Monitoring temporary ponds dynamics in arid areas with remote sensing and spatial modelling

Abstract. A hydrologic pond model was developed that simulates daily spatial and temporal variations (area, volume and height) of temporary ponds around Barkedji, a village located in the Ferlo Region in Senegal. The model was tested with rainfall input data from a meteorological station and from Tropical Rainfall Measuring Mission (TRMM) satellites. During calibration phase, we used climatic, hydrologic and topographic field data of Barkedji pond collected daily during the 2002 rainy season. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) and a QuickBird satellite image acquired in August 2005 (2.5 m pixel size) were used to apply the hydrologic model to all ponds (98 ponds) of the study area. With input rainfall data from the meteorological station, simulated water heights values for years 2001 and 2002 were significantly correlated with observed water heights for Furdu, Mous 2 and Mous 3 ponds, respectively with 0.81, 0.67 and 0.88 Nash coefficients. With rainfall data from TRMM satellite as model input, correlations were lower, particularly for year 2001. For year 2002, the results were acceptable with 0.61, 0.65 and 0.57 Nash coefficients for Barkedji, Furdu and Mous 3 ponds, respectively. To assess the accuracy of our model for simulating water areas, we used a pond map derived from Quickbird imagery (August 2007). The validation showed that modelled water areas were significantly correlated with observed pond surfaces (r2=0.90). Overall, our results demonstrate the possibility of using a simple hydrologic model with remote sensing data (Quickbird, ASTER DEM, TRMM) to assess pond water heights and water areas of a homogeneous arid area.

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The potential for remote sensing and hydrologic modelling to assess the spatio-temporal dynamics of ponds in the Ferlo Region (Senegal)

Hydrology and Earth System Sciences ◽

10.5194/hess-14-1449-2010 ◽

2010 ◽

Vol 14 (8) ◽

pp. 1449-1464 ◽

Cited By ~ 28

Author(s):

V. Soti ◽

C. Puech ◽

D. Lo Seen ◽

A. Bertran ◽

C. Vignolles ◽

...

Keyword(s):

Remote Sensing ◽

Sensitivity Analysis ◽

Water Level ◽

Catchment Area ◽

Remote Sensing Data ◽

Pond Water ◽

Hydrologic Model ◽

Main Stream ◽

Sensing Data ◽

Water Height

Abstract. In the Ferlo Region in Senegal, livestock depend on temporary ponds for water but are exposed to the Rift Valley Fever (RVF), a disease transmitted to herds by mosquitoes which develop in these ponds. Mosquito abundance is related to the emptying and filling phases of the ponds, and in order to study the epidemiology of RVF, pond modelling is required. In the context of a data scarce region, a simple hydrologic model which makes use of remote sensing data was developed to simulate pond water dynamics from daily rainfall. Two sets of ponds were considered: those located in the main stream of the Ferlo Valley whose hydrological dynamics are essentially due to runoff, and the ponds located outside, which are smaller and whose filling mechanisms are mainly due to direct rainfall. Separate calibrations and validations were made for each set of ponds. Calibration was performed from daily field data (rainfall, water level) collected during the 2001 and 2002 rainy seasons and from three different sources of remote sensing data: 1) very high spatial resolution optical satellite images to access pond location and surface area at given dates, 2) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) data to estimate pond catchment area and 3) Tropical Rainfall Measuring Mission (TRMM) data for rainfall estimates. The model was applied to all ponds of the study area, the results were validated and a sensitivity analysis was performed. Water height simulations using gauge rainfall as input were compared to water level measurements from four ponds and Nash coefficients >0.7 were obtained. Comparison with simulations using TRMM rainfall data gave mixed results, with poor water height simulations for the year 2001 and good estimations for the year 2002. A pond map derived from a Quickbird satellite image was used to assess model accuracy for simulating pond water areas for all the ponds of the study area. The validation showed that modelled water areas were mostly underestimated but significantly correlated, particularly for the larger ponds. The results of the sensitivity analysis showed that parameters relative to pond shape and catchment area estimation have less effects on model simulation than parameters relative to soil properties (rainfall threshold causing runoff in dry soils and the coefficient expressing soil moisture decrease with time) or the water loss coefficient. Overall, our results demonstrate the possibility of using a simple hydrologic model with remote sensing data to track pond water heights and water areas in a homogeneous arid area.

