Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: A Case Study of Alento, Italy

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

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Soil Surface Reflectance as a Tool to Estimate Water Infiltration Rate from UAV Platforms

10.5194/egusphere-egu2020-2092 ◽

2020 ◽

Author(s):

Nicolas Francos ◽

Eyal Ben Dor ◽

Nunzio Romano ◽

Paolo Nasta ◽

Briggita Szabó ◽

...

Keyword(s):

Soil Properties ◽

Precision Agriculture ◽

Soil Surface ◽

Infiltration Rate ◽

Soil Survey ◽

Water Infiltration ◽

Surface Reflectance ◽

Spectral Measurements ◽

Laboratory Conditions ◽

Spectral Libraries

Soil is an essential component in the environment and is vital for food security. It provides ecosystem services, filters water, supplies nutrients to plants, provides us with food, stores carbon, regulates greenhouse gases emissions and it affects our climate. Traditional soil survey methodologies are complicated, expensive, and time-consuming. Visible and infrared spectroscopy can effectively characterize soil properties. Spectral measurements are rapid, precise and inexpensive. The spectra contain information about soil properties, which comprises minerals, organic compounds, and water. Today, several Soil Spectral Libraries (SSLs) are being created worldwide because these datasets have a notable potential to be used as training datasets for machine learning methods that will benefit precision agriculture activity for better management of food production. Nonetheless, as SSL's are created under laboratory conditions it is not clear if it can be used to infer field conditions in situ and/or from the sky. Thus, study the relationship between RS, field spectroscopy and the laboratory measurements of soil is very important. Accordingly, this study postulates that traditional SSLs don't simulate the real spectral signatures in the field that both, satellite and airborne sensors measure as well, because they are affected by factors that are not an integral part of the soil, such as: moisture, litter, human and animal activity, plow, grass, dung, waste, etc&#8230; However, under laboratory conditions, these factors are usually removed for the preparation of SSLs. Thus, given the several SSLs available, it is necessary to evaluate the protocols that were used in these SSLs. The objective of this study is to evaluate the gap between field and laboratory spectral measurements through the analysis of the performance of spectral based models. This procedure combined two soil spectral libraries that contain 114 samples that were measured in the laboratory as well as in the field. The nature of the dataset is varied, because these samples were collected from six different fields in three countries of the Mediterranean basin: Israel, Greece and Italy. Moreover, 63 samples are mainly sandy and 51 are mainly clayey. In order to obtain optimal spectral measurements in the field, we used a new optical apparatus that simulates the sun's radiation. Next, we generated PLSR models to estimate one of the most important hydrological parameters namely &#8220;infiltration rate&#8221; that control the runoff stage, soil erosion and water storage in the soil profile. This property is strongly affected by the surface characteristics. Finally, the field based spectral model was adapted to an UAV hyperspectral sensor in order to estimate the infiltration rate from the sky. The results were successfully validated in field, and we concluded that for the estimation of the infiltration rate, SSLs must be created using surface reflectance in field because laboratory protocols can be detrimental for the performance of the dataset in question.&#160;

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Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

Sensors ◽

10.3390/s18113855 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3855 ◽

Cited By ~ 9

Author(s):

Lin Bai ◽

Cuizhen Wang ◽

Shuying Zang ◽

Changshan Wu ◽

Jinming Luo ◽

...

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Soil Ph ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Hyperspectral Data ◽

Validation Dataset ◽

Least Squares Regression ◽

Songnen Plain ◽

Mean Square Errors

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China.

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Estimating Soil Arsenic Content with Visible and Near-Infrared Hyperspectral Reflectance

Sustainability ◽

10.3390/su12041476 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1476 ◽

Cited By ~ 1

Author(s):

Lei Han ◽

Rui Chen ◽

Huili Zhu ◽

Yonghua Zhao ◽

Zhao Liu ◽

...

Keyword(s):

Near Infrared ◽

Back Propagation ◽

Hyperspectral Data ◽

Pollution Monitoring ◽

World Health ◽

Support Vector ◽

Inversion Method ◽

Arsenic Content ◽

Least Squares Regression ◽

Soil Arsenic

Soil arsenic (AS) contamination has attracted a great deal of attention because of its detrimental effects on environments and humans. AS and inorganic AS compounds have been classified as a class of carcinogens by the World Health Organization. In order to select a high-precision method for predicting the soil AS content using hyperspectral techniques, we collected 90 soil samples from six different land use types to obtain the soil AS content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A partial least squares regression (PLSR), a support vector regression (SVR), and a back propagation neural network (BPNN) were used to establish a relationship between the hyperspectral and the soil AS content to predict the soil AS content. In addition, the feasibility and modeling accuracy of different interval spectral resampling, different spectral pretreatment methods, feature bands, and full-band were compared and discussed to explore the best inversion method for estimating soil AS content by hyperspectral. The results show that 10 nm + second derivative (SD) + BPNN is the optimum method to predict soil AS content estimation; R v 2 is 0.846 and residual predictive deviation (RPD) is 2.536. These results can expand the representativeness and practicability of the model to a certain extent and provide a scientific basis and technical reference for soil pollution monitoring.

