Mapping irrigated areas using multi-sensor remote sensing data in a Mediterranean environment

Water managers need map of irrigated areas (defined as the identification of their location and their areal extent) to plan a rational use of water under limited availability and to prevent the unauthorized withdrawals. Many authors have shown that the Earth Observation techniques are an effective tool for mapping irrigated areas worldwide at different spatial scales (global/regional/and local). This study presents a methodology for mapping irrigated areas in semi-arid environment based on Earth Observation techniques and by fully exploiting datasets freely available processed by open source software and tools. Data acquired with the Landsat 8 Operational Land Imager (OLI) and the new Sentinel 2A MultiSpectral Instrument (MSI) sensors were integrated to obtain cloud free dense time series allowing to monitor the vegetation development throughout the growing seasons. Irrigated areas were identified by analysing the growing patterns under water deficit conditions from NDVI values under the assumption that, in arid and semi-arid environment (like the Mediterranean Region), high trend of vegetation growth are compatible only with irrigation. The method was applied inside the Cixerri Consortium Irrigation District located in South of Sardinia (Italy).

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Mapping irrigated areas using multi-sensor remote sensing data in a Mediterranean environment

10.7287/peerj.preprints.2155v1 ◽

2016 ◽

Author(s):

Pasquale Nino ◽

Silvia Vanino ◽

Flavio Lupia ◽

Giuseppe Pulighe ◽

Carlo De Michele ◽

...

Keyword(s):

Spatial Scales ◽

Remote Sensing Data ◽

Arid Environment ◽

Earth Observation ◽

Irrigation District ◽

Landsat 8 ◽

Vegetation Development ◽

Vegetation Growth ◽

Growing Seasons ◽

Semi Arid

Water managers need map of irrigated areas (defined as the identification of their location and their areal extent) to plan a rational use of water under limited availability and to prevent the unauthorized withdrawals. Many authors have shown that the Earth Observation techniques are an effective tool for mapping irrigated areas worldwide at different spatial scales (global/regional/and local). This study presents a methodology for mapping irrigated areas in semi-arid environment based on Earth Observation techniques and by fully exploiting datasets freely available processed by open source software and tools. Data acquired with the Landsat 8 Operational Land Imager (OLI) and the new Sentinel 2A MultiSpectral Instrument (MSI) sensors were integrated to obtain cloud free dense time series allowing to monitor the vegetation development throughout the growing seasons. Irrigated areas were identified by analysing the growing patterns under water deficit conditions from NDVI values under the assumption that, in arid and semi-arid environment (like the Mediterranean Region), high trend of vegetation growth are compatible only with irrigation. The method was applied in the Cixerri Consortium Irrigation District located in South of Sardinia (Italy).

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Mapping irrigated areas using multi-sensor remote sensing data in a Mediterranean environment

10.7287/peerj.preprints.2155v2 ◽

2016 ◽

Author(s):

Pasquale Nino ◽

Silvia Vanino ◽

Flavio Lupia ◽

Guido D'Urso ◽

Carlo De Michele ◽

...

Keyword(s):

Spatial Scales ◽

Remote Sensing Data ◽

Arid Environment ◽

Earth Observation ◽

Irrigation District ◽

Landsat 8 ◽

Vegetation Development ◽

Vegetation Growth ◽

Growing Seasons ◽

Semi Arid

Water managers need map of irrigated areas (defined as the identification of their location and their areal extent) to plan a rational use of water under limited availability and to prevent the unauthorized withdrawals. Many authors have shown that the Earth Observation techniques are an effective tool for mapping irrigated areas worldwide at different spatial scales (global/regional/and local). This study presents a methodology for mapping irrigated areas in semi-arid environment based on Earth Observation techniques and by fully exploiting datasets freely available processed by open source software and tools. Data acquired with the Landsat 8 Operational Land Imager (OLI) and the new Sentinel 2A MultiSpectral Instrument (MSI) sensors were integrated to obtain cloud free dense time series allowing to monitor the vegetation development throughout the growing seasons. Irrigated areas were identified by analysing the growing patterns under water deficit conditions from NDVI values under the assumption that, in arid and semi-arid environment (like the Mediterranean Region), high trend of vegetation growth are compatible only with irrigation. The method was applied in the Cixerri Consortium Irrigation District located in South of Sardinia (Italy).

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Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

Remote Sensing ◽

10.3390/rs11222603 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2603

Author(s):

George Xian ◽

Hua Shi ◽

Cody Anderson ◽

Zhuoting Wu

Keyword(s):

Spatial Resolution ◽

Signal To Noise Ratio ◽

Spatial Scales ◽

Regression Tree ◽

Remote Sensing Data ◽

Impervious Surface ◽

Landsat 8 ◽

Signal To Noise ◽

Study Results ◽

The Impact

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

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MODIFIED OPTIMIZATION WATER INDEX (MOWI) FOR LANDSAT-8 OLI/TIRS

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

10.5194/isprs-archives-xlii-4-w4-185-2017 ◽

2017 ◽

Vol XLII-4/W4 ◽

pp. 185-190 ◽

Cited By ~ 2

Author(s):

M. Moradi ◽

M. Sahebi ◽

M. Shokri

Keyword(s):

Water Resources ◽

Spatial Scales ◽

Landsat Imagery ◽

Human Life ◽

Spectral Band ◽

Remote Sensing Data ◽

Particle Swarm Algorithm ◽

Landsat 8 ◽

Water Index ◽

Water Detection

Water is one of the most important resources that essential need for human life. Due to population growth and increasing need of human to water, proper management of water resources will be one of the serious challenges of next decades. Remote sensing data is the best way to the management of water resources due time and cost effectiveness over a greater range of temporal and spatial scales. Between many kinds of satellite data, from SAR to optic or from high resolution to low resolution, Landsat imagery is more interesting data for water detection and management of earth surface water. Landsat8 OLI/TIRS is the newest version of Landsat satellite series. In this paper, we investigated the full spectral potential of Landsat8 for water detection. It is developed many kinds of methods for this purpose that index based methods have some advantages than other methods. Pervious indices just use a limited number of spectral band. In this paper, Modified Optimization Water Index (MOWI) defined by consideration of a linear combination of bands that each coefficient of bands calculated by particle swarm algorithm. The result shows that modified optimization water index (MOWI) has a proper performance on different condition like cloud, cloud shadow and mountain shadow.

