references.bib

% This file was created with Citavi 6.8.0.0

@article{Appel.2019,
 author = {Appel, Marius and Pebesma, Edzer},
 year = {2019},
 title = {On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library},
 url = {https://www.mdpi.com/2306-5729/4/3/92},
 pages = {92},
 volume = {4},
 number = {3},
 journal = {Data},
 doi = {10.3390/data4030092},
 file = {4d7c8d61-7bac-41af-b423-c1cd9bad8f27:C\:\\Users\\janni\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\yer1dcgzjik2kwsdp2vnf0pl2oj5bpkes3cbfz9fvrn\\Citavi Attachments\\4d7c8d61-7bac-41af-b423-c1cd9bad8f27.pdf:pdf;898c0bd3-ab5f-4fc2-a8de-c5ebd6449293:C\:\\Users\\janni\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\yer1dcgzjik2kwsdp2vnf0pl2oj5bpkes3cbfz9fvrn\\Citavi Attachments\\898c0bd3-ab5f-4fc2-a8de-c5ebd6449293.pdf:pdf}
}


@article{Spruce.2011,
 author = {Spruce, Joseph P. and Sader, Steven and Ryan, Robert E. and Smoot, James and Kuper, Philip and Ross, Kenton and Prados, Donald and Russell, Jeffrey and Gasser, Gerald and McKellip, Rodney},
 year = {2011},
 title = {Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks},
 url = {https://www.sciencedirect.com/science/article/pii/s0034425710002865},
 pages = {427--437},
 volume = {115},
 number = {2},
 issn = {0034-4257},
 journal = {Remote Sensing of Environment},
 doi = {10.1016/j.rse.2010.09.013}
}


@article{Rogelj.2016,
 abstract = {The Paris climate agreement aims at holding global warming to well below 2 degrees Celsius and to {\textquotedbl}pursue efforts{\textquotedbl} to limit it to 1.5 degrees Celsius. To accomplish this, countries have submitted Intended Nationally Determined Contributions (INDCs) outlining their post-2020 climate action. Here we assess the effect of current INDCs on reducing aggregate greenhouse gas emissions, its implications for achieving the temperature objective of the Paris climate agreement, and potential options for overachievement. The INDCs collectively lower greenhouse gas emissions compared to where current policies stand, but still imply a median warming of 2.6-3.1 degrees Celsius by 2100. More can be achieved, because the agreement stipulates that targets for reducing greenhouse gas emissions are strengthened over time, both in ambition and scope. Substantial enhancement or over-delivery on current INDCs by additional national, sub-national and non-state actions is required to maintain a reasonable chance of meeting the target of keeping warming well below 2 degrees Celsius.},
 author = {Rogelj, Joeri and den Elzen, Michel and H{\"o}hne, Niklas and Fransen, Taryn and Fekete, Hanna and Winkler, Harald and Schaeffer, Roberto and Sha, Fu and Riahi, Keywan and Meinshausen, Malte},
 year = {2016},
 title = {Paris Agreement climate proposals need a boost to keep warming well below 2 °C},
 pages = {631--639},
 volume = {534},
 number = {7609},
 issn = {1476-4687},
 journal = {Nature},
 doi = {10.1038/nature18307},
 file = {http://www.ncbi.nlm.nih.gov/pubmed/27357792},
 file = {707078b9-8aca-481b-815e-dfc9db753a55:C\:\\Users\\janni\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\yer1dcgzjik2kwsdp2vnf0pl2oj5bpkes3cbfz9fvrn\\Citavi Attachments\\707078b9-8aca-481b-815e-dfc9db753a55.pdf:pdf}
}


@misc{RCoreTeam.2020,
 author = {{R Core Team}},
 year = {2020},
 title = {R: A Language and Environment for Statistical Computing},
 url = {https://www.R-project.org/},
 address = {Vienna, Austria},
 institution = {{R Foundation for Statistical Computing}}
}


@misc{QGISDevelopmentTeam.2021,
 author = {{QGIS Development Team}},
 year = {2021},
 title = {QGIS Geographic Information System},
 url = {https://www.qgis.org}
}


