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A New Multi-Temporal Forest Cover Classification for the Xingu River

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A New Multi-Temporal Forest Cover Classification for the Xingu River data Data Descriptor A New Multi-Temporal Forest Cover Classification for the Xingu River Basin, Brazil Margaret Kalacska 1,*, Oliver Lucanus 1, Leandro Sousa 2 and J. Pablo Arroyo-Mora 3 1 Applied Remote Sensing Laboratory, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada 2 Flight Research Lab, National Research Council of Canada, Ottawa ON K1A 0R6, Canada 3 Laboratório de Ictiologia de Altamira, Universidade Federal do Pará, Altamira PA 68372040, Brazil * Correspondence: [email protected] Received: 26 June 2019; Accepted: 30 July 2019; Published: 1 August 2019 Abstract: We describe a new multi-temporal classification for forest/non-forest classes for a 1.3 million square kilometer area encompassing the Xingu River basin, Brazil. This region is well known for its exceptionally high biodiversity, especially in terms of the ichthyofauna, with approximately 600 known species, 10% of which are endemic to the river basin. Global and regional scale datasets do not adequately capture the rapidly changing land cover in this region. Accurate forest cover and forest cover change data are important for understanding the anthropogenic pressures on the aquatic ecosystems. We developed the new classifications with a minimum mapping unit of 0.8 ha from cloud free mosaics of Landsat TM5 and OLI 8 imagery in Google Earth Engine using a classification and regression tree (CART) aided by field photographs for the selection of training and validation points. Keywords: Landsat imagery; land cover change; deforestation; biodiversity; conservation; Xingu river basin 1. Summary Ongoing deforestation in the tropics is a well-known threat to biodiversity [1–3]. Species with restricted ranges are particularly at risk of extinction from habitat loss [4]. Global- and regional-scale maps developed to analyze large-scale trends (e.g., [5–8]) do not necessarily capture the small-scale changes experienced by restricted range species. Therefore, these areas require the development of detailed land cover datasets that allow for a better understanding of historical and current environmental threats (e.g., deforestation, mining, etc.). In addition to their high biodiversity, Amazonian forests are also important contributors to drivers of global atmospheric circulation [9] and climate change mitigation [10]. The Xingu River basin encompasses the fourth largest catchment area of the Amazon. It is known for its high fish biodiversity, with an estimated 600 species, 10% of them endemic to this river basin with many remaining to be described [11–14]. Several ethnic groups inhabit a mosaic of indigenous lands in a large part of the basin, contributing greatly to a slower rate of deforestation than is seen in unprotected Xingu basin headwaters [15–17]. The health of the riparian ecosystems is linked to changes in the overall forest cover and has a direct impact the unique fish biota [18]. In order to assess the rapid changes that the Xingu River basin has experienced over the last four decades, we developed a forest/non-forest classification system for four time periods from 1989 to 2018 as a case study applicable elsewhere in the tropics. These datasets were produced from cloud free surface reflectance mosaics of Landsat TM5 and Landsat 8-OLI images, classified with a classification and regression tree (CART) in Google Earth Engine. Data 2019, 4, 114; doi:10.3390/data4030114 www.mdpi.com/journal/data Data 2019, 4, x FOR PEER REVIEW 2 of 9 reflectance mosaics of Landsat TM5 and Landsat 8-OLI images, classified with a classification and regressionData 2019, 4, 114tree (CART) in Google Earth Engine. 2 of 8 2. Data Description 2. Data Description The four classifications are available for download in Geotiff format (30 m pixel size) (Figure 1). Each Theclassification four classifications is in a geographic are available projection for download (latitude/longitude) in Geotiff format with (30 mWGS84 pixel size)datum. (Figure Pixels1). representEach classification one of three is in classes: a geographic forest projection (1), water (latitude (2), or non-forest/longitude) (3). with Our WGS84 definition datum. of Pixelsforest representis “areas withone ofapproximately three classes: greater forest (1),than water 30% (2),tree or canopy non-forest cover”, (3). which Our definition is consiste ofnt forest with isthe “areas Brazilian with definitionapproximately [19]. greater The implementation than 30% tree canopy of this cover”, definition which iswas consistent based withon a thepproximately Brazilian definition 3000 field [19]. photographsThe implementation (see Section of this 3). definition The non-forest was based class on encompasses approximately all 3000 land field covers photographs and land (see uses Section that are3). notThe considered non-forest classforest encompasses or open water. all The land wa coverster class and represents land uses thatexposed are not surface considered water. forest or open water. The water class represents exposed surface water. (a) (b) Figure 1. Cont. DataData2019 2019, 4,, 1144, x FOR PEER REVIEW 3 of3 9 of 8 (c) (d) FigureFigure 1. 