restauraRapp – An R package to subsidize forest restoration planning in Riparian Permanent Preservation Areas (PPAs)

restauraRapp – Um pacote R para subsidiar o planejamento da restauração florestal em Áreas de Preservação Permanente (APPs) Ripárias

Abstract

The Law No. 12,651/2012 brought changes to the widths of riparian Permanent Preservation Areas (PPAs) to be restored, which now also depend on the size of the rural property in fiscal modules, which in turn, varies depending on the municipality. Because of this, defining the environmental liabilities of these PPAs is complex, requiring geoprocessing procedures and information that are not always readily available. To automate the identification of these areas to be restored, the restauraRapp package was created, which presents a set of functions for automating four steps of the procedure: I) Data acquisition; II) Classification of property sizes; III) Processing information on land use and cover data in riparian areas; IV) Cartographical and tabular output generation. This package can support landscape planning focused on the restoration of riparian PPAs, potentially assisting NGOs, municipality governments and landowners.

Keywords
Environmental policy; Brazilian Forest Act; Landscape management; GIS

Resumen

A Lei nº 12.651/2012 trouxe alterações nas larguras de Áreas de Preservação Permanentes (APPs) ripárias a serem restauradas, que passam a depender também do tamanho do imóvel rural em módulos fiscais, que por sua vez, varia conforme o município. Por conta disso, a definição do passivo ambiental dessas APPs é complexa, exigindo procedimentos de geoprocessamento e informações nem sempre prontamente disponíveis. Para automatizar a identificação destas áreas de passivo ambiental, foi criado o pacote restauraRapp, que apresenta um conjunto de funções para a automatização de quatro etapas do procedimento: I) Aquisição de dados; II) Classificação dos tamanhos dos imóveis; III) Processamento das informações sobre o uso e cobertura do solo em áreas ripárias; IV) Processamento e geração de resultados em formato cartográfico e tabular. O pacote gera resultados que podem auxiliar no planejamento de paisagens focado na recuperação de APPs ripárias, auxiliando ONGs, governos municipais e proprietários de terras.

Palavras-chave
Políticas ambientais; Código Florestal Brasileiro; Gestão de paisagem; SIG

Introduction

The conversion of natural habitats into anthropogenic areas threatens biodiversity and ecosystem services and functions, and is one of the major conservation challenges today (Foley et al. 2005, Tscharntke et al. 2012). Protected areas (PAs) alone are not sufficient to prevent biodiversity decline (Joppa et al. 2008, Williams et al. 2022), and lack of planning and political measures makes the establishment of new conservation units much harder (Bernard et al. 2015, Oliveira et al. 2017). Climate change brought additional threats to PA’s ability to guarantee biodiversity conservation, since it alters biophysical conditions, potentially making current PAs unsuitable for future species conservation (Araújo et al. 2011). Therefore, species conservation depends on anthropogenic landscapes (Melo et al. 2013), also because they can facilitate species movement between currently suitable sites to future suitable ones (Huang et al. 2020). Additionally, the synergistic effects of climate change and biodiversity simplification in fragmented landscapes compromise the provision of key ecosystem services (Fahrig et al. 2011). Thus, the proper management of human-modified landscapes is essential for biodiversity conservation and human well-being (Summers et al. 2012).

Brazil has a long history of environmental legislation (Drummond & Barros-Platiau 2006), particularly tackling forest destruction and the protection of rivers, water sources and slopes (Brasil 2010). However, it was not until the 20th century (Brazilian Forest Act 1934 and Brazilian Forest Act 1965) that the environmental legislation started to directly influence the territorial land use and cover. The first Brazilian Forest Act (Decreate 23.794/34, art. 4o) focused on forest destruction on environmentally sensitive areas, such as along rivers and water springs. The concerns with waterway banks were also expressed in the Water Code (24.643/34), however, with an administrative aspect and not aimed at environmental conservation. It was only in 1965 (4.771/65) that the Permanent Preservation Areas (PPAs) were properly defined (Brancalion et al., 2016) and then described as forests and other forms of natural vegetation located along rivers or any other watercourse, in a marginal strip whose minimum width was 5 (five) meters for rivers of less than 10 (ten) meters in width, equal to half the width of the water courses that measure from 10 (ten) to 200 (two hundred) meters of distance among river banks and 100 (one hundred) meters for all courses whose width is greater than 200 (two hundred) meters. The PPAs also encompassed areas around natural and artificial ponds and lakes, water springs, top of the hills, slopes greater than 45°, “restingas” and dunes that hold sandy soil, the edges of plateaus and areas with altitude above 1,800 m (Law no. 4.771/65).

