Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies

Small water bodies (SWBs), such as ponds and on-farm reservoirs, are a key part of the hydrological system and play important roles in diverse domains from agriculture to conservation. The monitoring of SWBs has been greatly facilitated by medium-spatial-resolution satellite images, but the monitoring accuracy is considerably affected by the mixed-pixel problem. Although various spectral unmixing methods have been applied to map sub-pixel surface water fractions for large water bodies, such as lakes and reservoirs, it is challenging to map SWBs that are small in size relative to the image pixel and have dissimilar spectral properties. In this study, a novel regression-based surface water fraction mapping method (RSWFM) using a random forest and a synthetic spectral library is proposed for mapping 10 m spatial resolution surface water fractions from Sentinel-2 imagery. The RSWFM inputs a few endmembers of water, vegetation, impervious surfaces, and soil to simulate a spectral library, and considers spectral variations in endmembers for different SWBs. Additionally, RSWFM applies noise-based data augmentation on pure endmembers to overcome the limitation often arising from the use of a small set of pure spectra in training the regression model. RSWFM was assessed in ten study sites and compared with the fully constrained least squares (FCLS) linear spectral mixture analysis, multiple endmember spectral mixture analysis (MESMA), and the nonlinear random forest (RF) regression without data-augmentation. The results showed that RSWFM decreases the water fraction mapping errors by ~ 30%, ~15%, and ~ 11% in root mean square error compared with the linear FCLS, MESMA unmixings, and the nonlinear RF regression without data-augmentation respectively. RSWFM has an accuracy of approximately 0.85 in R2 in estimating the area of SWBs smaller than 1 ha

Assessment of spatio-temporal direction of impervious surface area surface temperature in Pretoria, South Africa

Over the years, rapid urban growth has led to the conversion of natural lands into large man-made landscapes due to enhanced political and economic growth. This study assessed the spatio-temporal change characteristics of impervious surface area (ISA) expansion using its surface temperature (LST) at selected administrative subplace units (i.e., local region scale). ISA was estimated for 1995, 2005 and 2015 from Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images using a Random Forest (RF) algorithm. The spatio-temporal trends of ISA were assessed using an optimal analytical scale to aggregate ISA LST coupled with weighted standard deviational ellipse (SDE) method. The ISA was quantified with high predictive accuracy (i.e., AUROC = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015) using RF classifier. More than 70% of the selected administrative subplaces in Pretoria experienced an increase in growth rate (415.59%) between 1995 and 2015. LST computations from the Landsat TIRS bands yielded good results (RMSE = ∼1.44OC, 1.40OC, ∼0.86OC) for 1995, 2005 and 2015 respectively. Based on the hexagon polygon grid (90x90), the aggregated ISA surface temperature weighted SDE analysis results indicated ISA expansion in different directions at the selected administrative subplace units. Our findings can represent useful information for policymakers in evaluating urban development trends in Pretoria, City of Tshwane (COT).The University of South Africa Student Funding Directorate (UNISA, DSF) and GeoTerraImage (Pty) Ltd.https://www.tandfonline.com/loi/tgei20hj2023Geography, Geoinformatics and MeteorologyPlant Production and Soil Scienc

INTEGRAÇÃO DA INCERTEZA NA AMOSTRAGEM E CLASSIFICAÇÃO RANDOM FOREST UTILIZANDO BANDAS E ÍNDICES ESPECTRAIS PARA O MAPEAMENTO DE INUNDAÇÃO: Integration of uncertainty in sampling and random forest classification using bands and spectral indices for flood mapping

