The rehabilitation of cleared sites, such as well pads, is a core component of environmental restoration programs. Monitoring vegetation recovery over time is essential for assessing restoration effectiveness, and identifying when additional intervention may be needed. Remote sensing provides significant advantages over field-based methods by enabling repeatable, cost-effective observations at scale.

In this project, we focused on the analysis of open-source satellite products. While commercial very high resolution imagery can add significant value to targeted assessments, satellite programs such as Landsat and Sentinel remain the foundation for consistent, long-term, and cost-effective monitoring of rehabilitation outcomes.

In this study, we used Landsat products, which are jointly managed by NASA and USGS [1, 2]. Landsat's global coverage, consistent spectral bands, and long temporal depth make it particularly suited for long-term environmental change detection. Our analysis begins with Landsat 5, which provides 30m spatial resolution across 7 spectral bands, and a 16-day revisit cycle, from 1984. Critical for rehabilitation assessments, this allowed us to remote sense the pre-clearance state of the target sites.

Several recent studies support the value of Landsat time series, reference-based assessment, and the application of statistical approaches to ecological monitoring. For example, Monroe et al. [3] developed a dynamic reference approach using NDVI to monitor sagebrush recovery at former oil and gas well pads. Wang et al. [4] tracked vegetation disturbance and restoration in open-pit coal mines, using Landsat products and the LandTrendr temporal segmentation algorithm in Google Earth Engine [5].

Aerial view of arid terrain showing drainage patterns and variation in vegetation cover across the landscape.
The problem in one frameRecovery has to be judged against what the site was, and against comparable country nearby, not against a fixed target.

Method

1. Site pairing for reference selection

To control for spatial and environmental variability, each cleared rehabilitation site was paired with three nearby reference sites. Candidate reference pixels were selected based on spatial proximity to the rehabilitation site, pre-clearance land cover class, and temporal stability. Temporal stability was calculated using standard deviations of fractional cover components from Digital Earth Australia [6], the goal being to avoid reference sites that were subject to anthropogenic disturbance.

Candidate reference sites were then filtered and ranked, using Euclidean distance, across a range of normalised environmental and spectral features, including topographic relief, various soil metrics from TERN [7], and pre-clearance fractional cover.

2. NDVI time series extraction

For both the rehabilitation site and its reference sites, median annual Normalised Difference Vegetation Index (NDVI) values were extracted from Landsat surface reflectance products, from 1984 to the present, using Google Earth Engine [8]. This extended time series encapsulated the pre-clearance state at the rehabilitation site. To reduce cloud contamination and phenological noise, that is, natural variability in vegetation signals caused by seasonal growth and senescence cycles, the analysis was restricted to the southern hemisphere growing season, November to March of the following year.

3. Recovery threshold and logistic modelling

Using pre-clearance NDVI values from the rehabilitation site and its matched reference sites, a recovery threshold was calculated, defined here as 90% of the pre-disturbance NDVI. Post-disturbance NDVI values at the rehabilitation site were fitted to a regression model. The year in which the modelled NDVI curve exceeded the recovery threshold was recorded as the predicted recovery year. This analysis was performed on both raw and smoothed NDVI values.

4. LSTM-based prediction of rehabilitation trajectories

To improve rehabilitation trajectory predictions, a Long Short-Term Memory (LSTM) neural network was trained on a dataset of 144 rehabilitated sites. The LSTM architecture is capable of capturing nonlinear temporal dependencies, using both dynamic features such as weather and pre-clearance NDVI, and static features such as soil and relief [9]. The LSTM was trained to predict raw post-clearance NDVI trajectories, over 5-fold cross-validation, and out-of-fold predictions were saved for evaluation. Preliminary results, over our small dataset, suggest the potential for learning typical vegetation recovery trajectories across sites and weather scenarios, in effect generalising rehabilitation trajectory experience.

Conclusion

This method used the Landsat archive to provide an efficient, scalable, and ecologically meaningful assessment of rehabilitation progress at cleared well pads. The exceptional temporal depth of the Landsat archive allowed us to directly view the pre-clearance state of target sites, and thereby establish reasonable matched pairs and post-clearance rehabilitation targets. The integration of LSTM modelling may improve the robustness of recovery estimates.

For a more complete picture of site rehabilitation, NDVI-based time series approaches should be combined with other remote sensing modalities, including those using very high resolution satellite products.

References

  1. USGS (2021). Landsat Satellites. https://landsat.gsfc.nasa.gov/satellites/
  2. Wulder, M.A., et al. (2019). Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225, 127-147. https://doi.org/10.1016/j.rse.2019.02.015
  3. Monroe, A.P., et al. (2022). Assessing vegetation recovery from energy development using a dynamic reference approach. Ecological Indicators, 141, 109135.
  4. Wang, Y., et al. (2021). Tracking the vegetation change trajectory over large-surface coal mines in the Jungar coalfield using Landsat time-series data. Remote Sensing, 13(18), 3625.
  5. Pasquarella, V.J., et al. (2022). Demystifying LandTrendr and CCDC temporal segmentation. Remote Sensing of Environment, 276, 113124.
  6. Digital Earth Australia products. https://explorer.dea.ga.gov.au/products
  7. Terrestrial Ecosystem Research Network, TERN Soil and Landscape Grid of Australia. https://portal.tern.org.au
  8. Google Earth Engine Team (2015). Google Earth Engine: A planetary-scale geospatial analysis platform. https://earthengine.google.com
  9. Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), pp. 1735-1780. doi:10.1162/neco.1997.9.8.1735