@misc{Pebesma.2021,
 author = {Pebesma, Edzer},
 year = {2021},
 title = {stars: Spatiotemporal Arrays, Raster and Vector Data Cubes}
}


@misc{Pebesma.2021b,
 author = {Pebesma, Edzer},
 year = {2021},
 title = {st2.Rmd},
 url = {https://github.com/edzer/astd/blob/master/st2.Rmd},
 address = {GitHub},
 urldate = {20.03.2021},
 file = {https://github.com/edzer/astd/blob/master/st2.Rmd}
}


@misc{Pebesma.2021c,
 author = {Pebesma, Edzer},
 year = {2021},
 title = {st.Rmd},
 url = {https://github.com/edzer/astd/blob/master/st.Rmd},
 address = {GitHub},
 urldate = {20.03.2021}
}


@article{Mitchell.2017,
 abstract = {Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.},
 author = {Mitchell, Anthea L. and Rosenqvist, Ake and Mora, Brice},
 year = {2017},
 title = {Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD},
 pages = {9},
 volume = {12},
 number = {1},
 issn = {1750-0680},
 journal = {Carbon Balance and Management},
 doi = {10.1186/s13021-017-0078-9},
 file = {http://www.ncbi.nlm.nih.gov/pubmed/28417324},
 file = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5393981},
 file = {48b7f9f6-642d-42e8-9df4-23b31a9afe89:C\:\\Users\\janni\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\yer1dcgzjik2kwsdp2vnf0pl2oj5bpkes3cbfz9fvrn\\Citavi Attachments\\48b7f9f6-642d-42e8-9df4-23b31a9afe89.pdf:pdf}
}


@misc{Meyer.2021,
 author = {Meyer, Hanna},
 year = {2021},
 title = {CAST: 'caret' Applications for Spatial-Temporal Models},
 url = {https://CRAN.R-project.org/package=CAST}
}


@article{Malhi.2008,
 abstract = {The forest biome of Amazonia is one of Earth's greatest biological treasures and a major component of the Earth system. This century, it faces the dual threats of deforestation and stress from climate change. Here, we summarize some of the latest findings and thinking on these threats, explore the consequences for the forest ecosystem and its human residents, and outline options for the future of Amazonia. We also discuss the implications of new proposals to finance preservation of Amazonian forests.},
 author = {Malhi, Yadvinder and Roberts, J. Timmons and Betts, Richard A. and Killeen, Timothy J. and Li, Wenhong and Nobre, Carlos A.},
 year = {2008},
 title = {Climate change, deforestation, and the fate of the Amazon},
 pages = {169--172},
 volume = {319},
 number = {5860},
 issn = {1095-9203},
 journal = {Science},
 doi = {10.1126/science.1146961},
 file = {http://www.ncbi.nlm.nih.gov/pubmed/18048654},
 file = {9ce11950-c674-431b-b86f-67eb9fd547d2:C\:\\Users\\janni\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\yer1dcgzjik2kwsdp2vnf0pl2oj5bpkes3cbfz9fvrn\\Citavi Attachments\\9ce11950-c674-431b-b86f-67eb9fd547d2.pdf:pdf}
}


@article{Lovejoy.2019,
 abstract = {Thomas E. LovejoyCarlos NobreAlthough 2019 was not the worst year for fire or deforestation in the Amazon, it was the year when the extent of fires and deforestation in the region garnered full global attention. This year, the winds brought smoke from the fires into S{\~a}o Paulo, so thick that---after encountering moist air blowing from the ocean---the afternoon skies were darkened and street lights had to be lit 3 hours early for the city to continue to function. The rampant winds awoke the Brazilian populace and indeed the world to the harsh reality that the precious Amazon is teetering on the edge of functional destruction and, with it, so are we.For more than 50 years, scientists and policy makers have known unequivocally ( 1 ) that the hydrological cycle of the Amazon depends squarely on the transpiration of the forest's multitude of leaves and on the evaporation from the complex surfaces of the rain forest. When it rains on the Amazonian forest landscape, at least 75{\%} of the moisture is returned to the westward-moving air mass. The rainforest recycles the moisture five to six times before it turns southward, feeling the proximity of the high wall of the Andes. Over the whole basin, the air rises, cools, and precipitates out close to 20{\%} of the world's river water in the Amazon river system.The moisture of the Amazon is not confined to the basin $\ldots$},
 author = {Lovejoy, Thomas E. and Nobre, Carlos},
 year = {2019},
 title = {Amazon tipping point: Last chance for action},
 url = {https://advances.sciencemag.org/content/5/12/eaba2949?intcmp=trendmd-adv},
 pages = {eaba2949},
 volume = {5},
 number = {12},
 issn = {2375-2548},
 journal = {Science Advances},
 doi = {10.1126/sciadv.aba2949},
 file = {161d1837-5197-4503-aa9b-5102650f0837:C\:\\Users\\janni\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\yer1dcgzjik2kwsdp2vnf0pl2oj5bpkes3cbfz9fvrn\\Citavi Attachments\\161d1837-5197-4503-aa9b-5102650f0837.pdf:pdf}
}