Later in 1986 (Law no. 7,511, July 7th) and in 1989 (Law no. 7,803, July 18th) more detailed definitions of PPAs were provided, including the statement that the PPA width should be measured starting from the maximum water level, and an enlargement of the riparian PPAs, for instance 30 m from rivers with less than 10 m of width, 50 m for rivers between 10 and 50 m of width, 100 m for rivers between 50 and 200 m of width, 200 m for rivers between 200 and 600 m, 600 m of width for rivers wider than 600 m of width, also, a 50 m buffer around the water springs were defined as PPA (Table 1).

Table 1
PPA protection law changes since the first Brazilian Forest Act (1965) until the last change in May/2000.

Until 1965	September 15th 1965	July 7th 1986	July 18th 1989	May 26th 2000
Brazilian Forest Act 1934	Brazilian Forest Act 1965	Law no. 7,511	Law no. 7,803	MP no. 1956-50/00
No PPAs, but with some protection, if the area was declared as “protected forest”	Obligation to preserve native forests along watercourses, varying by its size.	Same as 4.771/65, but the native forests along watercourses enlarged, varying between 30 meters to 150 meters.	Riparian areas along intermittent water springs become PPA	PPAs do not need to be covered by native vegetation to be a preservation area.

After 13 years of debate in the Brazilian Congress and despite warnings from the academic community (e.g. Metzger 2010, Metzger et al. 2010, Nazareno 2012, Sparovek et al. 2012), regarding the interruption of important ecosystem services and the potential erosion of biodiversity due to the proposed changes (Toledo et al. 2010, Develey & Pongiluppi 2010 and a series of manuscript from this special issue, Alarcon et al. 2015), in 2012 a new law was promulgated that regulates the exploitation, conservation and restoration of the native vegetation in the Brazilian territory. The law no. 12,651 was sanctioned with vetoes by President Dilma Roussef on May 25th 2012, and later altered by law no 12,727, on October 17th 2012. One of the main changes in the Brazilian Forest Act (2012) lies in its transitional provisions. These provisions establish priority conditions for the restoration of PPAs in rural properties with consolidated areas. These areas receive differentiated treatment, whereby landowners must restore minimum vegetation buffers along watercourses, with variable widths (5 to 100 meters) depending on the type of watercourse and property and river sizes. This has added complexity to defining the width of riparian areas that require restoration, as it now demands more detailed information and geoprocessing expertise. The process involves partitioning the hydrography and properties data, making it more repetitive due to the need to create, manipulate and merge buffers of varying sizes multiple times, increasing the likelihood of processing errors. In order to help on this task, we built restauraRapp, which is an R package designed to seamlessly delimitate and quantify the riparian PPAs of water bodies, according to property sizes, therefore helping to define areas that should be restored. Here we introduce the restauraRapp R package, which aims to facilitate the steps to generate and quantify potential restoration areas along PPAs defined by law:

• A)

  Automate data download and acquisition,

• B)

  Divide property by sizes, based on fiscal module, as defined in the Brazilian Forest Act 2012,

• C)

  Create the buffer zones and clip the areas, according to the width defined by the Brazilian Forest Act 2012, based on property sizes and river widths,

• D)

  Export results in tabular and cartographic formats.

Methods

^1.

Data

Three datasets are used in order to calculate and define PPAs that are preserved and the ones that are degraded and could be restored (Table 2). These datasets are crucial, as they include all variables necessary to determine the preservation status of PPAs. Land use/cover and hydrography data are available at the municipality scale from the FBDS database, while CAR data is available at the state level and needs to be filtered to align with the other datasets. Although we strongly recommend using data at the municipality scale, the package also supports larger areas (such as stacked municipalities), provided the data sources and function parameters are respected (see below for details on function parameters). Ultimately, it is the user’s responsibility to define the study area to which these datasets will be applied during the process.