1.Four Four classifications classificationsof of forestforest covercover for the Xingu Xingu river river basin basin for for (a ()a circa) circa 1989, 1989, (b) ( bcirca) circa 2000, 2000, (c)( circac) circa 2010, 2010, and and (d ()d circa) circa 2018 2018 from from LandsatLandsat TM5TM5 and OLI OLI 8 8 im imagery.agery. Boundaries Boundaries of ofstates, states, larger larger rivers,rivers, and and landmarks landmarks including including cities cities andand thethe JericoJericoáá rapirapidsds are are shown shown for for reference. reference. 3.3. Methods Methods TheThe forest forest cover cover classifications classifications werewere createdcreated for a 1.3M km km22 areaarea encompassing encompassing the the Xingu Xingu River River basin,basin, Brazil, Brazil, representing representing circa circa 1989,1989, 2000,2000, 2010, and 2018 2018 from from Landsat Landsat imagery imagery (TM5 (TM5 and and OLI OLI 8) 8) (Table(Table1). The1). The area area crosses crosses the the borders borders of five of five states: states: Par áPará,, Mato Mato Grosso, Grosso, Amazonas, Amazonas, Tocantins, Tocantins, and and Goi ás (FigureGoiás1 ).(Figure The USGS1). The Landsat USGS SurfaceLandsat ReflectanceSurface Reflectance Tier 1 imagery Tier 1 imagery collection collection was queried was queried through Googlethrough Earth Google Engine Earth (Table Engine1). Clouds(Table 1). and Clouds cloud an shadowsd cloud shadows were masked were masked using the using pixel the QA pixel bands QA to createbands a cloudto create free a compositecloud free compos of the medianite of the surface median reflectance surface reflectance for each for reference each reference period. period. In order In to achieveorder to cloud achieve free cloud composites free composites for such for a large such areaa large in area the tropicsin the tropics which which is prone is prone to persistent to persistent cloud cover,cloud the cover, composites the composites represent represent periods periods spanning spanning 1985–1989, 1985–1989, 1995–2000, 1995–2000, 2005–2010, 2005–2010, and and 2017–2018. 2017– 2018. A classification and regression tree (CART) [20] was trained in Google Earth Engine for each A classification and regression tree (CART) [20] was trained in Google Earth Engine for each mosaic, mosaic, based on user-generated points split 70/30 for training and validation. based on user-generated points split 70/30 for training and validation. For the most recent mosaic, the training and validations points were selected based on For the most recent mosaic, the training and validations points were selected based on photographs photographs taken during four field expeditions in 2015–2018. The photographs were taken along taken during four field expeditions in 2015–2018. The photographs were taken along the Xingu River the Xingu River (by boat) from Vitória do Xingu, north of São Félix do Xingu (400 km straight line (bydistance boat) from to the Vit south)ória do(Figure Xingu, 2). Additional north of S ãphootographs Félix do Xinguwere taken (400 along km straight the PA-415 line road distance between to the south)Altamira (Figure and2 ).Vitória Additional do Xingu photographs and along werethe BR taken 230 (Transamazônica) along the PA-415 highway road between between Altamira Altamira and Vitandória an do unnamed Xingu and settlement along the near BR 230the (Transamazriver’s naturalônica) diversion highway to the between west, above Altamira the entrance and an unnamed to the settlementVolta Grande, near thewhere river’s the river natural drains diversion from the to Braz theilian west, shield above to thethe entranceAmazonian to lowlands. the Volta In Grande, the wheresouth, the photographs river drains were from taken the Brazilian along primary shield toroads the and Amazonian rivers (Culuene, lowlands. Suiá In Missu, the south, Darro, photographs Curuá) werein takenan area along bounded primary by roadsCanarana and rivers(SE), (Culuene,Sorriso (SW), Sui áCasteloMissu, Darro,do Sonhos Curu (NW),á) in an and area halfway bounded bydownstream Canarana (SE), the SorrisoSuiá Missu (SW), towards Castelo its do confluence Sonhos (NW), with the and Xingu halfway River downstream (NE). The thephotographs Suiá Missu towardsallowed its for confluence an understanding with the Xingu of the River landscape (NE). Thefrom photographs the satellite allowedimagery forin anorder understanding to improve of the landscape from the
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