Traditional classifications present limitations for mapping floods due to mixing the spectral response of water with adjacent non-aquatic targets or similar spectral response of non-aquatic targets with water. Furthermore, in general, these classifications are evaluated only in terms of overall accuracy without considering the uncertainties in the classification process. Thus, this study aimed to integrate uncertainty in the Random Forest (RF) classification process for flood mapping, which guided the sampling process. The classification used 21 variables including indices and spectral bands from the Operational Land Imager sensor of the Landsat-8 satellite. Sampling was performed initially with the selection of points from the visual interpretation of the satellite image and later by collecting samples with high Shannon entropy values in the uncertainty map. The variables with the greatest importance for classification were selected by the Recursive Feature Elimination (RFE) algorithm. The final RF classification using samples collected based on the uncertainty map and with the four selected variables by the RFE presented an accuracy of 98.0% and a reduction of uncertainty, which indicates a greater confidence in the spatial representation and quantification of water permanent and temporary surface associated with floods. Keywords: Flood mapping. Random Forest Classifier. Spectral bands and indices. Variable selection. Shannon Entropy.Classificações tradicionais apresentam limitações para o mapeamento de inundações devido à mistura da resposta espectral da água com alvos adjacentes não aquáticos ou resposta espectral similar de alvos não aquáticos com a água. Além disso, em geral, as classificações são avaliadas apenas em termos de acurácia global sem considerar as incertezas no processo de classificação. Assim, neste estudo objetivou-se integrar a incerteza na classificação Random Forest (RF) para o mapeamento de inundações auxiliando o processo de amostragem. A classificação utilizou 21 variáveis representadas por bandas e índices espectrais do sensor Operational Land Imager do satélite Landsat-8. A amostragem foi realizada inicialmente com a seleção de pontos a partir da interpretação visual da imagem de satélite e posteriormente coletando amostras com alta entropia de Shannon no mapa de incerteza. As variáveis com maior importância para a classificação foram selecionadas utilizando o algoritmo Recursive Feature Elimination (RFE). Os resultados mostram que a classificação RF final usando amostras coletadas com base no mapa de incerteza e o conjunto de variáveis selecionadas pelo RFE apresentou 98,0% de exatidão e redução das incertezas do mapeamento da água superficial em relação à classificação RF com todas as variáveis e sem considerar a amostragem baseada na incerteza. Palavras-chave: Mapeamento de inundação. Classificador Random Forest. Bandas e índices espectrais. Seleção de variáveis. Entropia de Shannon

Remote sensing of impervious surface area and its interaction with land surface temperature variability in Pretoria, South Africa

