Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165670
Dario Augusto Borges Oliveira
The use of convolutional neural networks improved greatly data synthesis in the last years and have been widely used for data augmentation in scenarios where very imbalanced data is observed, such as land cover segmentation. Balancing the proportion of classes for training segmentation models can be very challenging considering that samples where all classes are reasonably represented might constitute a small portion of a training set, and techniques for augmenting this small amount of data such as rotation, scaling and translation might be not sufficient for efficient training. In this context, this paper proposes a methodology to perform data augmentation from few samples to improve the performance of CNN-based land cover semantic segmentation. First, we estimate the latent data representation of selected training samples by means of a mixture of Gaussians, using an encoder-decoder CNN. Then, we change the latent embedding used to generate the mixture parameters, at random and in training time, to generate new mixture models slightly different from the original. Finally, we compute the displacement maps between the original and the modified mixture models, and use them to elastically deform the original images, creating new realistic samples out of the original ones. Our disentangled approach allows the spatial modification of displacement maps to preserve objects where deformation is undesired, like buildings and cars, where geometry is highly discriminant. With this simple pipeline, we managed to augment samples in training time, and improve the overall performance of two basal semantic segmentation CNN architectures for land cover semantic segmentation.
{"title":"Augmenting Data Using Gaussian Mixture Embedding For Improving Land Cover Segmentation","authors":"Dario Augusto Borges Oliveira","doi":"10.1109/LAGIRS48042.2020.9165670","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165670","url":null,"abstract":"The use of convolutional neural networks improved greatly data synthesis in the last years and have been widely used for data augmentation in scenarios where very imbalanced data is observed, such as land cover segmentation. Balancing the proportion of classes for training segmentation models can be very challenging considering that samples where all classes are reasonably represented might constitute a small portion of a training set, and techniques for augmenting this small amount of data such as rotation, scaling and translation might be not sufficient for efficient training. In this context, this paper proposes a methodology to perform data augmentation from few samples to improve the performance of CNN-based land cover semantic segmentation. First, we estimate the latent data representation of selected training samples by means of a mixture of Gaussians, using an encoder-decoder CNN. Then, we change the latent embedding used to generate the mixture parameters, at random and in training time, to generate new mixture models slightly different from the original. Finally, we compute the displacement maps between the original and the modified mixture models, and use them to elastically deform the original images, creating new realistic samples out of the original ones. Our disentangled approach allows the spatial modification of displacement maps to preserve objects where deformation is undesired, like buildings and cars, where geometry is highly discriminant. With this simple pipeline, we managed to augment samples in training time, and improve the overall performance of two basal semantic segmentation CNN architectures for land cover semantic segmentation.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115874555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165600
A.A. Silva, T. O. Falcão-Quintela, I. B. Magalhães, L. Pinheiro, L. Pinheiro, M. Calijuri, J. C. de Oliveira, J. Morais
Estuaries are unstable coastal environments of transitional character between the continent and the ocean that plays an important role in fauna reproduction. In general, these types of coastal features have suffered several environmental impacts due to inadequate forms of use and occupation, which result in significant modifications in the morphology of river mouths. This work aims to apply geotechnologies to study the morphological changes at the mouth of Aracatimirim River, Ceará, Brazil, contributing with useful information on the monitoring and environmental management of tropical coastal estuaries. The methodology was divided into three stages: recognition of the study area; field activity and data processing and integration. The Aracatimirim River estuary underwent major changes between 1985 and 2013, where it was possible to identify five evolutionary phases into this period. Previously, built by bar, it evolved to an estuary of coastal plain, accompanied by the progression of Torrões beach and agglutination of “barriers spits” to the continent. It is the main navigable river of the area of Itarema municipality. After the development of this work, it was possible to conclude that the forms of use and occupation in the surroundings of the Aracaratimirim River estuary had a wide growth in the last years, which reflects clearly in the estuary morphology, currently classified as an estuary of coastal plain. The morphological changes within the estuary interfere on not only the environmental conditions, but also in the economy and social conditions of the local community.