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Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data

Remote Sensing ◽

10.3390/rs12213573 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3573

Author(s):

J. Malin Hoeppner ◽

Andrew K. Skidmore ◽

Roshanak Darvishzadeh ◽

Marco Heurich ◽

Hsing-Chung Chang ◽

...

Keyword(s):

Chlorophyll Content ◽

Spectral Resolution ◽

Near Infrared ◽

Deciduous Forest ◽

Mixed Forest ◽

Hyperspectral Data ◽

Least Squares Regression ◽

Area Index ◽

Management Interventions ◽

Shortwave Infrared

Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.

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Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis

Scientific Reports ◽

10.1038/s41598-019-52802-5 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

Salah El-Hendawy ◽

Nasser Al-Suhaibani ◽

Majed Alotaibi ◽

Wael Hassan ◽

Salah Elsayed ◽

...

Keyword(s):

Near Infrared ◽

Agricultural Sector ◽

Hyperspectral Data ◽

Field Conditions ◽

Least Squares Regression ◽

Hyperspectral Reflectance ◽

Full Spectrum ◽

Salinity Levels ◽

Shortwave Infrared ◽

Related Trait

Abstract The timely estimation of growth and photosynthetic-related traits in an easy and nondestructive manner using hyperspectral data will become imperative for addressing the challenges of environmental stresses inherent to the agricultural sector in arid conditions. However, the handling and analysis of these data by exploiting the full spectrum remains the determining factor for refining the estimation of crop variables. The main objective of this study was to estimate growth and traits underpinning photosynthetic efficiency of two wheat cultivars grown under simulated saline field conditions and exposed to three salinity levels using hyperspectral reflectance information from 350–2500 nm obtained at two years. Partial least squares regression (PLSR) based on the full spectrum was applied to develop predictive models for estimating the measured parameters in different conditions (salinity levels, cultivars, and years). Variable importance in projection (VIP) of PLSR in combination with multiple linear regression (MLR) was implemented to identify important waveband regions and influential wavelengths related to the measured parameters. The results showed that the PLSR models exhibited moderate to high coefficients of determination (R2) in both the calibration and validation datasets (0.30–0.95), but that this range of R2 values depended on parameters and conditions. The PLSR models based on the full spectrum accurately and robustly predicted three of four parameters across all conditions. Based on the combination of PLSR-VIP and MLR analysis, the wavelengths selected within the visible (VIS), red-edge, and middle near-infrared (NIR) wavebands were the most sensitive to all parameters in all conditions, whereas those selected within the shortwave infrared (SWIR) waveband were effective for some parameters in particular conditions. Overall, these results indicated that the PLSR analysis and band selection techniques can offer a rapid and nondestructive alternative approach to accurately estimate growth- and photosynthetic-related trait responses to salinity stress.

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Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8100437 ◽

2019 ◽

Vol 8 (10) ◽

pp. 437 ◽

Cited By ~ 5

Author(s):

Yiping Peng ◽

Li Zhao ◽

Yueming Hu ◽

Guangxing Wang ◽

Lu Wang ◽

...

Keyword(s):

Near Infrared ◽

Regional Scale ◽

Soil Nutrient ◽

Hyperspectral Data ◽

Estimation Accuracy ◽

Soil Productivity ◽

Least Squares Regression ◽

Near Infrared Reflectance ◽

Nutrient Contents ◽

Soil Nutrient Contents

Quickly and efficiently monitoring soil nutrient contents using remote sensing technology is of great significance for farmland soil productivity, food security and sustainable agricultural development. Current research has been conducted to estimate and map soil nutrient contents in large areas using hyper-spectral techniques, however, it is difficult to obtain accurate estimates. In order to improve the estimation accuracy of soil nutrient contents, we introduced a GA-BPNN method, which combined a back propagation neural network (BPNN) with the genetic algorithm optimization (GA). This study was conducted in Guangdong, China, based on soil nutrient contents and hyperspectral data. The prediction accuracies from a partial least squares regression (PLSR), BPNN and GA-BPNN were compared using field observations. The results showed that (1) Among three methods, the GA-BPNN provided the most accurate estimates of soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) contents; (2) Compared with the BPNN models, the GA-BPNN models significantly improved the estimation accuracies of the soil nutrient contents by decreasing the relative root mean square error (RRMSE) values by 15.9%, 5.6% and 20.2% at the sample point level, and 20.1%, 16.5% and 47.1% at the regional scale for TN, TP and TK, respectively. This indicated that by optimizing the parameters of BPNN, the GA-BPNN provided greater potential to improving the estimation; and (3) Soil TK content could be more accurately mapped by the GA-BPNN method using HuanJing-1A Hyperspectral Imager (HJ-1A HSI) (manufacturer: China Aerospace Science and Technology Corporation; Beijing, China) data with a RRMSE value of 20.37% than the soil TN and TP with the RRMSE values of 40.41% and 34.71%, respectively. This implied that the GA-BPNN model provided the potential to map the soil TK content for the large area. The research results provided an important reference for high-accuracy prediction of soil nutrient contents.

