Remote Sensing for Mapping and Monitoring Wetlands and Small Lakes in Southeast Brazil

Thermokarst waterbodies caused by permafrost thawing and degradation are ubiquitous in many subarctic and Arctic regions. They are globally important components of the biogeochemical carbon cycle and have potential feedback effects on climate. These northern waters are mostly small lakes and ponds, and although they may be mapped using very high-resolution satellites or aerial photography, these approaches are generally not suitable for monitoring purposes, due to the cost and limited availability of such images. In this study we evaluated the potential use of widely available high-resolution imagery from Sentinel-2 (S2) for the characterization of the spectral reflectance of thermokarst lakes and ponds. Specifically, we aimed to define the minimum lake area that could be reliably imaged, and to identify challenges and solutions for remote sensing of such waters in the future. The study was conducted in subarctic Canada, in the vicinity of Whapmagoostui-Kuujjuarapik (Nunavik, Québec), an area in the sporadic permafrost zone with numerous thermokarst waterbodies that vary greatly in size. Ground truthing lake reflectance data were collected using an Unmanned Aerial System (UAS) fitted with a multispectral camera that collected images at 13 cm resolution. The results were compared with reflectance from Sentinel-2 images, and the effect of lake area on the reflectance response was assessed. Our results show that Sentinel-2 imagery was suitable for waterbodies larger than 350 m2 once their boundaries were defined, which in the two test sites would allow monitoring from 11% to 30% of the waterbodies and 73% to 85% of the total lake area. Challenges for remote sensing of small lakes include the confounding effects of water reflection (both direct radiation and diffuse), wind and shadow. Given the small threshold area and frequent revisit time, Sentinel-2 provides a valuable approach towards the continuous monitoring of waterbodies, including ponds and small lakes such as those found in thermokarst landscapes. UASs provide a complementary approach for ground truthing and boundary definition.

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Remote Sensing Applied to the Evaluation of Spatial and Temporal Variation of Water Quality in a Coastal Environment, Southeast Brazil

Journal of Geographic Information System ◽

10.4236/jgis.2019.115031 ◽

2019 ◽

Vol 11 (05) ◽

pp. 500-521

Author(s):

Pedro Bettencourt ◽

Julio Cesar Wasserman ◽

Fábio Ferreira Dias ◽

Paulo Roberto Alves ◽

Dandara Bernardino Bezerra ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Temporal Variation ◽

Coastal Environment ◽

Spatial And Temporal Variation ◽

Southeast Brazil

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Assessing water quality by remote sensing in small lakes: the case study of Monticchio lakes in southern Italy

iForest - Biogeosciences and Forestry ◽

10.3832/ifor0507-002 ◽

2009 ◽

Vol 2 (4) ◽

pp. 154-161 ◽

Cited By ~ 23

Author(s):

G Mancino ◽

A Nolè ◽

V Urbano ◽

M Amato ◽

A Ferrara

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Southern Italy ◽

Small Lakes

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Reservoir storage and hydrologic responses to droughts in the Paraná River Basin, Southeast Brazil

10.5194/hess-2016-258 ◽

2016 ◽

Cited By ~ 1

Author(s):

Davi C. D. Melo ◽

Bridget R. Scanlon ◽

Zizhan Zizhan ◽

Edson Wendland ◽

Lei Yin

Keyword(s):

Remote Sensing ◽

Water Supply ◽

River Basin ◽

Paraná River ◽

Monitoring Data ◽

Reservoir Storage ◽

Parana River ◽

Southeast Brazil ◽

Hydrological Droughts ◽

Paraná River Basin

Abstract. Droughts are particularly critical for Brazil because of impacts on water supply and because most (~ 70 %) of its electricity is derived from hydroelectric generation. The Paraná Basin (PB), a major hydroelectric producing region with ~ 32 % of Brazil’s population, recently experienced the most severe drought since the 1960s, compromising the water supply for ~ 11 million people in São Paulo city. The objective of this study was to quantify linkages between meteorological and hydrological droughts based on remote sensing, modeling, and monitoring data using the Parana River Basin in Southeast Brazil as a case study. Two major meteorological droughts were identified in the early 2000s and 2014, with precipitation 20–50 % below the long-term mean. Total water storage estimated from the Gravity Recovery and Climate Experiment (GRACE) satellites declined by ~ 150 km3 between Apr/2011 and Apr/2015. Simulated soil moisture storage declined during the drought, resulting in decreased runoff into reservoirs. Reservoir storage decreased by ~ 30 % relative to the systems maximum capacity, with negative trends ranging from ~ 17 km3 yr-1 (May 1997–Apr 2001) to 25 km3 yr-1 (May 2011–Apr 2015). Storage in upstream reservoirs is mostly controlled by natural climate forcing whereas storage in downstream reservoirs also reflects dam operations. This study emphasizes the importance of integrating remote sensing, modelling, and monitoring data to evaluate droughts and to establish a comprehensive understanding of the linkages between meteorological and hydrological droughts for future management.