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IMPACT ASSESSMENT OF A MINE SUBSIDENCE ON NATIVE VEGETATION OF SOUTH EASTERN COALFIELDS, INDIA

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

10.5194/isprs-archives-xlii-5-487-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 487-490

Author(s):

A. K. Vishwakarma ◽

A. K. Agnihotri ◽

R. Rai ◽

B. K. Shrivastva ◽

S. Mishra

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Adverse Impact ◽

Native Vegetation ◽

Landsat 8 ◽

Vegetation Growth ◽

South Eastern ◽

Wide Range

Abstract. This study aims to evaluate the effect of underground coal mining subsidence on the growth of native vegetation. For this study, an underground coal mine of South Eastern Coalfields Limited (SECL), India was selected. Changes in vegetation indices were analyzed using three remote sensing data of the previous five years. Three period’s Landsat 8 OLI resolution image data were used to calculate Normalized Difference Vegetation Index (NDVI) of the years 2014, 2016 and 2018 in QGIS environment. The study showed that the local grassland and forest were affected by the mining exploitation and subsidence but those effects were not significant to have an adverse impact on the same. The short-term mining was having an impact on the vegetation growth but the effects gradually disappeared with the gradual stabilization of the subsided land and in absence of human interference, vegetation recovered well. In long-term, subsidence was not having a major impact on the vegetation growth. Thus, coal resources exploitation and subsidence of the said mine of SECL did not bring out an adverse impact on a wide range of forest and grassland ecosystems, and these ecosystems could carry the partial destruction and ultimately stabilized ecosystems by self-repair.

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Hydrologic and land use impacts on vegetation growth and NPP at the watershed scale in a semi-arid environment

Regional Environmental Change ◽

10.1007/s10113-006-0014-0 ◽

2006 ◽

Vol 6 (3) ◽

pp. 147-156 ◽

Cited By ~ 16

Author(s):

N. Boulain ◽

B. Cappelaere ◽

L. Séguis ◽

J. Gignoux ◽

C. Peugeot

Keyword(s):

Land Use ◽

Arid Environment ◽

Watershed Scale ◽

Land Use Impacts ◽

Vegetation Growth ◽

Semi Arid

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Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE): 1st modelling intercomparison

10.5194/egusphere-egu21-8492 ◽

2021 ◽

Author(s):

Maria A. Jiménez ◽

Joan Cuxart ◽

Antoni Grau ◽

Aaron Boone ◽

Sylvie Donier ◽

...

Keyword(s):

Land Surface ◽

Numerical Models ◽

Remote Sensing Data ◽

Arid Environment ◽

Surface Interactions ◽

Surface Fluxes ◽

Atmospheric Measurements ◽

Pressure System ◽

Mesoscale Circulations ◽

Semi Arid

Land surface-atmosphere interactions determine the atmospheric boundary layer &#160;(ABL) features, and in the case of semi-arid regions the water availability in the upper ground strongly conditions the surface energy balance and in general the observed dominant processes. LIAISE (Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment, eastern Ebro sub-basin) is an observational campaign planned between spring and fall 2021 designed to study the land/atmosphere interactions and the effect of the surface heterogeneities on the ABL in a semi-arid environment enclosing a large irrigated area in summer.The combined analysis of the ground-based observations and ABL atmospheric measurements, including aircraft and remote-sensing data, is expected to improve the understanding of processes affecting exchange fluxes between the surface and the atmosphere, especially evapotranspiration, and to allow exploring the local and mesoscale circulations induced by the surface heterogeneities. In this sense, mesoscale simulations will be performed over the eastern Ebro sub-basin to contribute to this understanding while evaluating the representation of the surface features in the numerical models and its impact in the organisation of the flow at lower levels.A first mesoscale modelling inter-comparison for a 2016 summer event in the LIAISE area, is under progress, intended to evaluate the performance of the participating models compared to the observations and explore the differences between them, trying to understand the reasons behind them. In this initial phase the models are run at their standard configurations and the comparison is expected to allow improvements in the definitions of the setup of each model for a later phase.Four models participate in the inter-comparison: MesoNH, WRF, UKMO Unified Model and MOLOCH. They are run with similar horizontal (2km x 2km and 400m x 400m for the outer and inner domains) and vertical (2m at lower levels and stretched above) grid meshes and, in this first phase, using their default setup. A 48-h integration is made between 16 and 18 July 2016 for a case under a high-pressure system centred over NW France, with well developed thermally-driven circulations in the Ebro Basin. Sea breezes are found at the coast and seem to reach the basin after surmounting the mountain coastal range.Preliminary results show that each model has a different representation of the surface heterogeneities affecting the grid values of the surface fluxes. Nevertheless, the mesoscale circulations generated by them do not differ significantly between models, the differences lying mostly at smaller scales, namely the ABL characteristics, the values of the exchange fluxes at the surface or the state of the surface and the soil. The challenge at this point is to relate the observed differences to the particularities of the parameterisations and of the physiographic data bases used by each model.&#160;

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