Includes summary for chapter 1-5Pretoria, City of Tshwane (COT), Gauteng Province, South Africa is one of the cities that continues to experience rapid urban sprawl as a result of population growth and various land use, leading to the change of natural vegetation lands into impervious surface area (ISA). These are associated with transportation (paved roads, streets, highways, parking lots and sidewalks) and cemented buildings and rooftops, made of completely or partly impermeable artificial materials (e.g., asphalt, concrete, and brick). These landscapes influence the micro-climate (e.g., land surface temperature, LST) of Pretoria City as evidenced by the recent heat waves characterized by high temperature. Therefore, understanding ISA changes will provide information for city planning and environmental management. Conventionally, deriving ISA information has been dependent on field surveys and manual digitizing from hard copy maps, which is laborious and time-consuming. Remote sensing provides an avenue for deriving spatially explicit and timely ISA information. Numerous methods have been developed to estimate and retrieve ISA and LST from satellite imagery. There are limited studies focusing on the extraction of ISA and its relationship with LST variability across major cities in Africa. The objectives of the study were: (i) to explore suitable spectral indices to improve the delineation of built-up impervious surface areas from very high resolution multispectral data (e.g., WorldView-2), (ii) to examine exposed rooftop impervious surface area based on different colours, and their interplay with surface temperature variability, (iii) to determine if the spatio-temporal built-up ISA distribution pattern in relation to elevation influences urban heat island (UHI) extent using an optimal analytical scale and (iv) to assess the spatio-temporal change characteristics of ISA expansion using the corresponding surface temperature (LST) at selected administrative subplace units (i.e., local region scale). The study objectives were investigated using remote sensing data such as WorldView-2 (a very high-resolution multispectral sensor), medium resolution Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) at multiple scales. The ISA mapping methods used in this study can be grouped into two major categories: (i) the classification-based approach consisting of an object-based multi-class classification with overall accuracy ~90.4% and a multitemporal pixel-based binary classification. The latter yielded an area under the receiver operating characteristic curve (AUROC) = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015. (ii) the spectral index-based approach such as a new built-up extraction index (NBEI) derived in this study which yielded a high AUROC = ~0.82 compared to Built-up Area Index (BAI) (AUROC = ~0.73), Built-up spectral index (BSI) (AUROC = ~0.78), Red edge / Green Index (RGI) (AUROC = ~0.71) and WorldView-Built-up Index (WV-BI) (AUROC = ~0.67). The multitemporal built-up Index (BUI) also estimated with AUROC = 0.8487 for 1993, AUROC = 0.8302 for 2003, AUROC = 0.8790 for 2013. This indicates that all these methods employed, mapped ISA with high predictive accuracy from remote sensing data. Furthermore, the single-channel algorithm (SCA) was employed to retrieve LST from the thermal infrared (TIR) band of the Landsat images. The LST overall retrieval error for the entire study generally was quite low (overall root mean square RMSE ≤ ~1.48OC), which signifies that the Landsat TIR used provided good results for further analysis. In conclusion, the study showed the potential of multispectral remote sensing data to quantify ISA and evaluate its interaction with surface temperature variability despite the complex urban landscape in Pretoria. Also, using impervious surface LST as a complementary metric in this research helped to reveal urban heat island distribution and improve understanding of the spatio-temporal developing trend of urban expansion at a local spatial scale.Rapid urbanization because of population growth has led to the conversion of natural lands into large man-made landscapes which affects the micro-climate. Rooftop reflectivity, material, colour, slope, height, aspect, elevation are factors that potentially contribute to temperature variability. Therefore, strategically designed rooftop impervious surfaces have the potential to translate into significant energy, long-term cost savings, and health benefits. In this experimental study, we used the semi-automated Environment for Visualizing Images (ENVI) Feature Extraction that uses an object-based image analysis approach to classify rooftop based on colours from WorldView-2 (WV-2) image with overall accuracy ~90.4% and kappa coefficient ~0.87 respectively. The daytime retrieved surface temperatures were derived from 15m pan-sharpened Landsat 8 TIRS with a range of ~14.6OC to ~65OC (retrieval error = 0.38OC) for the same month covering Lynwood Ridge a residential area in Pretoria. Thereafter, the relationship between the rooftops and surface temperature (LST) were examined using multivariate statistical analysis. The results of this research reveal that the interaction between the applicable rooftop explanatory features (i.e., reflectance, texture measures and topographical properties) can explain over 22.10% of the variation in daytime rooftop surface temperatures. Furthermore, analysis of spatial