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On-Site VIS-NIR Spectral Reflectance and Colour Measurements—A Fast and Inexpensive Alternative for Delineating Sediment Layers Quantitatively? A Case Study from a Monumental Bronze Age Burial Mound (Seddin, Germany)

Heritage ◽

10.3390/heritage3020031 ◽

2020 ◽

Vol 3 (2) ◽

pp. 528-548 ◽

Cited By ~ 1

Author(s):

Vincent Haburaj ◽

Moritz Nykamp ◽

Jens May ◽

Philipp Hoelzmann ◽

Brigitta Schütt

Keyword(s):

Bronze Age ◽

Near Infrared ◽

Image Data ◽

Hyperspectral Data ◽

Burial Mound ◽

Spectral Measurements ◽

Administrative District ◽

Colour Measurements ◽

Sediment Layers ◽

Archaeological Excavations

Quantitative sediment analyses performed in the laboratory are often used throughout archaeological excavations to critically reflect on-site stratigraphic delineation. Established methods are, however, often time-consuming and expensive. Recent studies suggest that systematic image analysis can objectivise the delineation of stratigraphic layers based on fast quantitative spectral measurements. The presented study examines how these assumptions prevail when compared to modern techniques of sediment analysis. We examine an archaeological cross-section at a Bronze Age burial mound near Seddin (administrative district Prignitz, Brandenburg, Germany), consisting of several layers of construction-related material. Using detailed on-site descriptions supported by quantitatively measured sediment properties as a measure of quality, we compare clustering results of (i) extensive colour measurements conducted with an RGB and a multispectral camera during fieldwork, as well as (ii) selectively sampled sedimentological data and (iii) visible and near infrared (VIS-NIR) hyperspectral data, both acquired in the laboratory. Furthermore, the influence of colour transformation to the CIELAB colour space (Commission Internationale de l’Eclairage) and the possibilities of predicting soil organic carbon (SOC) based on image data are examined. Our results indicate that quantitative spectral measurements, while still experimental, can be used to delineate stratigraphic layers in a similar manner to traditional sedimentological data. The proposed processing steps further improved our results. Quantitative colour measurements should therefore be included in the current workflow of archaeological excavations.

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Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada

Remote Sensing ◽

10.3390/rs11111298 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1298 ◽

Cited By ~ 3

Author(s):

Ahmed Laamrani ◽

Aaron A. Berg ◽

Paul Voroney ◽

Hannes Feilhauer ◽

Line Blackburn ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Near Infrared ◽

Hyperspectral Data ◽

Support Vector ◽

Least Squares Regression ◽

Agricultural Field ◽

Southern Ontario ◽

Spectral Bands ◽

Selection Of

The recent use of hyperspectral remote sensing imagery has introduced new opportunities for soil organic carbon (SOC) assessment and monitoring. These data enable monitoring of a wide variety of soil properties but pose important methodological challenges. Highly correlated hyperspectral spectral bands can affect the prediction and accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful to describing SOC. This study evaluates the efficiency of visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data to identify the most informative hyperspectral bands responding to SOC content in agricultural soils. Soil samples (111) were collected over an agricultural field in southern Ontario, Canada and analyzed against two hyperspectral datasets: An airborne Nano-Hyperspec imaging sensor with 270 bands (400–1000 nm) and a laboratory hyperspectral dataset (ASD FieldSpec 3) along the 1000–2500 nm range (NIR-SWIR). In parallel, a multimethod modeling approach consisting of random forest, support vector machine, and partial least squares regression models was used to conduct band selections and to assess the validity of the selected bands. The multimethod model resulted in a selection of optimal band or regions over the VNIR and SWIR sensitive to SOC and potentially for mapping. The bands that achieved the highest respective importance values were 711–715, 727, 986–998, and 433–435 nm regions (VNIR); and 2365–2373, 2481–2500, and 2198–2206 nm (NIR-SWIR). Some of these bands are in agreement with the absorption features of SOC reported in the literature, whereas others have not been reported before. Ultimately, the selection of optimal band and regions is of importance for quantification of agricultural SOC and would provide a new framework for creating optimized SOC-specific sensors.