{"title":"Geotechnology Applied To The Morphological Analysis Of Aracatimirim River Estuary (Torrões - Ce)","authors":"A.A. Silva, T. O. Falcão-Quintela, I. B. Magalhães, L. Pinheiro, L. Pinheiro, M. Calijuri, J. C. de Oliveira, J. Morais","doi":"10.1109/LAGIRS48042.2020.9165600","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165600","url":null,"abstract":"Estuaries are unstable coastal environments of transitional character between the continent and the ocean that plays an important role in fauna reproduction. In general, these types of coastal features have suffered several environmental impacts due to inadequate forms of use and occupation, which result in significant modifications in the morphology of river mouths. This work aims to apply geotechnologies to study the morphological changes at the mouth of Aracatimirim River, Ceará, Brazil, contributing with useful information on the monitoring and environmental management of tropical coastal estuaries. The methodology was divided into three stages: recognition of the study area; field activity and data processing and integration. The Aracatimirim River estuary underwent major changes between 1985 and 2013, where it was possible to identify five evolutionary phases into this period. Previously, built by bar, it evolved to an estuary of coastal plain, accompanied by the progression of Torrões beach and agglutination of “barriers spits” to the continent. It is the main navigable river of the area of Itarema municipality. After the development of this work, it was possible to conclude that the forms of use and occupation in the surroundings of the Aracaratimirim River estuary had a wide growth in the last years, which reflects clearly in the estuary morphology, currently classified as an estuary of coastal plain. The morphological changes within the estuary interfere on not only the environmental conditions, but also in the economy and social conditions of the local community.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115867183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165607
N. Estrabis, L. Osco, A. P. Ramos, W. Gonçalves, V. Liesenberg, H. Pistori, J. M. Junior
Google Earth Engine (GEE) platform is an online tool, which generates fast solutions in terms of image classification and does not require high performance computers locally. We investigate several data input scenarios for mapping native-vegetation and nonnative-vegetation in the Atlantic Forest region encompassed in a Landsat scene (224/076) acquired on November 28, 2019. The data input scenarios were: I- spectral bands (blue to shortwave infrared); II- NDVI (Normalized Difference Vegetation Index); IIImNDWI (modified Normalized Difference Water Index); IV- scenarios I and II; and V- scenarios I to III. Our results showed that the use of spectral bands added NDVI and mNDWI (scenario V) provided the best performance for the native-vegetation mapping, with accuracy of 96.64% and kappa index of 0.91.
Google Earth Engine (GEE)平台是一个在线工具，它可以快速生成图像分类的解决方案，并且不需要本地的高性能计算机。我们研究了2019年11月28日获取的陆地卫星场景(224/076)中大西洋森林地区原生植被和非原生植被的几种数据输入场景。数据输入场景为:I-光谱波段(蓝色至短波红外);II-归一化植被指数NDVI;iii .修正归一化差水指数;IV-情景I和II;V-情景一至情景三。结果表明，利用NDVI和mNDWI组合的光谱波段(场景V)对原生植被制图效果最好，精度为96.64%，kappa指数为0.91。
{"title":"Brazilian Midwest Native Vegetation Mapping Based on Google Earth Engine","authors":"N. Estrabis, L. Osco, A. P. Ramos, W. Gonçalves, V. Liesenberg, H. Pistori, J. M. Junior","doi":"10.1109/LAGIRS48042.2020.9165607","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165607","url":null,"abstract":"Google Earth Engine (GEE) platform is an online tool, which generates fast solutions in terms of image classification and does not require high performance computers locally. We investigate several data input scenarios for mapping native-vegetation and nonnative-vegetation in the Atlantic Forest region encompassed in a Landsat scene (224/076) acquired on November 28, 2019. The data input scenarios were: I- spectral bands (blue to shortwave infrared); II- NDVI (Normalized Difference Vegetation Index); IIImNDWI (modified Normalized Difference Water Index); IV- scenarios I and II; and V- scenarios I to III. Our results showed that the use of spectral bands added NDVI and mNDWI (scenario V) provided the best performance for the native-vegetation mapping, with accuracy of 96.64% and kappa index of 0.91.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122919780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165634
A. C. M. Luzardi, C. Cárdenas
Most glaciers in Patagonia have been rapidly shrinking during the past decades in response to ongoing global warming. To extend techniques to monitor their dynamics is crucial to understand individual glacier response to climate change and its consequences. In that context, our study aims to investigate recent dynamic behaviour of two near-site outlet glaciers placed at the Southern Patagonian Icefield (Tyndall and Grey glaciers) with the usage of simple and cheap remote sensing techniques. Sentinel-1 images were used to estimate surface velocity by using the Offset-tracking algorithm, while Sentinel-2 images were used to estimate area change in the ice front. Moreover, climatic variables (e.g., accumulated precipitation and air temperature) were analysed in order to assess its influence on glacier dynamics. Our results indicates that precipitation rather then temperature changes has been playing a major role in both glaciers retreat. While Tyndall tends to stabilize its retreat, Grey exponentially enhances retreat by its east tongue. Additionally, mean ice speed was of 0.448 ± 0.242 m.day$^{-1}$ for Grey and 0.439 ± 0.245 m.day$^{-1}$ for Tyndall, which agrees with literature. However, high ice speeds near the ice front indicated by previous work could not be captured here. Our results also suggests that ice flow is a delayed response of precipitation in the accumulation zone, and that may be the cause of decrease in Tyndall’s retreat. Overall, Offset-tracking is an useful tool for studying time series of Patagonian glaciers dynamics. It should be used carefully, however, around high dynamical regions such as the glacier terminus.