Table 2
Data description and source. *Fundação Brasileira para o Desenvolvimento Sustentável; **Sistema Nacional de Cadastro Ambiental Rural.

Data	Type	Source	Description
Land use/cover	Shapefile	FBDS*	Spatial dataset with land use and cover information based on RapidEye imagery with 5 meters resolution at spatial resolution 1:10000
Hydrography	Shapefile	FBDS*	Hydrography spatial dataset that includes rivers and other water bodies types (spring and lakes) at spatial resolution 1:10000
CAR	Shapefile	SICAR**	Spatial dataset with properties by state

The Land use/cover FBDS dataset was mapped through supervised classification of Rapid Eye imagery level 3A (5m resolution) and edited in a scale of 1:10,000 in order to correct classification and discontinuity errors (Rezende et al. 2018). This dataset was chosen because it is a vector created from a raster map with 5 m pixels resolution (5 m × 5 m). This resolution provides the necessary scale to evaluate the area to be restored in the smallest possible PPA width (5 m in properties smaller than one Fiscal Module (FM), see below) and is the best resolution available for land use/cover in Brazil (Rezende et al. 2018). Also, it was made based on supervised classification of images starting in 2013 (FBDS, 2023), first year after the Brazilian Forest Act 2012 promulgation and the nearest map available to mid 2008, the date used as the baseline for the new legislation.

The hydrography spatial dataset is divided in four parts, considering the width and the type of the water body, rivers narrower than 10 meters, rivers wider than 10 meters, lakes and springs. This dataset was created and refined by compiling official cartographic sources at the best available scales and adapting them using RapidEye images at a 1:10,000 scale. Contours generated from the SRTM digital elevation model (30 m × 30 m/pixel) were used as a secondary reference (Rezende et al. 2018). This dataset represents the most refined data currently available.

The CAR (Cadastro Ambiental Rural) is a national electronic public register established by Law No. 12,651 and regulated by Normative Instruction MMA No. 2 of May 5, in 2014. The registration is mandatory for all rural properties and aims to integrate environmental information of the properties (SICAR [s.d]). The CAR is also the first step for the establishment of an Environmental Regularization Program (in portuguese Programa de Regularização Ambiental, PRA), where the objective is to promote the environmental regularization of the property including the recovery, recomposition, regeneration or compensation of environmental liabilities (SICAR [s.d]).

The property polygons are obtained from CAR data (“Perímetro dos imóveis” option) from the official database system (SICAR). This information is the only one that still needs to be accessed manually, all the other cartographic information are automatically retrieved by the functions present in restauraRapp package (see below). Based on this information, property sizes are defined according to the fiscal modules (FM), which vary between municipalities. The Law No. 12.727 alterated the Law No. 12,651, and now it considered four sizes of rural properties, that we named here: micro (up to 1 FM), small I (from 1 to 2 FM), small II (from 2 to 4 FM) and large (greater than 4 FM). The FM is a measurement unit, in hectares, determined by The National Institute for Colonization and Land Reform (INCRA) and different for each Brazilian municipality, in which the value of one FM could vary from 5 to 100 hectares depending on municipality land use aspects (Embrapa [s.d]).

^2.

Buffer sizes

The functions of the package divide properties by size and create a set of buffers according to property class sizes, as shown in Table 3. These sizes define the widths of the areas that, if degraded, should be restored according to the Brazilian Forest Act (2012). The transitional provisions define that regularization using the new PPA buffer zones is only possible when consolidated areas are present. These are areas that were deforested and/or altered before July 22, 2008, within preserved buffer zones. In these cases, the continuity of economic activities is permitted, as long as the property is registered in the CAR. Following registration, the landowner must also adhere to the PRA, which requires a detailed recovery plan for PPAs and Legal Reserves (LR). By completing these steps, the landowner becomes eligible for the benefits of the transitional provisions, including the new PPA buffer widths (ranging from 5 to 100 meters), permission to continue activities in consolidated areas, exemption from previous fines, and other advantages.