@article{Lenton.2008,
 abstract = {The term {\textquotedbl}tipping point{\textquotedbl} commonly refers to a critical threshold at which a tiny perturbation can qualitatively alter the state or development of a system. Here we introduce the term {\textquotedbl}tipping element{\textquotedbl} to describe large-scale components of the Earth system that may pass a tipping point. We critically evaluate potential policy-relevant tipping elements in the climate system under anthropogenic forcing, drawing on the pertinent literature and a recent international workshop to compile a short list, and we assess where their tipping points lie. An expert elicitation is used to help rank their sensitivity to global warming and the uncertainty about the underlying physical mechanisms. Then we explain how, in principle, early warning systems could be established to detect the proximity of some tipping points.},
 author = {Lenton, Timothy M. and Held, Hermann and Kriegler, Elmar and Hall, Jim W. and Lucht, Wolfgang and Rahmstorf, Stefan and Schellnhuber, Hans Joachim},
 year = {2008},
 title = {Tipping elements in the Earth's climate system},
 url = {https://www.pnas.org/content/105/6/1786.short},
 keywords = {climate change;climate policy;Earth system;large-scale impacts;tipping points},
 pages = {1786--1793},
 volume = {105},
 number = {6},
 issn = {1091-6490},
 journal = {Proceedings of the National Academy of Sciences},
 doi = {10.1073/pnas.0705414105},
 file = {http://www.ncbi.nlm.nih.gov/pubmed/18258748},
 file = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2538841},
 file = {3d26deb6-f89b-418f-ba4b-0e61faca7e3f:C\:\\Users\\janni\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\yer1dcgzjik2kwsdp2vnf0pl2oj5bpkes3cbfz9fvrn\\Citavi Attachments\\3d26deb6-f89b-418f-ba4b-0e61faca7e3f.pdf:pdf}
}


@book{Wikle.2019,
 abstract = {The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these {\textquotedbl}big data{\textquotedbl} that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps.{\textless}/p{\textgreater}

Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book:{\textless}/p{\textgreater} {\textless}ol{\textgreater}

{\textless}/p{\textgreater}{\textless}/ol{\textgreater}{\textless}ul{\textgreater} {\textless}li{\textgreater}Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation{\textless}/li{\textgreater} {\textless}li{\textgreater}Provides a gradual entry to the methodological aspects of spatio-temporal statistics{\textless}/li{\textgreater}{\textless}li{\textgreater}Provides broad coverage of using R as well as {\textquotedbl}R Tips{\textquotedbl} throughout.{\textless}/li{\textgreater} {\textless}li{\textgreater}Features detailed examples and applications in end-of-chapter Labs{\textless}/li{\textgreater} {\textless}li{\textgreater}Features {\textquotedbl}Technical Notes{\textquotedbl} throughout to provide additional technical detail where relevant{\textless}/li{\textgreater} {\textless}li{\textgreater}Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more{\textless}/li{\textgreater} {\textless}/ul{\textgreater}{\textless}ol{\textgreater}