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Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

Geodesy and Cartography ◽

10.22389/0016-7126-2021-973-7-21-31 ◽

2021 ◽

Vol 973 (7) ◽

pp. 21-31

Author(s):

Е.А. Rasputina ◽

A.S. Korepova

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Spatial Resolution ◽

Baikal Region ◽

Remote Sensing Data ◽

Meteorological Station ◽

Sensing Data ◽

Modis Data ◽

Weather Stations ◽

The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

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From Geometric-Optical Remote Sensing Modeling to Quantitative Remote Sensing Science—In Memory of Academician Xiaowen Li

Remote Sensing ◽

10.3390/rs10111764 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1764 ◽

Cited By ~ 2

Author(s):

Qinhuo Liu ◽

Guangjian Yan ◽

Ziti Jiao ◽

Qing Xiao ◽

Jianguang Wen ◽

...

Keyword(s):

Remote Sensing ◽

Thermal Emission ◽

Scale Effects ◽

Remote Sensing Data ◽

Geometric Optic ◽

Optical Remote Sensing ◽

Research Activities ◽

Quantitative Remote Sensing ◽

Vegetation Canopies ◽

High Regard

The academician Xiaowen Li devoted much of his life to pursuing fundamental research in remote sensing. A pioneer in the geometric-optical modeling of vegetation canopies, his work is held in high regard by the international remote sensing community. He codeveloped the Li–Strahler geometric-optic model, and this paper was selected by a member of the International Society for Optical Engineering (SPIE) milestone series. As a chief scientist, Xiaowen Li led a scientific team that made outstanding advances in bidirectional reflectance distribution modeling, directional thermal emission modeling, comprehensive experiments, and the understanding of spatial and temporal scale effects in remote sensing information, and of quantitative inversions utilizing remote sensing data. In addition to his broad research activities, he was noted for his humility and his dedication in making science more accessible for the general public. Here, the life and academic contributions of Xiaowen Li to the field of quantitative remote sensing science are briefly reviewed.

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Estimation of Infiltration Volumes and Rates in Seasonally Water-Filled Topographic Depressions Based on Remote-Sensing Time Series

Sensors ◽

10.3390/s21217403 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7403

Author(s):

Pavel P Fil ◽

Alla Yu Yurova ◽

Alexey Dobrokhotov ◽

Daniil Kozlov

Keyword(s):

Remote Sensing ◽

Time Series ◽

Shallow Groundwater ◽

Remote Sensing Data ◽

Water Bodies ◽

Water Infiltration ◽

Temporary Ponds ◽

Small Water ◽

Negative Exponential ◽

Topographic Depressions

In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balances and estimate volumes of temporary ponds in the Tambov area of Russia. First, small water bodies were automatically recognized in each of a time series of high-resolution Planet Labs images taken in April and May 2021 by object-oriented supervised classification. A training set of water pixels defined in one of the latest images using a small unmanned aerial vehicle enabled high-confidence predictions of water pixels in the earlier images (Cohen’s Κ = 0.99). A digital elevation model was used to estimate the ponds’ water volumes, which decreased with time following a negative exponential equation. The power of the exponent did not systematically depend on the pond size. With adjustment for estimates of daily Penman evaporation, function-based interpolation of the water bodies’ areas and volumes allowed calculation of daily infiltration into the depression beds. The infiltration was maximal (5–40 mm/day) at onset of spring and decreased with time during the study period. Use of the spatially variable infiltration rates improved steady-state shallow groundwater simulations.

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Risk management support through India Remote Sensing Satellites

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-1-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 1-6

Author(s):

N. Aparna ◽

A. V. Ramani ◽

R. Nagaraja

Keyword(s):

Remote Sensing ◽

Risk Management ◽

Satellite Data ◽

Global Climate ◽

Remote Sensing Data ◽

Temporal Variations ◽

Geographical Information ◽

Indian Remote Sensing ◽

Management Support ◽

Sensing Data

Remote Sensing along with Geographical Information System (GIS) has been proven as a very important tools for the monitoring of the Earth resources and the detection of its temporal variations. A variety of operational National applications in the fields of Crop yield estimation , flood monitoring, forest fire detection, landslide and land cover variations were shown in the last 25 years using the Remote Sensing data. The technology has proven very useful for risk management like by mapping of flood inundated areas identifying of escape routes and for identifying the locations of temporary housing or a-posteriori evaluation of damaged areas etc. The demand and need for Remote Sensing satellite data for such applications has increased tremendously. This can be attributed to the technology adaptation and also the happening of disasters due to the global climate changes or the urbanization. However, the real-time utilization of remote sensing data for emergency situations is still a difficult task because of the lack of a dedicated system (constellation) of satellites providing a day-to-day revisit of any area on the globe. The need of the day is to provide satellite data with the shortest delay. Tasking the satellite to product dissemination to the user is to be done in few hours. Indian Remote Sensing satellites with a range of resolutions from 1 km to 1 m has been supporting disasters both National & International. In this paper, an attempt has been made to describe the expected performance and limitations of the Indian Remote Sensing Satellites available for risk management applications, as well as an analysis of future systems Cartosat-2D, 2E ,Resourcesat-2R &RISAT-1A. This paper also attempts to describe the criteria of satellite selection for programming for the purpose of risk management with a special emphasis on planning RISAT-1(SAR sensor).

