Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests

Pacheco-Pascagaza, Ana María; Gou, Yongsheng; Louis, Valentin; Roberts, John F.; Rodríguez-Veiga, Pedro; Bispo, Polyanna da Conceição; Espírito-Santo, Fernando Del Bon; Robb, Ciaran; Upton, Caroline; Galindo, Gustavo; Cabrera, Edersson; Cendales, Indira Paola Pachón; Santiago, Miguel; Negrete, Oswaldo Carrillo; Meneses, Carmen; Iñiguez, Marco; Balzter, Heiko

doi:10.3390/rs14030707

Cited by 16 publications

(1 citation statement)

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“…If a precise date for a land cover change event is available, supervised methods can be trained to classify change using input data covering a period of time shorter than year (Figure 1). Furthermore, if these labelled events occur at a variety of points in time throughout the year (and potentially across multiple years) models that can recognise the signal of land cover change regardless of the time of year and are robust to intra and inter-annual natural variability can be trained (Pacheco-Pascagaza et al, 2022). These models can then be applied continuously as new data become available and produce near-realtime land cover change alerts.…”

Section: Introductionmentioning

confidence: 99%

Continuous land cover change detection in a critically endangered shrubland ecosystem using neural networks

Moncrieff

2021

Preprint

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Existing efforts to rapidly detect land cover change in satellite image time-series have mostly focused on forested ecosystems in the tropics and northern hemisphere. The notable difference in reflectance that occurs following deforestation allow for unsupervised methods, often with manually determined thresholds, to detect land cover change with relative accuracy. Less progress has been made in detecting change in low productivity, disturbance-prone vegetation such as grasslands and shrublands, where natural dynamics can be difficult to distinguish from habitat loss. Renosterveld is a hyperdiverse, critically endangered shrubland ecosystem in South Africa with less than 5-10% of its original extent remaining in small, highly fragmented patches. I demonstrate that supervised classification of satellite image time series using neural networks can accurately detect the transformation of Renosterveld within a few days of its occurrence, and that trained models are suitable for operational continuous monitoring. A training dataset of precisely dated vegetation change events between 2016 and 2020 was obtained from daily, high resolution Planet labs satellite data. This dataset was then used to train 1D convolutional neural networks and Transformers to continuously classify land cover change events in multivariate time-series of vegetation activity from Sentinel 2 satellites as new data becomes available. These models reached a f-score of 0.93, a 61% improvement over the f-score of 0.57 achieved using an unsupervised method designed for forested ecosystems. Models have been deployed to operational use and are producing updated detections of habitat loss every 10 days. There is great potential for supervised approaches to continuous monitoring of habitat loss in ecosystems with complex natural dynamics. A key limiting step is the development of accurately dated labelled datasets of land cover change events with which to train machine learning classifiers.

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Section: Introductionmentioning

confidence: 99%