Table 3
Riparian buffer widths that are considered in the package according to property class sizes.

	Property class sizes in Fiscal Modules (FM)
Hydrography	< 1	Between 1 e 2	Between 2 e 4	> 4
Rivers	5 m	8 m	15 m	20 m
Around the springs	15 m	15 m	15 m	15 M
Around the natural lakes	5 M	8 M	15 M	30 M

For rivers in properties with more than four fiscal modules, buffer width output was standardized to 20 meters, because such buffers can vary in width (between 20 meters and 100 meters), in accordance with the property’s PRA, and may need to be checked individually.

In addition, two alternative scenarios were provided to evaluate municipal regions where no CAR registration is available. These scenarios assume that areas without CAR correspond either to properties smaller than 1 FM (classified as micro properties, narrowed area to be restored) or larger than 4 FM (classified as large properties, wider areas to be restored), applying the respective buffer sizes for each class. Users have the discretion to apply one or both scenarios as needed.

^3.

Process development

All the functions of the restauraRapp package (see below) were designed and developed to facilitate the assessment of PPA preservation status. The workflow for buffer creation and PPA evaluation involves a structured process for manipulating spatial datasets (Figure 1). The first step is to download the hydrography (all four files) and land use/cover spatial datasets from FBDS database, which is an automated process (see below), while the properties boundaries datasets from the SICAR database must be manually downloaded. Once the data is ready, manually load the required spatial datasets (Table 2), ensuring that all datasets are checked for Coordinate Reference System (CRS) consistency. Universal Transverse Mercator (UTM) system should be used to enable accurate measurement of PPA’s areas. Ensuring that all datasets use the same CRS is crucial for the proper functioning of the functions. This must be verified and, if necessary, adjusted. Otherwise, the process will result in an error. Next, select the property boundaries of the target municipality from the SICAR dataset, and divide in classes by its FM size to determine the area occupied by each class. After classifying the properties, the polygons from each class are used to clip the hydrography spatial dataset (rivers narrowed than 10 meters, rivers wider than 10 meters and lakes), excluding springs. Springs were not included, as their buffer widths do not vary based on property size.

FIGURE 1
Workflow for assessing preservation status of PPAs, with automated steps using the restauraRapp package. (A) Data acquisition, 1. automated data download. (B) Data processing, 2. splitting properties by size based on fiscal modules defined by the Brazilian Forest Act (2012), 3. generation of PPA buffers according to properties sizes, 4. clipping PPA areas splitting among preserved or degraded. (C) Output generation, 5. exporting results in tabular data, shapefiles, KML formats.

Once the hydrography dataset is divided, buffers are applied according to the PPA width defined by the Brazilian Forest Act (2012) for each property size class (Table 3), generating the corresponding PPA areas. The buffered datasets are then merged. To ensure that only the PPA areas are measured, the internal areas of the polygons representing rivers wider than 10 m and lakes, corresponding water bodies, are cropped out. Finally, the land use/cover spatial dataset is clipped using the buffered areas to obtain the information of the preservation on the preservation status of the PPAs.

With the information on the preservation status of the PPAs, the user can export the results in various formats, such as a tabular file containing the values for each property class or for each property, a shapefile for spatial data storage and analysis in GIS software, and a KML file for visual validation using high-resolution satellite images.

All the development was made using the coding program R v4.0 (R core team 2023) and RStudio v1.4.1743 (RStudio team 2023), using the packages sf (Pebesma 2018), curl (Ooms 2024), XML (Lang 2024), abjutils (Lente & Trecenti 2022) and dplyr (Wickham et al. 2023).

Results

^1.

Package functions

The restauraRapp package currently includes six functions that automate the entire processes, from downloading data and creating property size classes, to clipping the PPAs based on property class sizes and river widths, and finally generating and organizing the results. It is important to note that PPAs sizes also depend on the presence of consolidated areas as of 2008 and the type of activity. Each function is detailed in Table 4.

Table 4
Functions of the package restauraRapp, parameters for the execution and function description.