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REMOTE SENSING AND GIS FOR LAND COVER CLASSIFICATION AND CHANGE DETECTION IN GAZIPUR SADAR, BANGLADESH DURING 1973-2017

Geoplanning Journal of Geomatics and Planning ◽

10.14710/geoplanning.5.2.215-228 ◽

2018 ◽

Vol 5 (2) ◽

pp. 215

Author(s):

Md Arafat Hassan ◽

Rakibul Islam ◽

Rehnuma Mahjabin

Keyword(s):

Remote Sensing ◽

Supervised Classification ◽

Agricultural Land ◽

Satellite Image ◽

Remote Sensing Data ◽

Remote Sensing And Gis ◽

Vegetation Coverage ◽

Accurate Information ◽

Sal Forest ◽

Shed Light

This paper has been developed to capture the land coverage change in Gazipur Sadar Upazila with the help of remote sensing data of 44 years from 1973 to 2017. After acquiring the study area image of 1973, 1991, 2006 and 2017 supervised classification method has been used to get the accurate information from the satellite image and the whole outcome has been transformed into measurable unit (sq km) and graphs. The accuracy of land coverage was ranged from 85% to 89%. The outcome says that the acceleration of economic growth and pressure of huge population took a heavy toll on the vegetation coverage which decreased -199.7%. People are destroying vegetation coverage for building up settlements and infrastructure. In the year 2017, the map shows that the built-up area increased 312.9% for industry, settlement and agricultural purpose. Moreover agricultural land also drops down from 42% to 32%. The rapid rate of decreasing vegetation coverage and small amount of existing vegetation coverage only 57 sq km (in 2017) is a red alert for the region. The Sal forest and other special flora species of that region is valuable resource for environment. This paper shed light on the fact that it is urgent to protect vegetation coverage so it will help the authority to make good policies and use other techniques to save vegetation coverage.

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Monitoring Droughts in the Greater Changbai Mountains Using Multiple Remote Sensing-Based Drought Indices

Remote Sensing ◽

10.3390/rs12030530 ◽

2020 ◽

Vol 12 (3) ◽

pp. 530 ◽

Cited By ~ 5

Author(s):

Yang Han ◽

Ziying Li ◽

Chang Huang ◽

Yuyu Zhou ◽

Shengwei Zong ◽

...

Keyword(s):

Remote Sensing ◽

Time Lag ◽

Remote Sensing Data ◽

Meteorological Drought ◽

Condition Index ◽

Temporal Variations ◽

Changbai Mountains ◽

Drought Indices ◽

Drought Condition ◽

Meteorological Observations

Various drought indices have been developed to monitor drought conditions. Each index has typical characteristics that make it applicable to a specific environment. In this study, six popular drought indices, namely, precipitation condition index (PCI), temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), scaled drought condition index (SDCI), and temperature–vegetation dryness index (TVDI), have been used to monitor droughts in the Greater Changbai Mountains(GCM) in recent years. The spatial pattern and temporal trend of droughts in this area in the period 2001–2018 were explored by calculating these indices from multi-source remote sensing data. Significant spatial–temporal variations were identified. The results of a slope analysis along with the F-statistic test showed that up to 20% of the study area showed a significant increasing or decreasing trend in drought. It was found that some drought indices cannot be explained by meteorological observations because of the time lag between meteorological drought and vegetation response. The drought condition and its changing pattern differ from various land cover types and indices, but the relative drought situation of different landforms is consistent among all indices. This work provides a basic reference for reasonably choosing drought indices for monitoring drought in the GCM to gain a better understanding of the ecosystem conditions and environment.