{"title":"Recent Dynamical Features of Tyndall and Grey Glaciers, From Southern Patagonian Icefield, By Using Satellite Remote Sensing Techniques","authors":"A. C. M. Luzardi, C. Cárdenas","doi":"10.1109/LAGIRS48042.2020.9165634","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165634","url":null,"abstract":"Most glaciers in Patagonia have been rapidly shrinking during the past decades in response to ongoing global warming. To extend techniques to monitor their dynamics is crucial to understand individual glacier response to climate change and its consequences. In that context, our study aims to investigate recent dynamic behaviour of two near-site outlet glaciers placed at the Southern Patagonian Icefield (Tyndall and Grey glaciers) with the usage of simple and cheap remote sensing techniques. Sentinel-1 images were used to estimate surface velocity by using the Offset-tracking algorithm, while Sentinel-2 images were used to estimate area change in the ice front. Moreover, climatic variables (e.g., accumulated precipitation and air temperature) were analysed in order to assess its influence on glacier dynamics. Our results indicates that precipitation rather then temperature changes has been playing a major role in both glaciers retreat. While Tyndall tends to stabilize its retreat, Grey exponentially enhances retreat by its east tongue. Additionally, mean ice speed was of 0.448 ± 0.242 m.day$^{-1}$ for Grey and 0.439 ± 0.245 m.day$^{-1}$ for Tyndall, which agrees with literature. However, high ice speeds near the ice front indicated by previous work could not be captured here. Our results also suggests that ice flow is a delayed response of precipitation in the accumulation zone, and that may be the cause of decrease in Tyndall’s retreat. Overall, Offset-tracking is an useful tool for studying time series of Patagonian glaciers dynamics. It should be used carefully, however, around high dynamical regions such as the glacier terminus.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116378742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165616
G. Miyoshi, N. Imai, A. Tommaselli, E. Honkavaara
The objective of this study is to evaluate the spectral difference between and within 11 tree species belonging to Brazilian Atlantic Forest located in the countryside of São Paulo State, Brazil. Tree species with different development stages may have different reflectance spectra because of the structural and phenological influence on it. Tree structure can affect the crown spectra due to the differences in geometry of view and density of shadow and sunlight pixels. Additionally, reflectance spectra can be similar between tree species belonging from different species, which affect the classification accuracy of these targets. In this sense, we evaluate the spectral difference between highly diverse Atlantic Forest using two different features extracted from individual tree crowns (ITCs). Mean reflectance spectra and mean normalized reflectance spectra of each ITC were used. They were computed from hyperspectral images acquired with sensor onboard unmanned aerial vehicle. Differences between tree species and within-species were calculated. Wilcoxon-Mann-Whitney test was applied in order to statistically evaluate whether the reflectance spectra were different. Results showed that the normalized values are more propense to increase the classification accuracies than using the mean values since it reduces the variability within-species and present a higher number of different values between-species.
