Mapping biodiversity patterns for Environmental Impact Assessments (EIA) using remote sensing
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Chaves, Pablo Pérez
In his talk, Pablo outlined the Environmental Impact Assessments (EIA) process to assess project’s impacts on the environment, including biodiversity, a powerful tool to propose mitigation measures when tackling negative impacts. However, these assessments often underestimate the impacts on biodiversity due to lack of continuous biological information covering the whole targeted area. Pablo’s work explored the potential of combining existing biological field-data with satellite images, such as Landsat Satellite images,as a cost-effective way to predict biodiversity patterns in areas far beyond the location of field-data, offering the possibility of reliable continuous biodiversity patterns for EIAs. This innovative biodiversity mapping method allows for properly quantifying a project footprint’s impact on biodiversity and identifying suitable areas for “like-for-like” compensation schemes.
 My name is Pablo Perez. I'm originally from Peru, but I'm living in Finland for a while already. I'm director of environmental services in Finnish overseas consultants or FINOC, and I'm also a visiting researcher in the University of Turku in Finland. I've been working mainly in Amazonia for a while, but I'm also working in international consultancy in different places around the world. I would like to, I wanted to show a little bit of the ideas that I have on how to use depth observation, how to use field data for mapping biodiversity patterns so we could improve environmental impact assessments. So I'll briefly talk a little bit about what environmental impact assessments are, some related concepts, and then I'll talk about how useful remote sensing data such as satellite images or elevation can be useful for mapping biodiversity. And then I'll talk briefly about this framework on how to improve the incorporation of biodiversity in environmental impact assessments and by using remote sensing and machine learning. Environmental impact assessments and related concepts, mainly an environmental impact assessment or an EIA, it's a tool or set of processes that basically does three main things, right? The characterization of the environmental conditions of a study area, a study area where a project, a development project is going to be whether it's mining a road project, a wind or other energy projects. And the idea also is to assess, identify the impacts of these specific development projects on the environment. And when I mean environment, I talk about the physical, biological, social and cultural aspects. And then based on these environmental conditions or what is called so-called baseline studies and the impacts on the environment, the ideas that we propose mitigation actions or management actions in order to avoid, reduce, mitigate and compensate for negative impacts. It is important also to mention that EIAs are also a requirement for development projects in order to, let's say, construct or that they are given a permission for construction. Some other related concepts, mitigation hierarchy, all these environmental impact assessments should be following the mitigation hierarchy in the sense that they should try first to avoid impacts. Let's say if you have different alternatives of a road project, you should choose the one that would have less impact. So the first step is to avoid, then of course minimize impacts as much as you can. If you think about, for instance, dust emissions, if you reduce the speed, if you limit the speed, if you water the temporary roads, then you could definitely minimize the impacts on your quality. The next step is rehabilitation. So if you have an impact and you're going to remove certain vegetation cover, then vegetation would be the rehabilitation part that I mentioned. And only after you have avoided, minimized, rehabilitated, then you would think about offsetting or compensating. So basically finding areas out of your project area and try to compensate and put efforts in this. And if you think about baseline studies, the main idea is to characterize the environmental conditions of that study area before a project is going to be reconstructed or it's going to be implemented. And in baseline studies, I mentioned that you can retrieve physical data, biological data, social data. I focus on biological data. And in particular, I focus on vegetation data. For instance, in that image that you see, you see more than 60 trans sets of 50 meter length where different species of shrubs and herbaceous plants have been recovered. So you have species data and vegetation cover there. Often these baseline studies are focalized. So you don't have, let's say a lot of data distributed in all your study area, but it's often focused on the areas where the project is going to be implemented. So for instance, in the first two images, you see the location of the plant infrastructure. So imagine that it could be a mining area or it could be some material deposits. And often the sampling is done in the surrounding areas where the project is going to be implemented and maybe in other areas also. But the people who have been working with environmental impact assessments would agree with me that often the projects change. So there's a new infrastructure coming or there's a deviation in the road or whatsoever. So we are in a situation where we have new areas. You see the, not the red polygons, but the purple polygons that often is the case that there's new infrastructure where we don't have field data recovered from those areas. So in this situation, we have two options, right? Go to the field, which would be logical to retrieve more data to characterize those locations. But what I'm suggesting here and many of you have been doing spatial analysis and spatial prediction so we can make the most out of data. And when I mentioned data, I mean biological field data and also Earth observation data. So there are different types of remote sensing data available. If you think about satellite images, is it really great that there's different options and they cover extensive areas and they provide continuous or intermittent spectral data over extensive areas. Remote sensing data, if you think about satellite images, if you compare those two areas A and B there in the screen, you would expect that an area that has more spectral heterogeneity would definitely