distribution between mean daytime surface temperature and the residential rooftop indicated that the red, brown and green roof surfaces show lower LST values due to high reflectivity, high emissivity and low heat capacity during the daytime. The study concludes that in any study related to the spatial distribution of rooftop impervious surface area surface temperature, effect of various explanatory variables must be considered. The results of this experimental study serve as a useful approach for further application in urban planning and sustainable development.Evaluating changes in built-up impervious surface area (ISA) to understand the urban heat island (UHI) extent is valuable for governments in major cities in developing countries experiencing rapid urbanization and industrialization. This work aims at assessing built-up ISA spatio-temporal and influence on land surface temperature (LST) variability in the context of urban sprawl. Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) were used to quantify ISA using built-up Index (BUI) and spatio-temporal dynamics from 1993-2013. Thereafter using a suitable analytical sampling scale that represents the estimated ISA-LST, we examined its distribution in relation to elevation using the Shuttle Radar Topography Mission (SRTM) and also create Getis-Ord Gi* statistics hotspot maps to display the UHI extent. The BUI ISA extraction results show a high predictive accuracy with area under the receiver operating characteristic curve, AUROC = 0.8487 for 1993, AUROC = 0.8302 for 2003, AUROC = 0.8790 for 2013. The ISA spatio-temporal changes within ten years interval time frame results revealed a 14% total growth rate during the study year. Based on a suitable analytical scale (90x90) for the hexagon polygon grid, the majority of ISA distribution across the years was at an elevation range of between >1200m – 1600m. Also, Getis-Ord Gi* statistics hotspot maps revealed that hotspot regions expanded through time with a total growth rate of 19% and coldspot regions decreased by 3%. Our findings can represent useful information for policymakers by providing a scientific basis for sustainable urban planning and management.Over the years, rapid urban growth has led to the conversion of natural lands into large man-made landscapes due to enhanced political and economic growth. This study assessed the spatio-temporal change characteristics of impervious surface area (ISA) expansion using its surface temperature (LST) at selected administrative subplace units (i.e., local region scale). ISA was estimated for 1995, 2005 and 2015 from Landsat-5 Thematic Mapper (TM) and Landsat-8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) images using a Random Forest (RF) algorithm. The spatio-temporal trends of ISA were assessed using an optimal analytical scale to aggregate ISA LST coupled with weighted standard deviational ellipse (SDE) method. The ISA was quantified with high predictive accuracy (i.e., AUROC = 0.8572 for 1995, AUROC = 0.8709 for 2005, AUROC = 0.8949 for 2015) using RF classifier. More than 70% of the selected administrative subplaces in Pretoria experienced an increase in growth rate (415.59%) between 1995 and 2015. LST computations from the Landsat TIRS bands yielded good results (RMSE = ~1.44OC, 1.40OC, ~0.86OC) for 1995, 2005 and 2015 respectively. Based on the hexagon polygon grid (90x90), the aggregated ISA surface temperature weighted SDE analysis results indicated ISA expansion in different directions at the selected administrative subplace units. Our findings can represent useful information for policymakers in evaluating urban development trends in Pretoria, City of Tshwane (COT).Globally, the unprecedented increase in population in many cities has led to rapid changes in urban landscape, which requires timely assessments and monitoring. Accurate determination of built-up information is vital for urban planning and environmental management. Often, the determination of the built-up area information has been dependent on field surveys, which is laborious and time-consuming. Remote sensing data is the only option for deriving spatially explicit and timely built-up area information. There are few spectral indices for built-up areas and often not accurate as they are specific to impervious material, age, colour, and thickness, especially using higher resolution images. The objective of this study is to test the utility of a new built-up extraction index (NBEI) using WorldView-2 to improve built-up material mapping irrespective of material type, age and colour. The new index was derived from spectral bands such as Green, Red edge, NIR1 and NIR2 bands that profoundly explain the variation in built-up areas on WorldView-2 image (WV-2). The result showed that NBEI improves the extraction of built-up areas with high accuracy (area under the receiver operating characteristic curve, AUROC = ~0.82) compared to the existing indices such as Built-up Area Index (BAI) (AUROC = ~0.73), Built-up spectral index (BSI) (AUROC = ~0.78 ), Red edge / Green Index (RGI) (AUROC = ~0.71) and WorldView-Built-up Index (WV-BI) (AUROC = ~0.67). The study demonstrated that the new built-up index could extract built-up areas using high-resolution images. The performance of NBEI could be attributed to the fact that it is not material specific, and would be necessary for urban area mapping.Environmental SciencesD. Phil. (Environmental Sciences

