Evaluation of the potential for carbon sink and association with land occupation

The Earth's environment is constantly changing, through anthropic or natural means. Of the two cited, the former causes major environmental impacts through extraction, exploitation and extensive use of natural resources. According to Wijerathna-Yapa & Pathirana (2022), environmental concern was boosted in the last decades of the 20th and 21st centuries through actions in favor of the Environmental Revolution, and through awareness that natural resources are finite and inappropriate use can lead to the end of these inputs in the next few generations.

The conferences and intergovernmental panels focused on environmental issues at the end of the 1960s, and mainly in the 1970s, addressed environmental sustainability, and the impact of the exploitation of natural resources and environmental degradation (Pingali 2012).

These discussions led to environmental regulations and pollution began to be considered a crime in many cities. Throughout the 1980s, new perceptions of development were created, limiting the use of natural resources, including the universal concern over ozone depletion and greenhouse gas (GHG) emissions.

In Brazil, the Earth Summit or Rio-92 conference was held in the city of Rio de Janeiro in 1992, based on five themes: Rio Declaration; Agenda 21; Convention of Biological Diversity; Principles about Forests; and the Convention about Climate Change. In the same decade, the United Nations Conference on Climate Change took place, with the intention to promote the Kyoto Protocol.

The Conference of the Parties (COP), according to UNEP (2022), aimed to propose numerous actions to reduce the temperature increase (to keep global warming below 2 °C but with the main objective of limiting the increase to 1.5 °C) and to mitigate the impacts of global climate change.

The emission of GHGs has been growing over the decades, due to the great anthropic influence, causing an increase in the concentration of CO₂ in the atmosphere and global warming. The concentration of carbon dioxide (CO₂) increased from 280 ppm in the era of the Industrial Revolution to 400 ppm in the mid-2010s. This may have influenced an increase of 1.1 °C in the planet's average temperature recorded in 2016 (IPCC 2013).

The greenhouse effect is an important process for maintaining life on Earth. It occurs when part of the infrared radiation is emitted by the Earth's surface and absorbed by gases in the atmosphere, which stabilizes the average temperature of the planet in habitable conditions, allowing part of the heat to leave the atmosphere. In contrast, the greenhouse effect when under an increased concentration of GHG causes greater heat retention. Among the main gases, the most prominent, according to Fagodiya et al. (2022) are water vapor (H₂O), CO₂, ozone (O₃), methane (CH₄) and nitrous oxide (NO₂).

CO₂, besides being part of the greenhouse effect, is an important element of photosynthesis, in which CO₂ is absorbed by plants and releases O₂ into the atmosphere. In this process, the plant uses CO₂ for the synthesis of carbohydrates, and part of the fixed carbon is returned to the atmosphere by the decomposition of dead organic matter and another part is used for the biomass of living beings, called carbon flux (Fagodiya et al. 2022).

However, the fixation and release process has suffered direct interference due to deforestation, considered the largest source of CO₂ emissions in the world. A primary or secondary forest can then be replaced by agricultural activities that provide the release of CO₂. In Brazil, as the conversion of forests into areas of pasture and agricultural land use change is considered the main source of carbon emissions into the atmosphere, between 1991 and 2019 the country emitted values that reached the order of 2.5 GtCO₂e (Giga ton of carbon equivalent) (Azevedo et al. 2018).

The increase in the planet's natural greenhouse effect, changes in the climate and global warming are associated with extreme climatic events such as the dry season in São Paulo in 2014 and 2015. Climate change poses risks to the supply of water, food and housing. Warming is a phenomenon that does not occur in isolation, it covers the whole world, and the poorer regions are more susceptible to feeling them more intensely and more quickly. The drought that struck Brazil in the years 2014 and 2015 is linked mainly to the deforestation of the Atlantic Forest, a biome that has the function of pumping moisture to the continent, with transpiration being responsible for 20 trillion liters of water for the area covered by the Atlantic Forest and on the meeting of the IPCC goals, on the other hand reducing GHG emissions and reducing deforestation, starting reforestation of native species (Sirvinskas 2021).

Certain phytophysiognomic characteristics affect the sink of carbon from the atmosphere. Depending on the phase of their phenological cycle or their vegetative growth rates and their size they can remove more or less carbon from the atmosphere, on the other hand, when they decompose or burn, they release carbon into the atmosphere. Thus, the reduction of deforestation and reforestation by planting trees in denuded areas or areas that have been converted to some other type of use, such as agriculture, are essential to mitigate the environmental impacts of climate change.