{\textless}/p{\textgreater}{\textless}/ol{\textgreater}

{\textless}/p{\textgreater}

The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.{\textless}/p{\textgreater}},
 author = {Wikle, Christopher K. and Zammit-Mangion, Andrew and Cressie, Noel},
 year = {2019},
 title = {Spatio-Temporal Statistics with R},
 address = {Milton},
 publisher = {{Chapman and Hall/CRC}},
 isbn = {9780429649783},
 series = {Chapman and Hall/CRC the R Ser}
}


@article{Lehmann.2013,
 author = {Lehmann, Eric A. and Wallace, Jeremy F. and Caccetta, Peter A. and Furby, Suzanne L. and Zdunic, Katherine},
 year = {2013},
 title = {Forest cover trends from time series Landsat data for the Australian continent},
 pages = {453--462},
 volume = {21},
 issn = {0303-2434},
 journal = {International Journal of Applied Earth Observation and Geoinformation},
 doi = {10.1016/j.jag.2012.06.005}
}


@misc{Kuhn.2020,
 author = {Kuhn, Max},
 year = {2020},
 title = {caret: Classification and Regression Training},
 url = {https://CRAN.R-project.org/package=caret}
}


@article{King.2017,
 author = {King, David and Schrag, Daniel and Dadi, Zhou and Ye, Qi and Ghosh, Arunabha},
 year = {2017},
 title = {Climate change: a risk assessment}
}


@misc{JeffreyA.Ryan.2020,
 author = {{Jeffrey A. Ryan} and {Joshua M. Ulrich}},
 year = {2020},
 title = {xts: eXtensible Time Series},
 url = {https://CRAN.R-project.org/package=xts}
}


@article{HojasGascon.2015,
 author = {Hojas-Gascon, L. and Belward, A. and Eva, H. and Ceccherini, G. and Hagolle, O. and Garcia, J. and Cerutti, P.},
 year = {2015},
 title = {Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program},
 pages = {417--423},
 volume = {XL-7/W3},
 issn = {2194-9034},
 journal = {ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
 doi = {10.5194/isprsarchives-xl-7-w3-417-2015},
 file = {4a45690e-3db8-4d5e-9055-60b810a52461:C\:\\Users\\janni\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\yer1dcgzjik2kwsdp2vnf0pl2oj5bpkes3cbfz9fvrn\\Citavi Attachments\\4a45690e-3db8-4d5e-9055-60b810a52461.pdf:pdf}
}


@misc{Hijmans.2020,
 author = {Hijmans, Robert J.},
 year = {2020},
 title = {raster: Geographic Data Analysis and Modeling},
 url = {https://CRAN.R-project.org/package=raster}
}