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Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil Profile

Applied and Environmental Soil Science ◽

10.1155/2012/439567 ◽

2012 ◽

Vol 2012 ◽

pp. 1-9 ◽

Cited By ~ 8

Author(s):

Naftali Goldshleger ◽

Alexandra Chudnovsky ◽

Eyal Ben-Dor

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Spectral Properties ◽

Clay Content ◽

Soil Surface ◽

Infiltration Rate ◽

Water Infiltration ◽

Morphological Properties ◽

Pls Regression ◽

Artificial Neural

We explored the effect of raindrop energy on both water infiltration into soil and the soil's NIR-SWIR spectral reflectance (1200–2400 nm). Seven soils with different physical and morphological properties from Israel and the US were subjected to an artificial rainstorm. The spectral properties of the crust formed on the soil surface were analyzed using an artificial neural network (ANN). Results were compared to a study with the same population in which partial least-squares (PLS) regression was applied. It was concluded that both models (PLS regression and ANN) are generic as they are based on properties that correlate with the physical crust, such as clay content, water content and organic matter. Nonetheless, better results for the connection between infiltration rate and spectral properties were achieved with the non-linear ANN technique in terms of statistical values (RMSE of 17.3% for PLS regression and 10% for ANN). Furthermore, although both models were run at the selected wavelengths and their accuracy was assessed with an independent external group of samples, no pre-processing procedure was applied to the reflectance data when using ANN. As the relationship between infiltration rate and soil reflectance is not linear, ANN methods have the advantage for examining this relationship when many soils are being analyzed.

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AVALIAÇÃO DO COMPORTAMENTO HIDROGEOLÓGICO DAS FORMAÇÕES GEOLÓGICAS EM UMA VERTENTE DA SERRA DO GANDARELA: RIO ACIMA, MG

e-xacta ◽

10.18674/exacta.v10i1.1780 ◽

2017 ◽

Vol 10 (1) ◽

pp. 1

Author(s):

Fernanda Bárbaro Franco ◽

Sidney Portilho ◽

Juliana Batista de Souza

Keyword(s):

Soil Surface ◽

Field Capacity ◽

Infiltration Rate ◽

Water Infiltration ◽

Metropolitan Region ◽

Infiltration Process ◽

Important Region ◽

Quadrilátero Ferrífero ◽

Water Recharge ◽

Belo Horizonte

A Serra do Gandarela apresenta uma das maiores reservas hídricas do Quadrilátero Ferrífero e seus aquíferos são de extrema importância para as áreas de drenagens das bacias hidrográficas ali presentes. Possui grande grau de conservação, belezas naturais e uma grande biodiversidade. É uma região que abriga várias espécies vegetais endêmicas e a canga, afloramentos ferruginosos, que é um dos sistemas ecológicos mais ameaçado do Brasil. Esse artigo visa trabalhar a relação entre os solos, coberturas de superfície da Serra do Gandarela e o comportamento hidrológico dos mesmos, demonstrando a capacidade de campo, armazenamento de água, e as taxas de infiltração de água de cada ponto amostrado. Dos três pontos selecionados dois apresentaram bons resultados quanto à recarga hídrica. O primeiro ponto por apresentar um sistema lento de infiltração e percolação e o segundo ponto por infiltrar grande quantidade de água. O terceiro ponto apresentou uma taxa de infiltração menor, por possuir a textura da parte cimentante da matriz coluvionar (argilo – arenosa), o que interferiu negativamente no processo de infiltração. Relacionando todos os pontos com os respectivos resultados verifica-se que a Serra do Gandarela é uma região importante para o processo de recarga hídrica da região metropolitana de Belo Horizonte. ABSTRACTSerra do Gandarela presents one of the biggest hydric stock of the Ferriferous Quadrangle and its aquifers are of utmost importance for draining areas of these existing watersheds.It has a great conservation degree, natural beauties, a great biodiversity. It's a region wich shelters several vegetal endemic species and the « canga », ferruginous outcrops, which is one of the most endangered ecological systems in Brazil. This article aims to work the relationship between the soil surface, covers the Serra do Gandarela and the hydrological behavior of the same, demonstrating the field capacity, water storage,and water infiltration rates of each chozen location. Of the three selected points two showed good results as to water recharge. The first point by presenting a slow infiltration and percolation system and the second point for infiltrating large amount of water. The third point presented a lower infiltration rate by having the texture of the cementitious matrix of the colluvial (clayey - sandy) which negatively interfere with the infiltration process. Listing all the points with the results it appears that the Serra do Gandarela is an important region for the water refilling process of the metropolitan region of Belo Horizonte.

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