On the other hand, to mitigate the impact caused by penalizable sources of pollution, institutions were created with legal representation in environmental matters, endowed with the technical capacity to sign agreements and commitments, to mediate conflicts and, supported by multidisciplinary knowledge, provide mechanisms of environmental accountability, acting to repair the environmental damage of diffuse origin and less to the punitive character (Sirvinskas 2021). They scrutinize degraded ecosystems, and if it is not possible to recover them, they require other forms of ecological compensation. The Clean Development Mechanisms (CDM), including Carbon Neutrality, were created to enable ecological compensation.

Therefore, encouraging reforestation actions and the recovery of degraded areas are recommended alternatives for mitigating GHG emissions, there is a need for technologies to estimate carbon sink, highlighting the use of Geographic Information Systems (GIS) and products from Remote Sensing (RS). Reliable tools, methodologies and estimations of carbon sinks are key to establishing ecological compensation. Satellite systems from free sources often cannot physiologically identify the vegetation, and which species are present, but they allow us to obtain information about phenological patterns, suggesting the definition of the characteristics of the ecosystems to which they belong by means of temporally and spatially observed rates.

In the scope of GHG quantification, studies demonstrate the importance of using GIS to obtain the Normalized Difference Vegetation Index (NDVI) and its interaction with the Photochemical Reflectance Index (PRI), to determine CO₂ flux, used in estimating biomass and carbon stock (Rossini et al. 2010; Ainsworth et al. 2013; Guarini et al. 2014; Vicca et al. 2016; Zhang et al. 2016).

This study aimed to estimate the potential CO₂ flux from spectral indices calculated with the use of GIS in the riparian vegetation of the Jacareí–Jaguari reservoir, in the State of São Paulo, where reforestation actions have occurred as a result of environmental commitments. It is estimated that the potential CO₂ flux for a new parcel of vegetation leads to a greater potential for carbon sink.

The quantification of CO₂ flux in the riparian vegetation of the Jacareí–Jaguari reservoir has its parcel of importance, as was demonstrated through vegetative rates that the reservoir of the Cantareira System, which is part of the Jacareí–Jaguari ecosystem and is responsible for supplying a large part of the São Paulo Metropolitan Region (RMSP), contributes to a carbon sink. Forest restoration in this case, besides acting as a tool to mitigate the effects of climate change, brings environmental benefits by protecting the main source of water of the RMSP.

The more detailed analysis of carbon emissions involves both the determination of extrinsic elements and the rates of the dynamics of land use and occupation, in which the constant anthropic alteration and factors linked to plant physiology and to parameters associated with the ecosystem to which the physiognomy belongs, influence the calculation of carbon stock for the plots comprised by the vegetation itself, of the plots belonging above and below ground.

Certain care must be taken when adopting parameters for the calculation of CO₂ emissions, as studies have shown, there are uncertainties regarding their emissions. Thus, understanding the carbon cycle and flows for a given biome also includes quantifying the vegetation biomass.

The spectral rates, as well as the map layouts, were obtained by the free and open-source Geographic Information System QGIS 3.16. Once the reservoir boundary was generated, the spectral bands obtained from the CBERS4A satellite were cropped for their use in generating the NDVI and PRI rates.

For the analysis of the potential flux of CO₂, images from the CBERS 4A satellite of the MUX camera were acquired in various wavelengths of the electromagnetic spectrum, at orbit 202 point 142 on 10 August 2021, for presenting the lowest cloud cover in the basin during the rainy and warm period, because it presents an intense flux of CO₂, in the most propitious phase for the increase of the biomass of the plants. Bands 5 (near infrared (NIR)), 4 (red), 3 (green) and 2 (blue) were chosen to generate the NDVI, the PRI and the carbon dioxide flux potential rate (CO₂ flux).

The images obtained in each of the bands underwent a reprojection consisting of the change of the original horizontal datum (WGS1984) for the horizontal datum SIRGAS 2000, EPSG 31985 Zone (F) 23 South (S). We chose to use the scenes from the CBERS 4A satellite, which is equipped with the MUX multispectral sensor and according to INPE (2019) has an orbit/point of 165–124, with spatial resolutions of 16.59 m and a radiometric resolution of 8 bits. The file contains the bands: 5 (0.45–0.52 μm), 6 (0.52–0.59 μm), 7 (0.63–0.69 μm) and 8 (0.77–0.89 μm). The launch of CBERS 4A was carried out on 20 December 2019, according to INPE (2019), so only 2 years of satellite operation are not enough to present relevant results for a temporal analysis with data for scaling and significant matrix operations.

