Characterizing forest disturbances across the Argentine Dry Chaco based on Landsat time series

Marzo, Teresa De; Pflugmacher, Dirk; Baumann, Matthias; Lambin, Éric F.; Gasparri, Ignácio; Kuemmerle, Tobias

doi:10.1016/j.jag.2021.102310

Cited by 34 publications

(30 citation statements)

References 79 publications

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“…To avoid problems due to cloud cover, changes in illumination, and atmospheric condition, we used all available images from the growing season of each year (1 May through 15 September) to derive yearly composite images 54 . As our spectral index, we used Tasseled Cap Wetness (TCW), as this index is particularly sensitive to forest structure 55 , is robust to spatial and temporal variations in canopy moisture 56 , and consistently outperforms other spectral indices, including Normalized Difference Vegetation Index 53 , for detecting forest disturbance 50,[57][58][59] . As input parameters for the LandTrendr algorithm when detecting forest disturbances, we used a prevalue of −300 TCW units, a minimum disturbance magnitude of 500 TCW units, and a maximum duration of 4 years.…”

Section: Technical Validationmentioning

confidence: 99%

European primary forest database v2.0

et al. 2021

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Primary forests, defined here as forests where the signs of human impacts, if any, are strongly blurred due to decades without forest management, are scarce in Europe and continue to disappear. Despite these losses, we know little about where these forests occur. Here, we present a comprehensive geodatabase and map of Europe’s known primary forests. Our geodatabase harmonizes 48 different, mostly field-based datasets of primary forests, and contains 18,411 individual patches (41.1 Mha) spread across 33 countries. When available, we provide information on each patch (name, location, naturalness, extent and dominant tree species) and the surrounding landscape (biogeographical regions, protection status, potential natural vegetation, current forest extent). Using Landsat satellite-image time series (1985–2018) we checked each patch for possible disturbance events since primary forests were identified, resulting in 94% of patches free of significant disturbances in the last 30 years. Although knowledge gaps remain, ours is the most comprehensive dataset on primary forests in Europe, and will be useful for ecological studies, and conservation planning to safeguard these unique forests.

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Section: Technical Validationmentioning

confidence: 99%

European primary forest database v2.0

et al. 2021

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“…Four complementary explanations for this finding are plausible. First, natural disturbances, such as from fires or river-bed migrations are common in the Chaco (Adamoli et al, 1990;Bravo et al, 2001;De Marzo et al, 2021). However, disturbance attribution is not always straightforward.…”

Section: Discussionmentioning

confidence: 99%

Frontier metrics for a process-based understanding of deforestation dynamics

Baumann¹,

Gasparri²,

Buchadas³

et al. 2022

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Agricultural expansion into tropical and subtropical forests often leads to major social-ecological trade-offs. Yet, despite ever-more detailed information on where deforestation occurs, how agriculture expands into forests remains unclear. Here, we developed and mapped a novel set of metrics that quantify agricultural frontier processes at unprecedented spatial and temporal detail. Specifically, we first derived consistent time series of land-use/cover to, second, describe archetypical patterns of frontier expansion, pertaining to the speed, the diffusion and activity of deforestation, as well as post-deforestation land use. We exemplify this approach for understanding agricultural frontier expansion across the entire South American Chaco (1.1 million km2), a global deforestation hotspot. Our study provides three major insights. First, agricultural expansion has been rampant in the Chaco, with more than 19.3 million ha of woodlands converted between 1985 and 2020, including a surge in deforestation after 2019. Second, land-use trajectories connected to frontier processes have changed in major ways over the 35-year study period we studied. For instance, while ranching expansion drove most of the deforestation in the 1980s and 1990s, cropland expansion dominated during the mid-2000s in Argentina, but not in Paraguay. Similarly, 40% of all areas deforested were initially used for ranching, but later on converted to cropping. Accounting for post-deforestation land-use change is thus needed to properly attribute deforestation and associated environmental impacts, such as carbon emissions or biodiversity loss, to commodities. Finally, we identified major, recurrent frontier types that may be a useful spatial template for land governance to match policies to specific frontier situations. Collectively, our study reveals the diversity of frontier processes and how frontier metrics can capture and structure this diversity for guiding spatially targeted policies, and for uncovering high-level patterns of human-nature interactions.

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“…Although decision-tree algorithms were found useful in this study, there exists the possibility of testing different machine learning algorithms. For example, Random forest [54], a widely used machine learning algorithm in disturbance-related scientific works [63,64]. Besides, by using such algorithms, the repetition of the modelling steps to select the tree that retrieved better results could be avoided as this algorithm is based on the combination of multiple decision trees.…”

Section: Discussionmentioning

confidence: 99%

Automatic Identification of Forest Disturbance Drivers Based on Their Geometric Pattern in Atlantic Forests

2022

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Monitoring forest disturbances has become essential towards the design and tracking of sustainable forest management. Multiple methodologies have been developed to detect these disturbances. However, few studies have focused on the automatic detection of disturbance drivers, an essential task as each disturbance has different implications for the functioning of the ecosystem and associated management actions. Wildfires and harvesting are two of the major drivers of forest disturbances across different ecosystems. In this study, an automated methodology is presented to automatically distinguish between the two once the disturbance is detected, using the properties of its geometry and shape. A cluster analysis was performed to automatically individualize each disturbance and afterwards calculate its geometric properties. Using these properties, a decision tree was built that allowed for the distinction between wildfires and harvesting with an overall accuracy of 91%. This methodology and further research relating to it could pose an essential aid to national and international agencies for incorporating forest-disturbance-driver-related information into forest-focused reports.

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Characterizing forest disturbances across the Argentine Dry Chaco based on Landsat time series

Cited by 34 publications

References 79 publications

European primary forest database v2.0

European primary forest database v2.0

Frontier metrics for a process-based understanding of deforestation dynamics

Automatic Identification of Forest Disturbance Drivers Based on Their Geometric Pattern in Atlantic Forests

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