Function		Parameters		Descriptions
resapp_fbds_dados		State (text), municipality (text),		Automatically downloads land use and cover and hydrography datasets, creates a directory with the name FBDS_MUNICIPALITY in your home folder and saves the datasets there
resapp_car_class		The CAR spatial dataset (.shp) downloaded from the SICAR website		Splits property by class sizes, returning a spatial dataset (*.shp) to each class size
resapp_car_info		The CAR spatial dataset (*.shp) downloaded from the SICAR website, municipality name (text), option between table of spatial dataset (text)		Returns the total area and the number of properties by class size for the focal municipality. Outputs can be a table or a spatial dataset (*.shp)
resapp_app_buffer		The hydrography spatial datasets (*.shp) automatically downloaded from the FBDS, the CAR spatial dataset (*.shp) downloaded from the SICAR website (*.shp), the land use and land cover spatial dataset downloaded from FBDS website (*.shp), scenario option (text)		Unifies hydrography spatial datasets and creates the buffer with the widths to be restored by class size, returning the intersection of these buffers with the land uses as a spatial dataset (*.shp) for the chosen scenario (see below for scenario details).
resapp_app_info		The scenarios generated by “resapp_app_buffer” (*.shp), CAR spatial dataset (*.shp) downloaded from the SICAR website (*.shp), option between table and spatial dataset (text)		Uses the outputs from the “resapp_app_buffer” function, unites the different land use/cover in the classes “restore” and “preserved”, returning the spatial dataset (*.shp) or data tables with the areas of these classes (restores and preserved) by property class sizes, for all properties or by property
resapp_app_kml		Scenario 1 (only properties with CAR) from the function “resapp_app_buffer” (*.shp), municipality (text), state (text)		Two options of spatial dataset (*.kml) files i. the entire PPA and ii. the areas to be restored, both by property class size

^2.

Downloading the data

The function resapp_fbds_dados (Equation 1, Table 4) downloads land use/cover and hydrography datasets. The parameters are “MUNICIPALITY” which is the name of the municipality in capital letters and, when necessary, separated by underscore, and “UF” which is the state abbreviation from which the municipality belongs. These are the only parameters needed to execute this function, which will download and save the required information in a folder named “data”. This folder is automatically created while running the function within the user’s R project directory. If the function is used outside an existing R project directory, the “data” folder is created in the R base working directory, which can be identified using the base R function “getwd()”. This ensures that the data is stored in an organized, traceable and easy to access manner.

$$\text{resapp\_fbds\_dados(UF, MUNICIPALITY)}$$

Equation 1: Function for downloading land use/cover and hydrography datasets from FBDS website.

So far, the CAR dataset needs to be manually downloaded from the SICAR database (https://www.car.gov.br/publico/estados/downloads), because of the presence of a captcha, preventing access through direct link. The “AREA_IMOVEL” file, downloaded through the option “Perímetros dos imóveis” provides information for the buffer calculation function in the package.

^3.

Property sizes classification

The function resapp_car_class (Equation 2, Table 4) classifies properties based on the number of FM, which vary in size depending on the municipality. The input “CAR” refers to the spatial dataset (*.shp) containing property boundaries information. This function returns a spatial dataset (*.shp) with the classified polygons, which can be used directly or as an internal input for other functions within the package.

$$\text{resapp\_car\_class(CAR)}$$

Equation 2: Function responsible for classifying CAR spatial dataset according to property class sizes.

The function resapp_car_info (Equation 3, Table 4) classifies the properties by class size, Returns the total area and the number of properties by class size for the focal municipality.

$$\text{resapp\_car\_info(CAR, mun, type)}$$

Equation 3: Function for obtaining information about property sizes distribution.

Where “CAR” is a spatial dataset (*.shp) carrying the properties information. If “mun” is defined, municipality limits will be used to get the portions of the properties that are exclusively within the focal municipality. If NULL, the results will include the entire property limits, also including portions that extrapolate municipality borders. It is important to note that the name of the municipality must be written according to the Brazilian Institute of Geography and Statistics (IBGE) database.