The calculation of the PRI aims to check the changes in carotenoid pigments in foliage. These pigments are indicative of the efficiency of photosynthetic light use or the rate of CO₂ stored by the foliage.

The validation of the values obtained for the CO₂ flux consisted of the Statistical Analysis (minimum, maximum, mean, standard deviation and variance) of the vegetation indices (NDVI, PRI and sPRI) intersecting with the land use classes made in the Map Biomes 5.0 Collection in the Jacareí–Jaguari Reservoir. To analyze the predominant pixels in each of the classes defined in the MapBiomas 5.0 Collection the histogram of each was determined, aggregating them into a number of interval groups distributed according to the amount of pixels of each CO₂ flux matrix clipping.

Classes identified as ‘water’ with discrepant PRI values, below the mean, were taken to test, parcels of ‘water’ that were out of line with the rest of their class, by means of interval ratio, and an empirical analysis indicative of chlorophyll-a formation was performed. For Modarelli et al. (2021), the surface concentration of chlorophyll-a is related to a ratio of intervals or blue and green reflectance bands, spectral bands that are used to calculate the PRI (Equation (2)).

When it comes to vegetative rates, there are ways to quantify the ability of vegetation to use incident light for photosynthesis. Considering the broad electromagnetic spectrum, only a small band is actually used by plants during photosynthesis. This band is within the visible portion of the spectrum, from 400 to 700 nm. A plant's ability to absorb energy efficiently within this range leads to improved growth rates and biomass production (L3HARRIS GEOSPATIAL 2022).

The NDVI can be used to detect seasonal and temporal variations along the various phases of vegetative development, of crop phenology. The NDVI has therefore been successfully used for monitoring changes in vegetation at different scales, at local and global levels. The vegetation rate enables plant characterization of environmental conditions, mapping of phytophysiologies and determining plant productivity.

According to Vasilakos et al. (2022) the studies by Pettorelli et al. (2005), Beck et al. (2006), Fensholt & Proud (2012) and Forkel et al. (2013) analyzed spatio-temporal variations by means of the NDVI for the investigation of vegetation change. While Bai et al. (2022) investigated vegetation change in spatio-temporal assessments from the NDVI, roles of climate change, CO₂ emissions and human activities in a mountain, oasis and desert ecosystem. The spatial heterogeneity caused by the variability of landscapes emphasized the need to resolve spatio-temporal patterns in complex ecosystems. Following the context, with a spatial focus, the NDVI was analyzed in a desert/countryside transition zone by Wu et al. (2020) from 1982 to 2015, showing increasing trends in the density of vegetation with the better recovery trends.

Zou et al. (2020) applied data on gross primary productivity, evapotranspiration, land surface temperature, and the NDVI to calculate an Ecosystem Water-Use Efficiency Rate and a Drought Vegetation Temperature Rate (TVDI) with moderate resolution imaging spectroradiometer (MODIS) data across different vegetation types, drought classifications and localities. The study demonstrated an inversely proportional relationship, with ecosystems of primary productivity having higher water-use efficiency scores, differences that are more evident with the degree of drought, and with the physiology and location of the species.

However, the NDVI loses its sensitivity to verify the condition of natural or agricultural vegetation, especially in the most advanced stage of development. When the canopy is more homogeneous and with a high leaf area rate, a phase in which saturation occurs, the reflectance ends up falling in closed canopies, losing its ability to distinguish vegetative vigor.

The PRI has good associations with vegetation rates such as the NDVI for detecting carbon sequestration; however, analysis needs to be expanded to various types of phytophysiognomies, in recent years radioactive efficiency models have been performed in temperate forests (Rossini et al. 2010; Ainsworth et al. 2013; Guarini et al. 2014; Vicca et al. 2016).

An accurate assessment of primary productivity is crucial for characterizing the climate-carbon cycle of an ecosystem. By remotely detecting the PRI across all functional vegetation types and spatio-temporal scales, the rate has often been used to monitor photosynthetic performance, determining large-scale primary productivity and biochemical processes that help plant organisms cope with sunlight damage. The long-term variation in the PRI can be attributed to changes in plant pigments, and structural changes in the canopy and ecosystem levels (Khotari et al. 2021).