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Mapping Listvenite Occurrences in the Damage Zones of Northern Victoria Land, Antarctica Using ASTER Satellite Remote Sensing Data

Remote Sensing ◽

10.3390/rs11121408 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1408 ◽

Cited By ~ 16

Author(s):

Amin Beiranvand Pour ◽

Yongcheol Park ◽

Laura Crispini ◽

Andreas Läufer ◽

Jong Kuk Hong ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Thermal Emission ◽

Remote Sensing Data ◽

Component Analysis ◽

Spectral Unmixing ◽

Metasomatic Alteration ◽

Sensing Data ◽

Victoria Land ◽

Mineral Assemblages

Listvenites normally form during hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represent a key indicator for the occurrence of ore mineralizations in orogenic systems. Hydrothermal/metasomatic alteration mineral assemblages are one of the significant indicators for ore mineralizations in the damage zones of major tectonic boundaries, which can be detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data were used to detect listvenite occurrences and alteration mineral assemblages in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL), Antarctica. Spectral information for detecting alteration mineral assemblages and listvenites were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineralogical assemblages containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were detected in the damage zones of the study area by implementing PCA/ICA fusion to visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate lithological groups were mapped and discriminated using PCA/ICA fusion to thermal infrared (TIR) bands of ASTER. Fraction images of prospective alteration minerals, including goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and possible zones encompassing listvenite occurrences were produced using LSU and CEM algorithms to ASTER VNIR+SWIR spectral bands. Several potential zones for listvenite occurrences were identified, typically in association with mafic metavolcanic rocks (Glasgow Volcanics) in the Bowers Mountains. Comparison of the remote sensing results with geological investigations in the study area demonstrate invaluable implications of the remote sensing approach for mapping poorly exposed lithological units, detecting possible zones of listvenite occurrences and discriminating subpixel abundance of alteration mineral assemblages in the damage zones of the Wilson-Bowers and Bowers-Robertson Bay terrane boundaries and in intra-Bowers and Wilson terranes fault zones with high fluid flow. The satellite remote sensing approach developed in this research is explicitly pertinent to detecting key alteration mineral indicators for prospecting hydrothermal/metasomatic ore minerals in remote and inaccessible zones situated in other orogenic systems around the world.

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Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

Geoscientific Model Development ◽

10.5194/gmd-6-1061-2013 ◽

2013 ◽

Vol 6 (4) ◽

pp. 1061-1078 ◽

Cited By ~ 67

Author(s):

G. Picard ◽

L. Brucker ◽

A. Roy ◽

F. Dupont ◽

M. Fily ◽

...

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Thermal Emission ◽

Remote Sensing Data ◽

Microwave Emission ◽

Alpine Regions ◽

Wide Range ◽

Dense Media ◽

Wet Snow ◽

Snow Conditions

Abstract. DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1–200 GHz similar to those acquired routinely by space-based microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large ice-sheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python.

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History of the Donguz-Orun Glacier from bioindication, historical, cartographic sources and remote sensing data

Journal Ice and Snow ◽

10.15356/2076-6734-2018-4-448-461 ◽

2018 ◽

Vol 58 (4) ◽

pp. 448-461

Author(s):

O. N. Solomina ◽

I. S. Bushueva ◽

P. D. Polumieva ◽

E. A. Dolgova ◽

M. D. Dokukin

Keyword(s):

Remote Sensing ◽

Satellite Image ◽

Remote Sensing Data ◽

Ice Age ◽

Aerial Photographs ◽

The Caucasus ◽

Mountain Glaciers ◽

Mountain Valley ◽

The Past ◽

History Of

On the basis of dendrochronological, lichenometric and historical data with the use of Earth remote sensing materials, the evolution of the Donguz-Orun Glacier has been reconstructed over the past centuries. In this work we used aerial photographs of 1957, 1965, 1981, 1987, satellite image of 2009, as well as descriptions, photographs, maps and plans of the glacier of the 19th and 20th centuries, data of instrumental measurements of the glacier end position in the second half of the 20th – early 21st centuries, dendrochronological dating of pine on the front part of the valley, and juniper to date coastal moraines, and the results of lichenometry studies. It has been established that the Donguz-Orun Glacier in the past had several clearly marked advances about 100, 200 and more than 350 years ago, which are expressed in relief in the form of uneven-aged coastal moraines. Despite the fact that the Donguz-Orun Glacier diﬀers from many mountain-valley glaciers of the Caucasus primarily by its predominantly avalanche feeding and a moraine cover, almost entirely covering its surface, the main periods of its advances are consistent with the known large ﬂuctuations of mountain glaciers during the Little Ice Age in the early 20th, early 19th, and, probably, in the middle of the 17th century. However, unlike most other Caucasian glaciers, the Donguz-Orun Glacier advanced in the 1970s–2000s. Te scale of its degradation from the end of the 19th to the beginning of the 21st century is also uncharacteristic for the Caucasus: the reduction in the length for longer than a century period is only about 100 m.

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