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The rise of forest plantations in Chile's Mapuche's homeland: Four decades of land cover estimates from a CNN-RNN model and the Landsat program

https://doi.org/10.2139/ssrn.3938635
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23/23 checkable references clean · checked 2026-08-07

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

16 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 23 checked references that resolve
resolves10.1016/j.worlddev.2005.10.011
Globalization, local ecosystems, and the rural poor
resolves10.1016/j.isprsjprs.2017.05.002
Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks
resolves10.1073/pnas.1917874117
Collective property rights reduce deforestation in the Brazilian Amazon
resolves10.1016/j.rse.2015.08.006
Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems
resolves10.1016/j.rse.2009.01.007
Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors
resolves10.1007/s13280-016-0768-7
International trade causes large net economic losses in tropical countries via the destruction of ecosystem services
resolves10.3390/rs10111746
A Two-Branch CNN Architecture for Land Cover Classification of PAN and MS Imagery
resolves10.1016/j.apgeog.2016.07.014
A plantation-dominated forest transition in Chile
resolves10.1016/j.rse.2016.02.028
A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research
resolves10.1016/j.jhydrol.2009.06.011
Revealing the impact of forest exotic plantations on water yield in large scale watersheds in South-Central Chile
resolves10.1109/IGARSS.2018.8517375
A Recurrent Convolutional Neural Network for Land Cover Change Detection in Multispectral Images
resolves10.1007/s10668-014-9519-8
Oil palm plantation investments in Indonesia’s forest frontiers: limited economic multipliers and uncertain benefits for local communities
resolves10.1109/TGRS.2019.2899955
$\mathcal{R}^2$ -CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images
resolves10.1109/TGRS.2003.811693
Assessment of different topographic corrections in landsat-TM data for mapping vegetation types (2003)
resolves10.3390/rs1030184
Comparison of Topographic Correction Methods
resolves10.5354/0719-5370.2007.27761
Cambios territoriales y efectos producidos por la industria forestal sobre el anclaje de las comunidades locales en la Cuenca del Itata Chile Central
resolves10.1016/j.neunet.2018.05.019
Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks
resolves10.1109/LGRS.2019.2893306
Deep Learning for Multilabel Land Cover Scene Categorization Using Data Augmentation
resolves10.1080/01431161.2018.1516313
Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series
resolves10.3390/ijgi8040189
Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using CNN-Based Classification
resolves10.1016/j.rse.2016.05.016
Detailed dynamic land cover mapping of Chile: Accuracy improvement by integrating multi-temporal data
resolves10.1109/MGRS.2017.2762307
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
resolves10.1016/j.rse.2014.12.014
Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images
The 16 references without a DOI — listed, not checked
no DOI — not checkedref5
no DOI — not checkedref8
no DOI — not checkedLandsat tm-based forest damage assessment: correction for topographic effects
no DOI — not checkedThe shuttle radar topography mission
no DOI — not checkedEstad �sticas forestales
no DOI — not checkedref14
no DOI — not checkedref15
no DOI — not checkedref16
no DOI — not checkedref18
no DOI — not checkedIndustria forestal en el sur de chile. Transformaciones en comunidades campesinas de la regi�n de Los R�os
no DOI — not checkedChile: Efectos de la banda de precios de importaci�n de trigo
no DOI — not checkedSmall object detection in optical remote sensing images via modified faster r-cnn
no DOI — not checkedA new end-to-end multi-dimensional cnn framework for land cover/land use change detection in multi-source remote sensing datasets
no DOI — not checkedref31
no DOI — not checkedref34
no DOI — not checkedref39
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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