Zhang et al. (2016) suggest that the PRI is a good indicator of photosynthetic efficiency at different spatial and temporal scales. The authors gathered recent publications that reported factors affecting the variations of the PRI on diurnal and seasonal scales at leaf levels, but cannot be used untreated. This is a necessary correction of the PRI, by decreasing the influence of physical or physiological factors on it, and improving the relationships between the PRI with Solar Radiation Use Efficiency (RUE) and gross primary productivity.

The PRI is a rate that determines the photosynthetic efficiency of a forest, the range for a PRI is −1 to 1, where healthy vegetation generally lies between values of −0.2 and 0.2, and closer to zero (0) the PRI is, the greater the capacity for light utilization by photosynthesis (L3HARRIS GEOSPATIAL 2022).

Ryu et al. (2022) evaluated crop damage caused by heat stress in rice crops, related to productivity through RS vegetation indices, the NDVI and PRI, between 2016 and 2019. Seasonal trends of the NDVI and PRI were examined at different spatial scales using a leaf spectrometer, field spectrometer and a multispectral camera at the canopy scale. It was noted that the tendency to change the application of the NDVI and PRI depends on the degree of thermal stress, right after the most advanced maturation stage, evaluated in the leaf and canopy scales. Since the NDVI and PRI decrease under normal conditions after these advanced stages of maturation, however, the decrease in vegetation indices is minimal under extremely high air temperatures. On the other hand, when there was a decrease in rice productivity due to the low maturation rate, physical damage was not evident in the phytophysiognomy under higher temperatures.

The NDVI can detect structural changes, but the index is not accurate enough to detect less visible changes in the physiological processes that control photosynthetic activity, particularly in perennial species, noting little change in greenness from the budding to the growing season. On the other hand, the PRI detects more subtle changes in the regulatory processes related to photosynthetic activity, unlike the NDVI, in perennial species. The PRI can detect pigment responses to environmental stimuli. On diurnal scales, the PRI responses are bolded by changes in the yellow pigment cycle that carotenoid A variety of other wavebands have been used to calculate the PRI, and proper interpretation of the PRI may depend on the formula and sampling scale used (Springer et al. 2017).

Many physical, biochemical and physiological factors can affect PRI patterns, diurnal and seasonal factors, at leaf, canopy and ecosystem levels. Factors that increase variation in the PRI cause changes in the production of photosynthetic pigments, elements with the ability to absorb sunlight in the process of photosynthesis, the constitutive pigments, chlorophyll and carotenoids. Physical or external factors, such as lighting, temperature, water stress, ‘background’, diseases and low nitrogen levels, high ozone concentrations, and shading, have the potential to produce biochemical and physiological changes, causing variations in the PRI (Hilker et al. 2007; Rossini et al. 2010; Ainsworth et al. 2013; Vicca et al. 2016; Zhang et al. 2016; Zhang 2017).

Canopy structural properties and solar sensing directions (zenith angle) at ecosystem levels are also important factors contributing to changes in the PRI, which is a reliable indicator of diurnal changes in RUE and a good indicator of seasonal changes in photosynthetic efficiency, in different analyses of different scales (Hilker et al. 2007; Rossini et al. 2010; Ainsworth et al. 2013; Vicca et al. 2016; Zhang et al. 2016; Zhang 2017).

Environmental conditions related to drought can affect the performance of the PRI. Further evaluation of the CO₂ flux emission model considering temporal variations related to drought and rainfall seasonality is needed, since the performance of carbon sinks is related to canopy water content and vegetation structure as studied by Ryu et al. (2022) at the leaf and canopy level as temperature changes, a typical situation that needs to be further investigated in tropical forests.

Physical factors related to the satellite detection direction influenced the determination of the PRI for the Jacareí–Jaguari reservoir margin, which as for the water body should present less contrast and more uniformity (Figure 1), attributed to the zenith angle of view, which is proven to be highly sensitive for the PRI measurement. In Figure 1, it is noted that the difference in contrast, is attributed to the satellite image capture, ruling out the possibility of imbalances in the processes of primary biomass growth in aquatic environments, induced by the blooming of phytoplankton organisms.

The use and occupation of the soil in permanent preservation areas, on the margins and in the riparian forests, might interfere in the environmental aspects of a dam, due to the continuous alteration of the surface, when forest areas are replaced by urbanized areas, besides agricultural crops and pasture areas.