{"title":"Spectral Differences Of Tree Species Belonging To Atlantic Forest Obtained From Uav Hyperspectral Images","authors":"G. Miyoshi, N. Imai, A. Tommaselli, E. Honkavaara","doi":"10.1109/LAGIRS48042.2020.9165616","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165616","url":null,"abstract":"The objective of this study is to evaluate the spectral difference between and within 11 tree species belonging to Brazilian Atlantic Forest located in the countryside of São Paulo State, Brazil. Tree species with different development stages may have different reflectance spectra because of the structural and phenological influence on it. Tree structure can affect the crown spectra due to the differences in geometry of view and density of shadow and sunlight pixels. Additionally, reflectance spectra can be similar between tree species belonging from different species, which affect the classification accuracy of these targets. In this sense, we evaluate the spectral difference between highly diverse Atlantic Forest using two different features extracted from individual tree crowns (ITCs). Mean reflectance spectra and mean normalized reflectance spectra of each ITC were used. They were computed from hyperspectral images acquired with sensor onboard unmanned aerial vehicle. Differences between tree species and within-species were calculated. Wilcoxon-Mann-Whitney test was applied in order to statistically evaluate whether the reflectance spectra were different. Results showed that the normalized values are more propense to increase the classification accuracies than using the mean values since it reduces the variability within-species and present a higher number of different values between-species.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125114602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/lagirs48042.2020.9165673
A. Saha, S. Sastry, Viral A. Dave, R. Ghosh
Tree species classification is an important step towards forest monitoring and biodiversity conservation. This research study evaluates several multispectral image classification techniques for tree species over Ahwa village in Dang district, South Gujarat, India. Multispectral images consisting of 4 bands-R, G, B and NIR collected over 4 months was used. Object-based segmentation using mean shift, cluster-based using K-Means and Gaussian Mixture Model (GMM) and pixel-based methods have been analyzed. Additionally, a new method of classification has been described using the Dynamic Time Warping (DTW) algorithm. It outperformed supervised classification techniques with accuracy over 95%. The GMM+DTW model accurately reflected the actual species distribution found in the ground truth.
{"title":"Evaluation of Tree Species Classification Methods using Multi-Temporal Satellite Images","authors":"A. Saha, S. Sastry, Viral A. Dave, R. Ghosh","doi":"10.1109/lagirs48042.2020.9165673","DOIUrl":"https://doi.org/10.1109/lagirs48042.2020.9165673","url":null,"abstract":"Tree species classification is an important step towards forest monitoring and biodiversity conservation. This research study evaluates several multispectral image classification techniques for tree species over Ahwa village in Dang district, South Gujarat, India. Multispectral images consisting of 4 bands-R, G, B and NIR collected over 4 months was used. Object-based segmentation using mean shift, cluster-based using K-Means and Gaussian Mixture Model (GMM) and pixel-based methods have been analyzed. Additionally, a new method of classification has been described using the Dynamic Time Warping (DTW) algorithm. It outperformed supervised classification techniques with accuracy over 95%. The GMM+DTW model accurately reflected the actual species distribution found in the ground truth.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126809367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165568
G. Martins, J. Nogueira, A. Setzer, F. Morelli
Fires are intrinsic disturbances in ecosystems functioning and structure in fire-prone biomes. In recent decades there has been an increase in the number of fire events in Brazilian biomes, especially due to misuse of fire in the land use and deforestation. The spatial and temporal pattern fire risk is a important way to understanding the seasonality and intensity of fire in different climate and fuel conditions. However, consistent long-term assessment at biome level is only possible with the support of remote sensing and modeling information. Thus, the objective of this work was to evaluate the fire risk patterns for the Brazilian biomes in the last years (2015-2018), using the new version of INPE’s fire risk (FR, v2). Regarding the temporal and spatial FR patterns by this new version from FR model, we evaluated that elevation and latitude correction factors, as well as the meteorological and land cover datasets with finer spatial scales can be contributed to adjust better the fire season vulnerability, notably in the less prone-biomes, such as Mata Atlantica, Pampa and Pantanal. However, there is still a need for adjustment to match the spatial active fire distribution, considering a biomass (fuel) map and the vegetation water status indicators. These improvements help to inform with more accuracy the most fire prone areas to define the strategies and decisions for fire combat and management.