Qualidade ambiental do estuário Mamanguape atrvés da bioindicação de diatomáceas (Bacillariophyta)

In order to characterize the environmental quality and multitemporias changes Mamanguape River Estuary, remote sensing analyzes were performed (IVs) Vegetation Index and Rating and analysis of diatom community in sediments. Bordered by two protected areas, both federal liability management (CUs), the APA of the Mamanguape River Bar (APA Mamanguape) and ARIE Foz do Mamanguape River (ARIE Mamanguape), the estuary is an important conservation and refuge area for Trichechus manatus. The general hypothesis of this study were: i) conservation units, although quite pressured by anthropogenic changes have served effectively the conservation of Mamanguape estuary and, ii) the species of diatoms found along the estuary and the physicochemical factors indicating high quality environmental body aquatic. For analysis of multitemporal estuary, modifications over the years it was used vegetation and classification index. Six images were obtained corresponding to the region analyzed by Glovis website, for the years 1985, 1994, 1999, 2001, 2010 and 2016. The images were processed in the program ERDAS version 9.3 and analyzed by the NDWI indices Albedo and Classification. It was done Pearson correlation to analyze possible associations between data accumulated rainfall and analyzed indices. The NDWI and Albedo found correlation with the accumulated rainfall, different classification which showed that monoculture (previous deployment creating UC) and the tanks of shrimp (implementation in 2001) increased sharply by 2016, with a decrease in vegetation south associated with monoculture in both conservation areas, with further expansion in APA Mamanguape. For diatoms analysis, the twelve sampling points were predisposed according to the presence of human disturbances (aquaculture tanks, the presence of solid waste and the absence of these factors) at two different periods a higher flow estuary and a lower flow (drought and flood). The ebb was obtained in ANA's website through HIDROWEB software that provides the streamflow data. It was collected in situ Dissolved Oxygen (DO), temperature of water and air, electrical conductivity, water transparency (Seccli), pH, and samples of surface water for later total phosphorus analysis (Pt) and orthophosphate (Ortho-P). Sediment samples were obtained for Pt analyzes, Ortho-P, organic matter and diatoms. 47 taxa was detected diatomaceous distributed on both hydroperiods, with the region of greater richness ZM characterized in mangrove vegetation is composed of silt and clay sediment. There were no major changes of taxa in both periods analyzed, except Seminavis cf. robusta, Pleurosigma aestuari, Cocconeis sp., and Grammatophora sp. found only in the period of higher flow. The species that were present in all zones were Diploneis cf. bombus and Diploneis sp. For ZM were recorded high availability of Pt both in water and sediment and high abundance of diatom species. In conclusion: i) the classification index was more effective in understanding the different landscapes and changes in protected areas, indicating structural changes in vegetation and activity progress with economic purposes; ii) the region of mangrove (ZM) has acted as a nutrient filter and diatomaceous arising both from the continent and ocean, as well as the areas of continental shrimp tanks.Com o objetivo de caracterizar a qualidade ambiental e as modificações multitemporias do Estuário do Rio Mamanguape, foram realizadas análises de sensoriamento remoto (IVs), Índices de Vegetação e Classificação, bem como análises da comunidade de diatomáceas em sedimentos. Delimitado por duas Unidades de Conservação (UCs), ambas com gestão de responsabilidade federal, a Área de Proteção Ambiental da Barra do Rio Mamanguape (APA Mamanguape) e a Área de Relevante Interesse Ecológico da Foz dos Manguezais do Rio Mamanguape (ARIE Mamanguape), o estuário representa uma importante área de conservação e refúgio para Trichechus manatus. As hipóteses gerais deste trabalho foram: i) As unidades de conservação, embora bastante pressionadas pelas modificações antrópicas, têm atendido de maneira efetiva a conservação do estuário Mamanguape e, ii) As espécies de diatomáceas encontradas ao longo do estuário e os fatores físico-químicos indicam alta qualidade ambiental do corpo aquático. Para análise de modificações multitemporais do estuário ao longo dos anos usou-se índice de vegetação e classificação. Foram obtidas seis imagens correspondentes a região analisada através do site do GloVis, dos anos de 1985, 1994, 1999, 2001, 2010 e 2016. As imagens foram processadas no programa ERDAS versão 9.3 e analisadas através dos índices NDWI, Albedo e Classificação. Foi feita correlação de Pearson para analisar possíveis associações entre os dados de precipitação pluviométrica acumulada e os índices analisados. O NDWI e o Albedo constataram correlação com a precipitação acumulada, diferente da classificação que mostrou que a monocultura (implantação anterior à criação da UC) e os tanques de carcinicultura (implantação em 2001) aumentaram acentuadamente até 2016, com um recuo na vegetação a sul associado à monocultura nas duas áreas de conservação, com maior expansão na APA Mamanguape. Em relação a análise diatomológica, os doze pontos de amostragem foram predispostos de acordo com a presença de modificações antrópicas (tanques de carcinicultura, presença de resíduos sólidos e ausência desses fatores), em dois períodos distintos um de maior vazão do estuário e um de menor vazão (estiagem e enchente). A vazante foi obtida no site da ANA através do software HidroWeb que disponibiliza os dados fluviométricos. Foram coletados in situ Oxigênio Dissolvido (OD), temperatura da água e do ar, condutividade elétrica, transparência da água (Seccli) e pH e amostras de água superficial para posterior análise de fósforo total (Pt) e ortofosfato (Orto-P). Amostras de sedimento foram obtidas para análises de Pt, Orto-P, Matéria Orgânica e diatomáceas. Detectou-se 47 táxons de diatomáceas distribuídos nos dois hidroperíodos, sendo a região de maior riqueza a ZM caracterizada por vegetação de mangue e sedimento composto de silte e argila. Não houve grandes alterações de táxons nos dois períodos analisados, com exceção da Seminavis cf. robusta., Pleurosigma aestuari, Cocconeis sp. e Grammatophora sp. encontrada apenas no período de maior vazão. As espécies que estiveram presentes em todas as Zonas foram Cocconeis sp., Diploneis cf. bombus e Diploneis sp. Para ZM foram registradas altas disponibilidades de Pt, tanto na água, como no sedimento, e alta abundância de espécies de diatomáceas. Em conclusão: i) o índice de classificação foi mais eficaz na compreensão das diferentes paisagens e de alterações nas UCs, indicando mudanças na estrutura vegetação e avanço de atividades com finalidades econômicas; ii) a região de manguezal (ZM) tem atuado como filtro de nutrientes e de diatomáceas advindos tanto do continente, como oceânicas, bem como das áreas de tanques de carcinicultura continentais