It allows users to select among the total area of the properties or just the areas within the limits of the municipality, according to what is registered in CAR, returning cartographic information (default), or just the data table. To select the areas within the municipality limits, it is necessary to add the name of the municipality to the function (parameter “mun”), which will automatically get the county limits and return the result according to the user’s choice, important to notice that the name of municipality must be written accordingly to Brazilian Institute of Geography and Statistics database. For the output options, the parameter type could NULL (default) for spatial dataset (*.shp) and “df” for table.

^4.

Buffering the PPAs

The function resapp_app_buffer (Equation 4, Table 4) creates buffers referring to the PPAs (Table 3) and clips the land use and land cover data (Table 2). This function takes a considerable processing time, depending on municipality scale and hydrography complexity, but is comparable to a geoprocessing process made in R, responding directly to hardware performance. For this reason, currently it has a parameter to be executed in parts, split by property class sizes and/or scenarios (see below). To execute the entire process at once, use option “tudo”, to split the process use: “micro” for properties below 1 FM, “peq12” for properties between 1 and 2 FM, “peq24” for properties between 2 and 4 FM and “grande” for properties above 4 FM. To split by scenario the “tipo” parameter must be “out2” for Scenario 2 or “out3” for Scenario 3 (see below) the option “tudo” (out1) only works for the areas that have a CAR registered.

This function automates the division of properties by size class, the creation of buffers using the Brazilian Forest Act (2012) widths for consolidated areas, and the clipping of these buffers with land use/cover data, followed by the calculation of preserved and deforested areas. With this approach, the user no longer needs to repeatedly create buffers of different sizes, manually calculate property areas, or perform other data manipulations. This reduces the likelihood of processing errors and fosters a more streamlined, standardized workflow.

$$\text{resapp\_app\_buffer(mapa\_NAS, mapa\_MDA, mapa\_RMS, mapa\_RMD, CAR, uso, tipo)}$$

Equation 4: Function to create the buffers, using the widths defined by law (Table 3), and clips the land use/cover inside it.

Where “mapa_NAS” is an object carrying the information about the springs; “mapa_MDA” carries the information about lakes; “mapa_RMS” have information about the rivers smaller than 10 m; “mapa_RMD” have information about rivers bigger than 10 m; “CAR” are the properties limits; “uso” is the land use/cover of the municipality; “tipo” is the process that need to be made (according to property class size, or all properties).

In some cases, municipalities do not have lakes or rivers bigger than 10 m inside its limits, in these cases the parameters “mapa_MDA” and “mapa_RMD” accept NULL values. If one of the class sizes is selected (in “tipo”), the union of the objects is needed before proceeding in other functions.

Based on the data and the procedures above, three different scenarios can be created:

• Scenario 1 (“out1”): All the properties with CAR are evaluated, and the restoration areas are evaluated based on river width and property size;

• Scenario 2 (“out2”): Only the land that is not covered by CAR is considered, and all the area is treated as properties smaller than 1 FM, i.e., the smaller buffer width that should be restored (5 m);

• Scenario 3 (“out3”): Only the land that is not covered by properties in the CAR is considered, and these areas are treated as large properties (> 4 FM), i.e., the wider buffer possible (20 m);

^5.

Compiling and organizing the results

The function resapp_app_info (Equation 5, Table 4) divides the different land uses/cover as “Preserved” (Forest and non-forest native habitats) and “Restore” (other uses, except water) classes. This function has options to return the results as a shapefile (parameter “tipo = tudo”), as tabular data for the municipality (parameter “tipo = df”) or as tabular data for each property (parameter “tipo = prop”).

$$\text{resapp\_app\_info(out1, out2, out3,}\text{CAR, tipo)}$$

Equation 5: Function responsible for splitting land use/cover data into “Preserved” and “Restore” and creates the shape or tabular output from the results of Equation 4.

Where “out1”, “out2” and “out3” are the results of the function resapp_app_buffer (Equantion 4) respectively Scenarios 1, 2 and 3. “CAR” is a spatial dataset with property limits and “tipo” the type of output that will be created (“tudo” for spatial dataset *.shp, “df” for a tabular output for the entire municipality, and “prop” for a tabular output by each property). For the “tudo” option, it is necessary to provide only the output from Scenario 1 (“out1”) from the function resapp_app_buffer (Equation 4). The “df” option needs the information of all three scenarios (“out1”, “out2” and “out3”), and the “prop” option needs the properties limits from the CAR spatial dataset and the Scenario 1 result (“out1”).