@article{Finer.2018,
 abstract = {Tropical forests are critically important for human livelihoods, climate stability, and biodiversity conservation but remain threatened ([ 1 ][1]). Recent years have seen major strides in documenting historical and annual tropical forest loss with satellites ([ 2 ][2]). Now, a convergence of satellite technologies and analytical capabilities makes it increasingly possible to monitor deforestation in near real time, on the scale of days, weeks, or months, rather than years ([ 3 ][3], [ 4 ][4]). This advance creates greater potential for near--real-time action as well and could play a key role in achieving local, national, and international forest, biodiversity, and climate policy goals, as there is a global imperative to address deforestation. Challenges remain, however, to attaining effective policy action based on the new technology. On the basis of lessons learned from pioneering work in Brazil and Peru, we suggest at least two key factors for successfully linking the technical and policy realms. On the technical side, it is critical to capitalize on continually improving satellite technology to better detect, understand, and prioritize deforestation events. On the policy side, institution building, along with related civil-society engagement, is needed to facilitate effective action within complex government frameworks. We outline a five-step protocol for near--real-time tropical deforestation monitoring, with the goal of bridging the gap between technology and policy.The number of Earth observation satellites, and the quality and accessibility of the imagery they provide, has greatly improved in recent years (see the first figure) ([ 5 ][5]), making satellite imagery the most consistent and effective tool for large-scale forest monitoring. Satellite-based monitoring has four key considerations: spatial resolution, temporal resolution, sensor type, and data access. Spatial resolution (that is, pixel size) has been steadily increasing since the 1970s (see the first figure), trending from coarse ({\textgreater}250 m) to medium (10 to 30 m) to high ({\textless}5 m). Temporal resolution (frequency of imagery for any given location) has also improved. Until recently, there had been a trade-off between spatial and temporal resolution, with higher-resolution sensors covering less area per day. For example, NASA's coarse-resolution MODIS (Moderate Resolution Imaging Spectroradiometer) sensor collects optical imagery of every point daily, whereas medium-resolution Landsat has a revisit time of 16 days. However, constellations of miniature satellites (such as the 175 satellites of the company Planet) address the trade-off, by providing high-resolution (3 m) optical imagery with near-daily frequency ([ 6 ][6]).![Figure][7]{\textless}/img{\textgreater}Trends in earth observation satellitesData reflect 488 earth observation satellites launched since 1972 by commercial and government providers (excluding military). We followed methods established in ([ 5 ][5]) and added satellites from the Union of Concerned Scientists database and public launch information from SpaceFlightNow and Planet. See the supplementary materials for details.GRAPHIC: ADAPTED BY KELLIE HOLOSKI/ SCIENCE Though cloud cover often limits the availability of usable optical satellite imagery, radar sensors can penetrate clouds. The European Space Agency's new Sentinel-1 satellites offer medium-resolution radar data on a regular basis for every point on Earth (for example, every 12 days in the Amazon), regardless of the weather conditions.Cost can limit access to some imagery. Although coarse- and medium-resolution imagery from U.S. and European governments is freely available (MODIS, Landsat, and Sentinel), high-resolution imagery from companies such as Planet (including RapidEye), DigitalGlobe, and Airbus is available commercially and tends to be costly. However, the public- versus commercial-access gap is closing. For example, Europe's new Sentinel-2 satellite freely offers 10-m-resolution optical imagery every 4 days ([ 7 ][8]), and Sentinel-1 is the first radar satellite that provides freely available data. Planet now displays monthly 5-m-resolution cloud-free mosaics online.Along with improvements in resolution and access, increased computing power, such as by Google Earth Engine, has enabled effective processing of the massive amounts of satellite data.The challenge remains to harness improving satellite technology and unprecedented data streams into a strategic and effective monitoring system that can ultimately be used by decision-makers to reduce deforestation. We propose a five-step near--real-time deforestation-monitoring protocol designed primarily for government and civil society. This comprehensive protocol, based on recent monitoring initiatives in the Amazon ([ 8 ][9], [ 9 ][10]) and insights from the international Global Forest Watch partnership, is particularly aimed at tropical countries that are designing strategies to confront deforestation. One of our main motivations is to help convert cutting-edge technological data into actionable information, a transformation that has thus far been lacking on a global scale.