Nestola et al. (2016) evaluated the seasonal patterns of CO₂ flux by comparing different optical sampling techniques with field spectrometer measurements, the NDVI from automated sensors at an ‘optical phenology station’, and MODIS NDVI measurements. Depending on the formulation used for the NDVI and the temporal sampling used, the three methods showed slightly different results, with the automated sensors showing better cost–benefit for CO₂ flux measurement by providing the most detailed time series, matching the sampling frequency to that of the flux measurements. Xu et al. (2022) conducted a survey on the use of the vegetation rate (NDVI) in bibliometric data from 17,755 scientific studies and found that researchers have used the NDVI to determine the relationship between the CO₂ flux and NDVI (Gilmanov et al. 2005; Balzarolo et al. 2011) calibrating RS inversion results of CO₂ flux (Wylie et al. 2003; Zulueta et al. 2013). Fernandez et al. (2021) point out that techniques based on RS and CO₂ flux rates have been applied to some studies to monitor Angolan forest deforestation (Gough 2011), to update the boundaries between Amazon and Cerrado biomes in Brazil caused by human actions (Da Silva et al. 2019) and to assess the post-fire recovery process in Gangwon province (South Korea) (Ryu et al. 2018). Fernandez et al. (2021) produced maps of fire severity and forest biomass evolution through Primary Productivity Indicator and after forest fire events from 1991 to 2019 based on Landsat 5, 8 and MODIS satellite imagery. Analysis showed that forest fires increased in area and severity with each decade due to the large accumulation of biomass promoted by the abandonment of rural areas, before the large fires.

Rahman et al. (2000) proposed the rate, named CO₂ flux, by means of two other vegetation rates, the NDVI and PRI, to measure the efficiency of the carbon sink process by vegetation with images from the AVIRIS (Airborne Visible InfraRed Imaging Spectrometer) sensor in a dense tree community forest, achieving a high correlation CO₂ flux obtained from meteorological stations.

To correlate the existing land uses in the Jacareí–Jaguari reservoir, the land use and vegetation cover map made available by the MapBiomas 5.0 Collection was used. Each boundary determined by a vector layer of the Mapbiomas 5. 0 (mask layer) was determined by means of a statistical analysis of the vegetation rates (NDVI, PRI and sPRI), and making the correspondence admitted by the intervals to discrete data according to the number of samples (fragment) in maximum, minimum, mean (μ), standard deviation (δ) and variance values in the land use classes identified in the Mapbiomas Collection (Table 3) as the following: (i) forest formation; (ii) planted forest; (iii) pasture; (iv) mosaic of agriculture and pasture; (v) urban infrastructure; (vi) other non-vegetated areas; (vii) rivers, lakes and oceans; (viii) soybean and (ix) other temporary crops.

It is possible to note that the measures of dispersion, the measures of distance from the mean, are smaller and improve as the biomass detection capability by the bands present in the NDVI and PRI vegetation rates that are intersected, with emphasis on the class of planted forest and forest formation.

The results obtained for the vegetation indices NDVI and PRI and by the NDVI matrix operation and PRI scaling for the calculation of CO₂ flux were correlated with the land use and occupation mapping. Different types of vegetation contribute to the carbon stock, and the highest concentration of carbon was found in areas of forest formation, demonstrating the importance of the preservation of protected areas and planting actions. Figure 5 shows synthetically each vegetation rate in comparison to the range of values covered for all land cover and occupation classes (forest formation; planted forest; pasture; mosaic of agriculture and pasture; urban infrastructure; other non-vegetated areas; rivers, lakes and oceans; soybeans and other temporary crops).

Della-Silva et al. (2022) used different satellite and airborne systems to calculate the vegetation indices for the estimation of CO₂ flux and found a big difference in obtaining the PRI sPRI, in the open access images, where both PRI reference narrow bands are in the green band for these data sets while in the proprietary sources, they were allocated in different bands. It is then noted that spectral mixing may occur in the PRI results, since the proprietary sources showed negative values for this vegetation index, while the open access ones showed values above zero in all land cover types, behavior that can be observed in the box plot of Figure 10.

The histograms in Figure 11 show that most of the CO₂ flux classification is found in the areas defined as ‘River, Lake and Ocean’, all classes without exception present a leptokurtic distribution, which was already expected in the non-vegetated classes. An exception occurs in the urban infrastructure class, where the histograms are close to a positive asymmetry, with the declination to negative values of CO₂ flux, while on the other hand, other ‘green’ classes are in the positive portion of CO₂ flux.