have more environmental heterogeneity and hence would provide different niches for different species. This is an ongoing discussion whether this applies to different scales, a different spec with different spectral products, but overall the remote sensing data such as satellite images have been already found to be correlated with different soil properties in different areas, for instance in the Amazon or these reflectance values from Landsat data or other products have been related also to floristic patterns in different areas and for different plants in Savannah, South Africa, with trees and shrubs in the Amazon, in grasslands in Europe, in different ecosystems and locations. And other types of remote sensing data such as RSTM or other elevation data have also been related to geological patterns, to soil patterns, hydrology and different climate conditions. So in this sense, reflectance values and elevation and remote sensing are indeed ecologically informative remote sensing layers and at local scales. When we talk about environmental impact assessments often we're not talking about regional scale but it's more of like landscape and local scales. So how can we improve this biodiversity impact assessment? So I suggest this framework that is based in many of the research that you guys have been doing here. And so we have field data focalized from different locations and then we can also have community data, species data. So for each inventory plot or from each transit we know which species are there and with these species matrix or abundance matrix or community matrix we can then summarize this community in an ordination. So in this case this is a floristic ordination, this is a non metric multidimensional scaling. There are different ways of doing these ordinations, this is one of them and in this plot you see if you think about two dots each dot is a plot or a transit and if two dots are close to each other it means that they are floristically similar or that they basically share a lot of species together. So this floristic information or these axes are my response variables in this modern approach that I'm going to show. So you have locations where you go to the field but you also have continuous layers of remote sensing data. So of course you can get biological data from the field but you can also extract different measures or different predictors or different products of remote sensing data. For instance in this case I use Landsat bands, elevation, slope and NDVI and I extract within these different measures like average or median or standard deviation and then you could relate each one of these floristic gradings or floristic access to each one of the predictor variables and that's what you see in this simple linear regression on the right, floristic first gradient and the same as slope. In the second floristic ordination or the second axis the bands three and four so the green and red spectrum of Landsat eight bands also explain quite a lot of this variation in floristic patterns and in the case of the third axis there's not a quite strong relationship, this is a linear relationship of course and that's why I got interested in using other methods or more complicated methods that would be more flexible to the type of data if you think about biological data it's more complicated. So I decided to use different machine learning algorithms initially I started working with random forest so the main idea is to use these response variables from the biological data and using the predictor variables the remote sensing data to map these floristic gradients or patterns and in this image you can see these three axes what you saw in this floristic ordination but predict spatially so in each one of these maps to chorus mean that in that gradient they're sharing different species so they're floristically different but of course you can also represent these you cannot represent this in 3D in the sense as we saw in the ordination but of course you can make a composite of this and that's what you see in this image what I call the singularity map that I put these three predicted axes as a red green and blue composite and in this case also if you think about different colors each color represents an area that has a unique composition of species or for instance this blue color here it means that the composition of species is completely different than this other for instance green colors in the southern part of the area and so this you could also apply different sort of clustering whether it could be a k means or it could be clad a classification random first classification different methods for this and so the idea that you would have an area or a map where each color represents again a unique composition of species and when you have these these singularity maps or also called beta diversity maps then you could properly quantify the impacts on of different development projects on on biodiversity when I mentioned biodiversity I'm talking just about one component of biodiversity of course but we're doing with we're dealing with machine learning we're dealing with spatial predictions so of course we cannot trust every single prediction out there so we should only take into conversation those areas where you have reliable predictions and that's why it's very important to one one method would be to to use this area of applicability developed by mayor and a mayor so we could trust the predictions within these these green areas for instance and if you remember from the first lights this was a let's say a new a new infrastructure that would be implemented and actually it is within the area of applicability so these these these usage of remote sensing and field data for mapping purposes right it's very useful in many ways one of these of course you're gonna make spatial predictions in areas that you don't have field data so you can make the most out of field data and other earth observation data of course the duration that you cannot make spatial predictions you cannot make extrapolation so you should be careful with that definitely and having these biodiversity maps at local scales are suitable many for two reasons in environmental impact assessments first of all you can actually quantify how much areas of certain unique class have been or will be affected by the project and you can also identify suitable areas for like for like compensation schemes or for offsetting I was mentioning that the very last step in the mitigation