote sensing data can be communicated and disseminated to various audiences, such as researchers, policy makers, educators, or the public, to raise awareness and inform decision making about deforestation and its impact on ecosystems. For example, creating interactive web maps or dashboards that show deforestation trends and indicators can help engage and educate users about the issue. However, communicating and disseminating remote sensing data can also face some challenges, such as data accessibility, usability, and credibility. Therefore, using appropriate formats, platforms, and languages can help make remote sensing data more accessible, usable, and credible for different users.

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5 Data ethics and governance

Remote sensing data can raise some ethical and governance issues that need to be considered and addressed when studying deforestation and its impact on ecosystems. For example, who owns and controls the data, and how are they used and shared? How are the data quality and accuracy ensured and verified? How are the data privacy and security protected and respected? How are the data impacts and implications assessed and monitored? Therefore, following ethical principles and governance frameworks can help ensure that remote sensing data are used responsibly and accountably for studying deforestation and its impact on ecosystems.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What challenges do remote sensing data pose in studying deforestation's impact on ecosystems?

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2

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1 Data quality and availability

2 Data analysis and interpretation

3 Data integration and visualization

4 Data communication and dissemination

5 Data ethics and governance

6 Here’s what else to consider

Geographic Information Systems (GIS)

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What challenges do remote sensing data pose in studying deforestation's impact on ecosystems?

1

2

3

4

5

6

1 Data quality and availability

2 Data analysis and interpretation

3 Data integration and visualization

4 Data communication and dissemination

5 Data ethics and governance

6 Here’s what else to consider

Geographic Information Systems (GIS)

Rate this article

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