Zhang et al. (2016) demonstrated that the PRI can be related to an RUE rate and vegetative biomass rates such as the NDVI. While for Guarini et al. (2014) NDVI and sPRI showed positive relationships with light use efficiency, certifying that vegetation can use incident light as an energy source for photosynthesis, the NDVI showed weak correlations with vegetative tissue efficiency used for photosynthetic respiration. Rossini et al. (2010) evaluated the same efficiency in rice crops by spectroradiometer and concluded that the relationship becomes more sensitive as the vegetation matures and senesces.

On the other hand, Zhang et al. (2016) indicate that most publications on the use of the PRI focused on broadleaf forests, herbaceous forests and crops, so there is a need to expand the study to a wide range of plant functional types or to stress situations that affect both nutrition and vegetative development.

Surveys performed by means of RS and geoprocessing allied to the investigation of land use and occupation are tools that stand out for the quantification analysis of GHGs such as CO₂ when compared to surveys that require vegetative metrics to estimate dry biomass obtained in situ.

The vegetation rates applied in this study were established primarily for terrestrial vegetation. Rowan & Kalacska (2021) point out that studies focusing on specific rates of vegetation in wetlands can be challenged. Certain vegetation rates do not have adherence with surface aquatic vegetation, due to the difficulty of detecting the properties of aquatic vegetation also by the high absorption of red and NIR by water columns.

Martins (2017) initially used the NDVI in the maps of euphotic zone extension and maps of macrophyte presence in Paiva Castro reservoir, belonging to SABESP, also consisting of the Atlantic Forest Biome, and it was shown to be more suitable for the study of the use of the Normalized Difference Water Index (NDWI). No practical results were found for the calculation of CO₂ flux, for NDWI matrix operations with the PRI and with sPRI scaling. The rates used for macrophytes need to be validated on satellite imagery considering real aquatic environments with different aquatic temperatures, salinity, depths, in sediment texture types and different temporal functions such as the Normalized Water-Adjusted Vegetation Index (NWAVI). Developed only for portions of primary submerged aquatic vegetation, the rate is based on NIR and red reflectance, according to the slope of view below the waterline. For the determination of primary biomass, several spectral rates for the percentage of submerged aquatic vegetation cover were analyzed. NWAVI is not listed among the top five rates with the best 5% significance tests (p: 0.05).

Global warming is a relevant issue around the world, and technologies, methodologies and tools helping to accurately estimate GHG emission and mitigation are fundamental to enable projects which promote a positive impact on the environment. Restoration actions are associated with carbon sink and reduction of GHG emissions, but there is a lack of tools to estimate the carbon sink, especially in tropical forests.

From the vegetation indices NDVI, PRI, sPRI, the potential CO₂ flux of the Jacareí–Jaguari reservoir was estimated, indicating the need for vegetal restoration, planting and conservation of existing vegetation. The individual analysis of the classes with vegetative characteristics of the MapBiomas 5.0 showed high photosynthetic potential, being the densest plot, of forest formations that showed properties capable of storing CO₂. On the other hand, for plots that accumulate large amounts of chlorophyll, the use of spectral range between red and NIR in the proposal is more accurate and more sensitive to analyze the degradation of vegetation cover, replacing the visible red.

The analysis allowed observations of the photosynthetic activities, in the respiration phase, as well as the concentrations of CO₂ in the atmosphere, which vary according to the use and occupation of the soil. In turn, the presence of vegetation, specifically trees, appears to be a key element in reducing CO₂ concentrations, confirming its importance as an area endowed with an ecosystemic function, of carbon sinks.

Quantitative surveys by means of RS matrix operations prove to be important tools for public managers in zoning and land parceling, adhering to the watershed plan, and encouraging the restoration of the riparian vegetation and the vegetation plot, which consequently improves the quality of the reservoir water, assists in the maintenance of natural systems essential to biodiversity, improving the quality of life of the local population, as has been done by forest restoration.

Additionally, by measuring the potential of carbon sink of forest restoration initiatives, the access to environmental funds, such as MDL, might be facilitated, which could boost the implementation of climate change mitigation projects.

The authors thank the Companhia de Saneamento Básico do Estado de São Paulo (SABESP) for providing data for this article.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.