hierarchy is to offset so if there's nothing you can do you should find areas outside your project and then you could put some efforts there but of course if I'm the forest in a tropical forest I cannot compensate a shrub length right so this like for like by having this unique or singularity maps then you can identify locations where similar to the areas that you're being affected so you could allocate this offsetting or compensation in these in these areas and also through this area of applicability method then you could definitely determine areas that will require higher sampling effort for complementing the environmental impact assessment baseline studies and overall complementing the whole the whole process so that's what I wanted to show today thank you very much for the option thank you very much for for allowing me to be here and for presenting my my work so we have around five minutes in case someone has a question thank you so let's use the question thank you for your question I have two questions I was wondering how you calculate the area of applicability for this case yeah and I was wondering if your impact maps are actually used by by the agencies and is there really change their decision making? Did that happen? Yeah show how how was it used? Yeah uh will you have a question of the area of applicability I I use the the approach by Hannah Mayer and so I derived an area of applicability for each one of the predictions the axis and then I just multiplied like the v0s or ones I derived the whole map of this so that's the first question and the second I really I'm working on this paper in manuscript to to propose this framework and as I mentioned I have used this in the Amazon I have used it in the Andes this can be used in Europe and Russia so the idea is that this framework could be applied in different places myself I have been using this for actual consultancy so people that are doing environmental impact assessments we could make them reduce their impacts on biodiversity so this is actually being used by environmental agencies or private companies but my idea is to put it a little bit higher and actually contact actual environmental agencies in the government level and yeah I'm doing that at the moment. More questions? What about GPIP data? Yeah definitely um in this case I work with community data right so in GPIP data you might have just registers of species but I mean similar things you could do with species distribution models not modeling the community but species and definitely you can use GPIP data to complement some of the registers or if there's a key species that you need to confirm whether it's there or not but definitely GPIP data could complement this this approach. Yeah in this case yes but often it really varies among projects so in this paper that I'm making I have data from different places and it really varies it's not always necessary cluster it depends on how the project is distributed let's say but yeah. Yeah in environmental impact assessment it really depends on the country and the requirements but often if it's a really big project let's say a big mining project then at least you'll have to go twice to collect data in the dry season and the humid season right the rainy season so in that case you only have two moments in the baseline you don't have like let's say monitoring every month this data then you could incorporate this but for instance in these satellite images I downloaded it to the time as close as where the data were collected so this is one dimension in the temporal domain but if you would have more time of collection data then I would definitely like to incorporate this.
 My name is Pablo Perez. I'm originally from Peru, but I'm living in Finland for a while  already. I'm director of environmental services in Finnish overseas consultants or FINOC,  and I'm also a visiting researcher in the University of Turku in Finland. I've been  working mainly in Amazonia for a while, but I'm also working in international consultancy  in different places around the world. I would like to, I wanted to show a little bit of  the ideas that I have on how to use depth observation, how to use field data for mapping  biodiversity patterns so we could improve environmental impact assessments.  So I'll briefly talk a little bit about what environmental impact assessments are, some  related concepts, and then I'll talk about how useful remote sensing data such as satellite  images or elevation can be useful for mapping biodiversity. And then I'll talk briefly about  this framework on how to improve the incorporation of biodiversity in environmental impact assessments  and by using remote sensing and machine learning. Environmental impact assessments and related  concepts, mainly an environmental impact assessment or an EIA, it's a tool or set of processes  that basically does three main things, right? The characterization of the environmental  conditions of a study area, a study area where a project, a development project is going  to be whether it's mining a road project, a wind or other energy projects. And the idea  also is to assess, identify the impacts of these specific development projects on the  environment. And when I mean environment, I talk about the physical, biological, social  and cultural aspects. And then based on these environmental conditions or what is called  so-called baseline studies and the impacts on the environment, the ideas that we propose  mitigation actions or management actions in order to avoid, reduce, mitigate and compensate  for negative impacts. It is important also to mention that EIAs are also a requirement  for development projects in order to, let's say, construct or that they are given a permission  for construction. Some other related concepts, mitigation  hierarchy, all these environmental impact assessments should be following the mitigation  hierarchy in the sense that they should try first to avoid impacts. Let's say if you have  different alternatives of a road project, you should choose the one that would have less  impact. So the first step is to avoid, then of course minimize impacts as much as you  can. If you think about, for instance, dust emissions, if you reduce the speed, if you  limit the speed, if you water the temporary roads, then you could definitely minimize  the impacts on your quality. The next step is rehabilitation. So if you have an impact  and you're going to remove certain vegetation cover, then vegetation would