{\#}{\#}{\#} Forest loss detectionThe first step is to detect forest loss as precisely and quickly as possible. Advances in automated forest-loss detection have followed those of satellites. The original near--real-time detection systems, starting in the early 2000s, utilized coarse-resolution MODIS imagery, including well-known systems in Brazil [DETER (Real-Time System for Detection of Deforestation; government) and SAD (Deforestation Alert System; civil society)] and Terra-i and FORMA (Forest Monitoring for Action) from international organizations.More recently, detection systems have incorporated medium-resolution Landsat imagery. In 2009, researchers at the Carnegie Institution for Science developed CLASlite (Carnegie Landsat Analysis System--Lite), pioneering Landsat-based forest-loss detection software. In 2016, researchers at the University of Maryland developed GLAD (Global Land Analysis and Discovery), the first fully automated, Landsat-based alert system ([ 10 ][11]). In 2017, the Peruvian government, which previously used GLAD, developed its own Landsat-based alert system.Most recently, the Brazilian SAD alerts were further innovated by incorporating Sentinel imagery, both optical and radar. Thus, sensing capability for automated forest-loss alerts has improved resolution by two orders of magnitude (from 1000 to 10 m) in less than a decade and can see through clouds. Future alerts may improve to 3 m, with Planet imagery.Given the widespread nature of small-scale deforestation events ([ 11 ][12]), medium-resolution (Landsat and Sentinel)--based alerts have replaced coarse-resolution (MODIS)--based alerts as the standard. As a practical option, GLAD alerts now cover more than 20 tropical countries, essentially providing pantropical coverage. These alerts are free datasets, updated weekly, that indicate 30 m--by--30 m pixels of likely forest loss. For Brazil and Peru, there are also more specialized alerts.{\#}{\#}{\#} Prioritization of dataAlerts may yield thousands, even millions, of raw data points (pixels) on a regular basis and can quickly become overwhelming, especially at larger scales. Thus, prioritizing alert data is often necessary, for example, by incorporating important spatial data such as protected areas, indigenous territories, or any specific area of interest. Kernel density analysis identifies deforestation hot spots, allowing users to focus on geographic areas with the highest concentrations of recent forest loss ([ 12 ][13]). Other prioritization techniques include visually scanning for large clusters or linear features that indicate anthropogenic clearing and adding river and terrain information to screen for forest loss that is likely due to natural causes. In the future, machine learning may aid prioritization by highlighting the most important alerts on the basis of pattern (size and density), shape (linear), and location (overlap with areas of interest).{\#}{\#}{\#} Identification of driversTo convert priority alerts to actionable information, they need to be validated, to verify that the highlighted pixels are indeed deforestation, and investigated, to determine the driver (cause).Alerts can typically be validated by visually checking the underlying medium-resolution imagery (Landsat or Sentinel). Also, new mobile apps, such as Forest Watcher, facilitate field verification. Drones, which can fly targeted missions under cloud cover, will likely play increasing roles in providing local information.After validation, visual analysis of high-resolution imagery is an effective and efficient technique to determine the driver. Our protocol focuses on identification of the direct driver, the immediate action at the local level that is causing the forest loss. For example, important direct drivers in the Amazon include agriculture (both large and small scale), logging roads, and gold mining (see second figure). In turn, identification of the direct driver aids identification of the indirect driver, such as a market force, that often determines the appropriate policy action. For example, the response to a new mining operation will be different, and involve different government agencies, than the response to a new oil palm plantation.In cases where high-resolution imagery is cost-prohibitive, there are improving free alternatives. At 10-m resolution, Sentinel-2 is sufficient to distinguish most drivers. Further, Planet now offers 5-m-resolution visual maps online, updated monthly.In the future, machine learning could greatly enhance identification of major deforestation drivers (such as mining and agriculture) and even identify specific crop types (oil palm, cacao, coffee, soy, and so on) from satellite imagery.{\#}{\#}{\#} Timely communication of resultsThe next challenge is to communicate important results when traditional methods, such as published reports or peer-reviewed articles, are not time-effective options. Although the operative audience is government officials, as described in the next step below, there is scientific, educational, and governance (transparency) value in reaching a wider public audience that includes civil society, journalists, researchers, and the private sector. It is often difficult to predict the exact group of actors that will seize on any given set of new findings, so it is strategic to cast a wide net, as this step is important in bridging the gap between the technical and policy components.Civil society has developed approaches that may serve as a model. Launched in 2015, Monitoring of the Andean Amazon Project (MAAP) specializes in publishing concise, timely, web-based reports about ongoing near--real-time monitoring efforts in the Andean Amazon. Reports often feature high-resolution imagery both before and after recent deforestation events. All reports are reviewed by preselected expert committees before publication. Particularly sensitive findings, such as illegal activity that may be subject to an intervention, are passed directly to relevant government entities before being made public.Global Forest Watch has recently built off this approach, expanding to a global scale with the ``Places to Watch'' initiative ([ 13 ][14]). Brief, web-based reports highlight the most consequential cases of recent deforestation identified by GLAD alerts around the tropics.