{"title":"Comparison Between Different Versions of INPE’S Fire Risk Model for the Brazilian Biomes","authors":"G. Martins, J. Nogueira, A. Setzer, F. Morelli","doi":"10.1109/LAGIRS48042.2020.9165568","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165568","url":null,"abstract":"Fires are intrinsic disturbances in ecosystems functioning and structure in fire-prone biomes. In recent decades there has been an increase in the number of fire events in Brazilian biomes, especially due to misuse of fire in the land use and deforestation. The spatial and temporal pattern fire risk is a important way to understanding the seasonality and intensity of fire in different climate and fuel conditions. However, consistent long-term assessment at biome level is only possible with the support of remote sensing and modeling information. Thus, the objective of this work was to evaluate the fire risk patterns for the Brazilian biomes in the last years (2015-2018), using the new version of INPE’s fire risk (FR, v2). Regarding the temporal and spatial FR patterns by this new version from FR model, we evaluated that elevation and latitude correction factors, as well as the meteorological and land cover datasets with finer spatial scales can be contributed to adjust better the fire season vulnerability, notably in the less prone-biomes, such as Mata Atlantica, Pampa and Pantanal. However, there is still a need for adjustment to match the spatial active fire distribution, considering a biomass (fuel) map and the vegetation water status indicators. These improvements help to inform with more accuracy the most fire prone areas to define the strategies and decisions for fire combat and management.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127434296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-01DOI: 10.1109/LAGIRS48042.2020.9165656
E. Bontempo, M. Demirel, C. Corsini, F. Martins, D. Valeriano
The mapping of vegetation and Land Cover (LC) is important for research and for public policy planning but, in Brazil, although diverse maps exist there are few studies comparing them. The semiarid region of the Caatinga, in northeastern Brazil is an area long neglected by scientific research and its vegetation is diverse and relatively rich despite years of human occupation and very little preservation effort. In this study we make a comparison between the main maps made for the Caatinga from four different sources: IBGE (Brazilian Institute of Geography and Statistics), TCN (Third National Communication), ProBio (Project for Conservation and Sustainable Use of Biological Biodiversity) and MapBiomas. We also test these maps against well-known Land Cover maps from ESA and NASA: ESA’s GlobCover and Climate Change Initiative (CCI) Land Cover, and NASA’s MODIS MCD12Q1. This was done on a sample area where many of the Caatinga’s vegetation physiognomies can be found, using well-established Difference metrics and the new SPAtial EFficiency (SPAEF) algorithm as they present complementary viewpoints to test the correspondence of mapped classes as well as that of their spatial patterns. Our results show considerable disagreement between the maps tested and their class semantics, with IBGE’s and ProBio’s being the most similar among all national maps and MapBiomas’ the most closely related to global LC maps. The nature of the observed disagreement between these maps shows they diverge not only in the application of their classification systems, but also in their mapped spatial pattern, signaling the need for a better classification system and a better map of vegetation and land cover for the region.
{"title":"Classification System Drives Disagreement Among Brazilian Vegetation Maps at a Sample Area of the Semiarid Caatinga","authors":"E. Bontempo, M. Demirel, C. Corsini, F. Martins, D. Valeriano","doi":"10.1109/LAGIRS48042.2020.9165656","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165656","url":null,"abstract":"The mapping of vegetation and Land Cover (LC) is important for research and for public policy planning but, in Brazil, although diverse maps exist there are few studies comparing them. The semiarid region of the Caatinga, in northeastern Brazil is an area long neglected by scientific research and its vegetation is diverse and relatively rich despite years of human occupation and very little preservation effort. In this study we make a comparison between the main maps made for the Caatinga from four different sources: IBGE (Brazilian Institute of Geography and Statistics), TCN (Third National Communication), ProBio (Project for Conservation and Sustainable Use of Biological Biodiversity) and MapBiomas. We also test these maps against well-known Land Cover maps from ESA and NASA: ESA’s GlobCover and Climate Change Initiative (CCI) Land Cover, and NASA’s MODIS MCD12Q1. This was done on a sample area where many of the Caatinga’s vegetation physiognomies can be found, using well-established Difference metrics and the new SPAtial EFficiency (SPAEF) algorithm as they present complementary viewpoints to test the correspondence of mapped classes as well as that of their spatial patterns. Our results show considerable disagreement between the maps tested and their class semantics, with IBGE’s and ProBio’s being the most similar among all national maps and MapBiomas’ the most closely related to global LC maps. The nature of the observed disagreement between these maps shows they diverge not only in the application of their classification systems, but also in their mapped spatial pattern, signaling the need for a better classification system and a better map of vegetation and land cover for the region.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117286332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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