be the rehabilitation  part that I mentioned. And only after you have avoided, minimized, rehabilitated, then  you would think about offsetting or compensating. So basically finding areas out of your project  area and try to compensate and put efforts in this. And if you think about baseline studies,  the main idea is to characterize the environmental conditions of that study area before a project  is going to be reconstructed or it's going to be implemented. And in baseline studies,  I mentioned that you can retrieve physical data, biological data, social data. I focus  on biological data. And in particular, I focus on vegetation data. For instance, in that  image that you see, you see more than 60 trans sets of 50 meter length where different species  of shrubs and herbaceous plants have been recovered. So you have species data and vegetation  cover there. Often these baseline studies are focalized. So you don't have, let's say  a lot of data distributed in all your study area, but it's often focused on the areas  where the project is going to be implemented. So for instance, in the first two images,  you see the location of the plant infrastructure. So imagine that it could be a mining area  or it could be some material deposits. And often the sampling is done in the surrounding  areas where the project is going to be implemented and maybe in other areas also. But the people  who have been working with environmental impact assessments would agree with me that often  the projects change. So there's a new infrastructure coming or there's a deviation in the road  or whatsoever. So we are in a situation where we have new areas. You see the, not the red  polygons, but the purple polygons that often is the case that there's new infrastructure  where we don't have field data recovered from those areas. So in this situation, we have  two options, right? Go to the field, which would be logical to retrieve more data to  characterize those locations. But what I'm suggesting here and many of you have been  doing spatial analysis and spatial prediction so we can make the most out of data. And when  I mentioned data, I mean biological field data and also Earth observation data. So there  are different types of remote sensing data available. If you think about satellite images,  is it really great that there's different options and they cover extensive areas and  they provide continuous or intermittent spectral data over extensive areas. Remote sensing  data, if you think about satellite images, if you compare those two areas A and B there  in the screen, you would expect that an area that has more spectral heterogeneity would  definitely have more environmental heterogeneity and hence would provide different niches for  different species. This is an ongoing discussion whether this applies to different scales,  a different spec with different spectral products, but overall the remote sensing data such as  satellite images have been already found to be correlated with different soil properties  in different areas, for instance in the Amazon or these reflectance values from Landsat data  or other products have been related also to floristic patterns in different areas and for  different plants in Savannah, South Africa, with trees and shrubs in the Amazon, in grasslands  in Europe, in different ecosystems and locations. And other types of remote sensing data such as  RSTM or other elevation data have also been related to geological patterns, to soil patterns,  hydrology and different climate conditions. So in this sense, reflectance values and elevation  and remote sensing are indeed ecologically informative remote sensing layers and at local  scales. When we talk about environmental impact assessments often we're not talking about  regional scale but it's more of like landscape and local scales. So how can we improve this  biodiversity impact assessment? So I suggest this framework that is based in many of the research  that you guys have been doing here. And so we have field data focalized from different locations  and then we can also have community data, species data. So for each inventory plot or from each  transit we know which species are there and with these species matrix or abundance matrix or  community matrix we can then summarize this community in an ordination. So in this case  this is a floristic ordination, this is a non metric multidimensional scaling. There are different  ways of doing these ordinations, this is one of them and in this plot you see if you think about  two dots each dot is a plot or a transit and if two dots are close to each other it means that  they are floristically similar or that they basically share a lot of species together.  So this floristic information or these axes are my response variables in this modern approach  that I'm going to show. So you have locations where you go to the field but you also have  continuous layers of remote sensing data. So of course you can get biological data from the  field but you can also extract different measures or different predictors or different  products of remote sensing data. For instance in this case I use Landsat bands, elevation,  slope and NDVI and I extract within these different measures like average or median or  standard deviation and then you could relate each one of these floristic gradings or floristic  access to each one of the predictor variables and that's what you see in this simple linear regression  on the right, floristic first gradient and the same as slope. In the second floristic  ordination or the second axis the bands three and four so the green and red spectrum of Landsat  eight bands also explain quite a lot of this variation in floristic patterns and in the case  of the third axis there's not a quite strong relationship, this is a linear relationship  of course and that's why I got interested in using other methods or more complicated methods  that would be more flexible to the type of data if you think about biological data it's more  complicated. So I decided to use different machine learning algorithms initially I started working  with random forest so the main idea is to use these response variables from the biological data  and using the predictor variables the remote sensing data to map these floristic gradients or  patterns and in this image you can see these three axes what you saw in this floristic ordination but  predict spatially so in each one of these maps to chorus mean that in that gradient  