{\#}{\#}{\#} Impact to reduce deforestationThe ultimate goal is that the steps above lead to actual policy or conservation action. On the basis of recent experience in the Amazon, first in Brazil and now also in Peru, effective government-institution building is the key element to ensuring that near--real-time forest monitoring information is utilized.![Figure][7]{\textless}/img{\textgreater}Detecting deforestation and identifying drivers in the Peruvian AmazonDetecting forest loss with Landsat-based GLAD alerts (top) and identifying deforestation drivers with high spatial and temporal resolution Planet imagery (bottom).IMAGES: (TOP ROW) GLOBAL FOREST WATCH; (BOTTOM ROW) PLANETStreamlined coordination between the array of government ministries and agencies to process and respond to the technical information is critical. In a pioneering effort, the effective use of MODIS-based deforestation alerts (DETER) was widely credited with helping to curb surging deforestation in the Brazilian Amazon in the early 2000s. Notably, the Brazilian government increased its capacity to physically and legally respond to the alerts by improving coordination across 15 ministries (including the federal police, army, and public prosecutor) ([ 14 ][15], [ 15 ][16]).Similar institution building is now taking place in Peru, which is constructing a National System of Monitoring and Control, led by the National Forest Service and Wildlife Authority (SERFOR), to coordinate actions related to the generation, analysis, and response to deforestation information among government entities. This system is specifically designed to build the government infrastructure for efficient, data-driven policy action, including laying out responsibilities and pathways for the complex array of actors. Notably, in addition to generating and receiving forest-loss information (that is, detection), SERFOR conducts both a technical and legal analysis of the data before sending the information to decision-makers. This technical and legal analysis also provides detailed guidance to authorities on how to prioritize deforestation events, on the basis of factors such as driver, area, and land designation, to facilitate prompt decision-making.Although Brazil and Peru are the most advanced examples, other countries are making progress as well. Colombia is now producing quarterly early-warning reports to identify the most-active deforestation fronts across the country. These reports are an input for the strengthening of coordinated actions between the environment ministry, regional authorities, and the military. The Indonesian government is also using near--real-time monitoring data as an additional source of information to improve law enforcement in the forestry sector.Civil society has also emerged as a key actor related to institution building. Brazil, again, led the way with the development of an independent alert system (SAD) in addition to the government-generated alerts. This independent data generation and analysis by civil society set an important precedent, with implications for improving transparency, spurring innovation, and creating public pressure.In Peru, the civil society--designed protocol presented in this paper served as an initial model for the emerging National System of Monitoring and Control. The protocol was modified and expanded on to fit Peru's complex government structure but serves as a good example of how civil society, and the protocol itself, may assist institution building. In Uganda, civil society (led by the Jane Goodall Institute) is training government rangers to use GLAD alerts to identify and respond to new deforestation fronts.Advancing satellite technology has made near--real-time deforestation monitoring increasingly feasible for a growing number of countries and actors. Our protocol may serve as a foundation for efforts to effectively link the technological advances with the ultimate goal of policy action to reduce deforestation.[www.sciencemag.org/content/360/6395/1303/suppl/DC1][17]1.  [↵][18]1.   J. Barlow et al        ., Nature 535, 144 (2016).        [OpenUrl][19]2.  [↵][20]1.   M. Hansen et al        ., Science 342, 850 (2013).        [OpenUrl][21][Abstract/FREE Full Text][22]3.  [↵][23]1.   J. Lynch,     2.   M. Maslin,     3.   H. Balzter,     4.   M. Sweeting        , Nature 496, 293 (2013).        [OpenUrl][24]4.  [↵][25]1.   A. K. Pratihast et al        ., PLOS ONE 11, e0150935 (2016).        [OpenUrl][26]5.  [↵][27]1.   A. S. 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Harris et al        ., Environ. Res. Lett. 12, 024012 (2017).        [OpenUrl][43][CrossRef][44]13. [↵][45]1.   M. J. Weisse,     2.   R. Petersen,     3.   S. Sargent,     4.   S. Gibbes        , ``Places to watch: Identifying high-priority forest disturbance from near-real time satellite data'' (World Resources Institute, Washington, DC, 2017).        14. [↵][46]1.   D. Nepstad et al        ., Science 344, 1118 (2014).        [OpenUrl][47][Abstract/FREE Full Text][48]15. [↵][49]1.   J. Assun{\c{c}}{\~a}o,     2.   C. Gandour,     3.   R. Rocha        , Environ. Dev. Econ. 20, 697 (2015).        [OpenUrl][50]Acknowledgments: This work was partially funded by the International Conservation Fund of Canada and the Norwegian Agency for Development Cooperation. We thank C. Garcia, J. Swenson, and M. Silman for reviewing the manuscript. 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 journal = {Educational and Psychological Measurement},
 doi = {10.1177/001316446002000104},
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}