Finally, the function resapp_app_kml (Equation 6, Table 4) exports the spatial datasets obtained by executing the function resapp_app_buffer (Equation 4) function as KML files to be opened in Google Earth, which is a free and an easy to use tool. The final files of this function are the complete PPA polygons based on property sizes and the polygons of the areas to be restored, also splitted by property size class.

$$\text{resapp\_app\_kml (out1, MUNICIPALITY, UF)}$$

Equation 6: Function to export the spatial dataset as a KML file.

Where “out1” is the output from function resapp_app_buffer (Equation 4). “MUNICIPALITY” is the name of the municipality and “UF” is the abbreviation of the state to which the municipality belongs. The “MUNICIPALITY” and “UF” parameters are for file name generation and can be a NULL value.

Discussion

The changes brought by the Brazilian forest Act 2012 aim to facilitate the compliance with the environmental legislation in rural areas (Soares-Filho et al. 2014), however, they also bring additional difficulties to define and monitor environmental legislation observance (Garcia et al. 2013, Soares-Filho et al. 2014, Brancalion et al. 2016). With restauraRapp, we show that the areas to be restored in PPAs can be straightforward mapped in a precise and spatially explicit way. This can help initiatives for environmental legislation compliance, since it can automatically identify forest deficits for large areas and help in the planning of the restoration initiatives, allowing restoration costs to be reduced and area prioritization to be done. Since cost and planning are among the biggest challenges in restoration initiatives (Ifthekar et al. 2016, Strassburg et al. 2019, Brancalion et al. 2019, Schimetka, 2024), the package can help restoration actions to gain scale.

Additionally, restauraRapp can help validate, mainly through visual analysis, the information present in the CAR system, since the information is self-declared, and incongruities are commonly found in the dataset (Melo et al. 2021). It is noteworthy that watercourses PPAs play a key role within agricultural landscapes, as they provide connections between the native remnants (Rosot et al. 2018, Rother et al. 2018), as well as the protection of water resources (Valera et al. 2019). Supporting decision-making through the approximation of science based solutions and police makers, as well the application of these decisions, are also essential for reconciling agricultural production and conservation (Ferreira et al. 2012, Tavares et al. 2021). In this context, the proposed R package can subsidize the prioritization of areas for ecological restoration, either because they present a higher importance for water resources conservation, or because of forest fragment connectivity, or even because they are located in a much more resilient region of the landscape.

Additionally, restauraRapp automates the downloading and standardization of data sources, streamlining access to information and organizing data more efficiently. The system automatically creates directories in the selected project folder, ensuring organized data storage. While the time required to run the package may vary depending on the municipality size and hydrography complexity, it remains comparable to standard geoprocessing tasks in R. However, automation allows for a smoother, more efficient workflow by minimizing manual intervention. By automating the repetitive steps, the package also significantly reduces the risk of common geoprocessing errors, ensuring greater accuracy and consistency in the results.

In summary, restauraRapp is a tool developed to facilitate the delimitation of riparian areas that should be restored based on the Brazilian Forest Act (2012). Therefore, it is a tool that can help to plan landscape management actions towards restoration of riparian areas, and can aid the conservation of aquatic and terrestrial environments in private lands in Brazil.

Acknowledgments

Thanks to V. Ferrari for the English review, to P. F. Fernandes for code review and R. Borioni, T. Timo and A. Peressin for manuscript review. This work was supported by São Paulo Research Foundation (FAPESP grant number 18/20501-8), by Cílios da Terra (ProEx nº 015849/2022-30) and SOS Mata Atlântica (ProEx nº 41339/2022-18) NGOs and J. C. L. Araújo thanks to CAPES (grant number 001).

Data Availability

The functions, as well all the files for the execution of an example of the package run, are available at: https://github.com/NEEDS-LS/restauraRapp.

References

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Publication Dates

• Publication in this collection
  03 Feb 2025
• Date of issue
  2025

History

• Received
  23 May 2024
• Accepted
  03 Jan 2025