they're sharing different species so they're floristically different but of course you can  also represent these you cannot represent this in 3D in the sense as we saw in the ordination but  of course you can make a composite of this and that's what you see in this image what I call the  singularity map that I put these three predicted axes as a red green and blue composite and in  this case also if you think about different colors each color represents an area that has a unique  composition of species or for instance this blue color here it means that the composition of  species is completely different than this other for instance green colors in the southern part  of the area and so this you could also apply different sort of clustering whether it could  be a k means or it could be clad a classification random first classification different methods  for this and so the idea that you would have an area or a map where each color represents again  a unique composition of species and when you have these these singularity maps or also called  beta diversity maps then you could properly quantify the impacts on of different development  projects on on biodiversity when I mentioned biodiversity I'm talking just about one component  of biodiversity of course but we're doing with we're dealing with machine learning we're dealing  with spatial predictions so of course we cannot trust every single prediction out there so we  should only take into conversation those areas where you have reliable predictions and that's why  it's very important to one one method would be to to use this area of applicability developed by  mayor and a mayor so we could trust the predictions within these these green areas for instance  and if you remember from the first lights this was a let's say a new a new infrastructure  that would be implemented and actually it is within the area of applicability so these these  these usage of remote sensing and field data for mapping purposes right it's very useful in many ways  one of these of course you're gonna make spatial predictions in areas that you don't have field  data so you can make the most out of field data and other earth observation data of course the  duration that you cannot make spatial predictions you cannot make extrapolation so you should be  careful with that definitely and having these biodiversity maps at local scales are suitable  many for two reasons in environmental impact assessments first of all you can actually quantify  how much areas of certain unique class have been or will be affected by the project  and you can also identify suitable areas for like for like compensation schemes or for offsetting  I was mentioning that the very last step in the mitigation hierarchy is to offset so if there's  nothing you can do you should find areas outside your project and then you could put some efforts  there but of course if I'm the forest in a tropical forest I cannot compensate a shrub length right so  this like for like by having this unique or singularity maps then you can identify locations  where similar to the areas that you're being affected so you could allocate this offsetting or  compensation in these in these areas and also through this area of applicability method then  you could definitely determine areas that will require higher sampling effort for complementing  the environmental impact assessment baseline studies and overall complementing the whole  the whole process so that's what I wanted to show today thank you very much for the  option thank you very much for for allowing me to be here and for presenting my my work so we have  around five minutes in case someone has a question thank you  so  let's use the question  thank you for your question I have two questions I was wondering how you calculate the area of  applicability for this case yeah and I was wondering if your impact maps are actually used by  by the agencies and is there really change their decision making? Did that happen? Yeah show how  how was it used? Yeah uh will you have a question of the area of applicability I I use the the  approach by Hannah Mayer and so I derived an area of applicability for each one of the  predictions the axis and then I just multiplied like the v0s or ones I derived the whole map  of this so that's the first question and the second I really I'm working on this paper  in manuscript to to propose this framework and as I mentioned I have used this in the Amazon I have  used it in the Andes this can be used in Europe and Russia so the idea is that this framework could  be applied in different places myself I have been using this for actual consultancy so people that  are doing environmental impact assessments we could make them reduce their impacts on biodiversity  so this is actually being used by environmental agencies or private companies but my idea is to  put it a little bit higher and actually contact actual environmental agencies in the government  level and yeah I'm doing that at the moment. More questions? What about GPIP data?  Yeah definitely um in this case I work with community data right so in GPIP data you might  have just registers of species but I mean similar things you could do with species distribution  models not modeling the community but species and definitely you can use GPIP data to complement  some of the registers or if there's a key species that you need to confirm whether it's there or not  but definitely GPIP data could complement this this approach.  Yeah in this case yes but often it really varies among projects so in this paper that I'm making  I have data from different places and it really varies it's not always necessary cluster it depends  on how the project is distributed let's say but yeah.  Yeah in environmental impact assessment it really depends on the country and the requirements but  often if it's a really big project let's say a big mining project then at least you'll have to  go twice to collect data in the dry season and the humid season right the rainy season  so in that case you only have two moments in the baseline you don't have like let's say  monitoring every month this data then you could incorporate this but for instance in these  satellite images I downloaded it to the time as close as where the data were collected so this is  one dimension in the temporal domain but if you would have more time of collection data then  I would definitely like to incorporate this.
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