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Steady infiltration rate spatial modeling from remote sensing data and terrain attributes in southeast Brazil

Geoderma Regional ◽

10.1016/j.geodrs.2019.e00242 ◽

2020 ◽

Vol 20 ◽

pp. e00242

Author(s):

André Geraldo de Lima Moraes ◽

Daniel Fonseca de Carvalho ◽

Mauro Antônio Homem Antunes ◽

Marcos Bacis Ceddia ◽

Dennis C. Flanagan

Keyword(s):

Remote Sensing ◽

Spatial Modeling ◽

Remote Sensing Data ◽

Infiltration Rate ◽

Sensing Data ◽

Southeast Brazil ◽

Terrain Attributes

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Investigating the hydrometeorological impacts of the 2013-2015 extreme drought in southeast Brazil by combining cluster analysis with land surface modeling

10.5194/egusphere-egu21-10879 ◽

2021 ◽

Author(s):

Rafael Rosolem ◽

Jun Zhang ◽

Lucas Pontes ◽

Humberto da Rocha ◽

Leonardo Domingues

Keyword(s):

Remote Sensing ◽

Cluster Analysis ◽

Land Surface ◽

Model Performance ◽

Sao Paulo ◽

Extreme Drought ◽

Southeastern Brazil ◽

São Paulo ◽

Southeast Brazil ◽

The Impact

The 2013-2015 drought in the southeast Brazil was considered the worst since 1930, affecting more than 21 million people in the Sao Paulo metropolitan region. Previous studies have focused on the meteorological mechanisms and their impact based on low-resolution remote sensing datasets. Here, we simulated this entire drought event at 1 km2 resolution using the Joint UK Land Environment Simulator (JULES). The simulated domain covers large portions of the state of Sao Paulo and Minas Gerais with total area of about 200 thousand km2 (458 km by 463 km). We first investigate the impact of using both global and local datasets (soil and vegetation cover maps) on model performance by comparing the simulated evapotranspiration against the Global Land Evaporation Amsterdam Model. We found that using additional local land cover information together with vegetation-specific leaf area index from remote sensing has significantly improved the model performance while the local soil information has limited influence. Preliminary results suggest a lag of about one month for the drought to propagate from rainfall decrease in December/2013 to soil moisture depletion in January/2014. In addition, we combined the predicted results from JULES with a cluster analysis within the region to further categorized the domain into five groups clusters based on climatic and soil properties. This was done to better understand and explain the key controlling factors associated with the drought over these groups. Overall, we found that clusters with larger soil water storage capacity and slower drainage present more resilience to the drought. This study presents a detailed analysis on the impact of the extreme drought based on a high-resolution land surface model for a large domain in southeastern Brazil, and reveal the specific characteristics of drought propagation processes throughout the 2013-2015 period, adding a more hydrologically-oriented view on the impacts of the 2013-2015 drought to the meteorological findings discussed previously.

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Remote Sensing Drought Indices for the Semi-Arid Region of Southeast Brazil

Modern Environmental Science and Engineering ◽

10.15341/mese(2333-2581)/06.04.2018/007 ◽

2018 ◽

Vol 4 (6) ◽

pp. 537-545

Author(s):

Antônio H. de C. Teixeira ◽

Janice F. Leivas ◽

Fulvio R. Simão ◽

João B. R. S. Reis

Keyword(s):

Remote Sensing ◽

Arid Region ◽

Drought Indices ◽

Southeast Brazil ◽

Semi Arid Region ◽

Semi Arid

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Dual-satellite (Sentinel-2 and Landsat 8) remote sensing of supraglacial lakes in Greenland

The Cryosphere ◽

10.5194/tc-12-3045-2018 ◽

2018 ◽

Vol 12 (9) ◽

pp. 3045-3065 ◽

Cited By ~ 9

Author(s):

Andrew G. Williamson ◽

Alison F. Banwell ◽

Ian C. Willis ◽

Neil S. Arnold

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

Water Volume ◽

Landsat 8 ◽

Total Water ◽

Small Lakes ◽

Supraglacial Lakes ◽

Run Off ◽

Lake Drainage ◽

Sentinel 2

Abstract. Remote sensing is commonly used to monitor supraglacial lakes on the Greenland Ice Sheet (GrIS); however, most satellite records must trade off higher spatial resolution for higher temporal resolution (e.g. MODIS) or vice versa (e.g. Landsat). Here, we overcome this issue by developing and applying a dual-sensor method that can monitor changes to lake areas and volumes at high spatial resolution (10–30 m) with a frequent revisit time (∼3 days). We achieve this by mosaicking imagery from the Landsat 8 Operational Land Imager (OLI) with imagery from the recently launched Sentinel-2 Multispectral Instrument (MSI) for a ∼12 000 km2 area of West Greenland in the 2016 melt season. First, we validate a physically based method for calculating lake depths with Sentinel-2 by comparing measurements against those derived from the available contemporaneous Landsat 8 imagery; we find close correspondence between the two sets of values (R2=0.841; RMSE = 0.555 m). This provides us with the methodological basis for automatically calculating lake areas, depths, and volumes from all available Landsat 8 and Sentinel-2 images. These automatic methods are incorporated into an algorithm for Fully Automated Supraglacial lake Tracking at Enhanced Resolution (FASTER). The FASTER algorithm produces time series showing lake evolution during the 2016 melt season, including automated rapid (≤4 day) lake-drainage identification. With the dual Sentinel-2–Landsat 8 record, we identify 184 rapidly draining lakes, many more than identified with either imagery collection alone (93 with Sentinel-2; 66 with Landsat 8), due to their inferior temporal resolution, or would be possible with MODIS, due to its omission of small lakes <0.125 km2. Finally, we estimate the water volumes drained into the GrIS during rapid-lake-drainage events and, by analysing downscaled regional climate-model (RACMO2.3p2) run-off data, the water quantity that enters the GrIS via the moulins opened by such events. We find that during the lake-drainage events alone, the water drained by small lakes (<0.125 km2) is only 5.1 % of the total water volume drained by all lakes. However, considering the total water volume entering the GrIS after lake drainage, the moulins opened by small lakes deliver 61.5 % of the total water volume delivered via the moulins opened by large and small lakes; this is because there are more small lakes, allowing more moulins to open, and because small lakes are found at lower elevations than large lakes, where run-off is higher. These findings suggest that small lakes should be included in future remote-sensing and modelling work.

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