Flood Extent and Volume Estimation using Multi-Temporal Synthetic Aperture Radar.

Ph. D. Thesis.Satellite imagery has the potential to monitor flooding across wide geographical regions. Recent launches have improved the spatial and temporal resolution of available data, with the European Space Agency (ESA) Copernicus programme providing global imagery at no end-user cost. Synthetic Aperture Radar (SAR) is of particular interest due to its ability to map flooding independent of weather conditions. Satellite-derived flood observations have real-world application in flood risk management and validation of hydrodynamic models. This thesis presents a workflow for estimating flood extent, depth and volume utilising ESA Sentinel-1 SAR imagery. Flood extents are extracted using a combination of change detection, variable histogram thresholding and object-based region growing. An innovative technique has been developed for estimating flood shoreline heights by combining the inundation extents with high-resolution terrain data. A grid-based framework is used to derive the water surface from the shoreline heights, from which water depth and volume are calculated. The methodology is applied to numerous catchments across the north of England that suffered from severe flooding throughout the winter of 2015-16. Extensive flooding has been identified throughout the study region, with peak inundation occurring on 29th December 2015. On this date, over 100 km2 of flooding is identified in the Ouse catchment, equating to a water volume of 0.18 km3. The SAR flood extents are validated against satellite optical imagery, achieving a Total Accuracy of 91% and a Critical Success Index of 77%. The derived water surfaces have an average error of 3 cm and an RMSE of 98 cm compared to river stage measurements. The methods developed are robust and globally applicable, shown with an additional study along the Mackenzie River in Australia. The presented methodology, alongside the increased temporal resolution provided by Sentinel-1, highlights the potential for accurate, reliable mapping of flood dynamics using satellite imagery.NERC, (DREAM) CD

Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China

Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated areas in Yuyao City in China using medium resolution optical imagery. MESMA was adopted to tackle the mixing pixel problem induced by medium resolution data. Specifically, 35 optimal endmembers were selected to construct a total of 3111 models in the MESMA procedure to derive accurate fraction information. A multi-dimensional feature space was constructed including the normalized difference water index (NDWI), topographical parameters of height, slope, and aspect together with the fraction maps. A Random Forest classifier consisting of 200 decision trees was adopted to classify the post-flood image based on the above multi-features. Experimental results indicated that the proposed method can extract the inundated areas precisely with a classification accuracy of 94% and a Kappa index of 0.88. The inclusion of fraction information can help improve the mapping accuracy with an increase of 2.5%. Moreover, the proposed method also outperformed the maximum likelihood classifier and the NDWI thresholding method. This research provided a useful reference for flood mapping using medium resolution optical remote sensing imagery

Atlas digital de las subcuencas Birrís y Páez, Cartago, Costa Rica

Proyecto de Graduación (Licenciatura en Ingeniería Forestal) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Forestal, 2021Las subcuencas de los ríos Páez y Birrís son de vital importancia ya que son tributarias de la cuenca del río Reventazón. Sin embargo, debido a la intensa actividad agrícola desarrollada en la zona, se han presentado problemas en las plantas hidroeléctricas a causa del arrastre de sedimentos. Con la ley N° 8023 se creó el Plan de manejo de la cuenca del Reventazón para, realizar acciones concretas en las subcuencas Birrís y Páez. Para ello en el año 2011 se estableció la Comisión para el Manejo y Recuperación de la Subcuenca del río Birrís-Páez (COBIRRÍS-PÁEZ). Este trabajo tiene el objetivo de generar un atlas digital para las subcuencas de los ríos Birrís y Páez como herramienta en el manejo integral de ambas cuencas. En total se crearon 15 capas geográficas, sin embargo 4 de ellas son las más representativas, ya que se calcularon índices importantes como valores de elevación, tiempo de concentración y caudal máximo para cada una de las microcuencas. Se identificó el sistema de drenajes permanentes de las subcuencas para realizar la delimitación de las microcuencas, en total se ubicaron 70 microcuencas. La capa de uso del suelo representa la cobertura actual para el año 2020, la clasificación realizada presenta una exactitud global de 75%, con 6 clasificaciones. Entre las demás capas realizadas se encuentran temperatura, precipitación, cantones, distritos y suelo. Finalmente se generó una guía de usuario que permite a los interesados del atlas, conocer el procedimiento para el cálculo de caudal máximo.The sub-catchments of the Páez and Birrís rivers are of vital importance as tributaries of the Reventazón river subbasins. However, due to the high agricultural activity developed in the area, there have been problems in hydroelectric plants due to the dragging of sediments. With Law 8023, the Reventazón Basin Management Plan was created to carry out concrete actions in the Birrís and Páez subbasins. To do this, the Commission for the Management and Recovery of the Birrís-Páez River watershed (COBIRRÍS-PÁEZ) was created in 2011. This work has the objective of generating a digital atlas for the subbasins of the Birrís and Páez rivers as a tool in the integral management of basins. In total 15 geographic layers were created, however 4 of them are the most representative and important because they provide indices such as elevation values, concentration time and maximum flow for each of the microbasins. In addition, the permanent drainage system of each subbasins was identified to delimit the microbasins, in total 70 microbasins were identidied. The land use layer represents the current coverage for the year 2020, the classification carried out presents a global accuracy of 75%, with 6 land cover classes. Among other generated layers are: temperature, precipitation, counties, country districts and soil. Finally, a user guide was generated that allows atlas users to know the procedure for calculating maximum Flow