In recent years, remote sensing has emerged as one of the most useful tools in agronomy. A series of biophysical indicators can be derived from satellite images and used as inputs for decision support systems in irrigation management, crop planning or determination of crop yields, thus achieving a better management of resources. An automated system implemented under the IDEaRM services provides these indicators on a daily rate to support in decision-making.This work has been done through the project “Telerieg – Remote sensing use for irrigation practice recommendation and monitoring in the SUDOE space” financed by Programme (SUDOE), which supports regional development through European Regional Development Fund (ERDF) co-financing of transnational projects

Erena Arrabal, Manuel

García, S.

Hernández, Z.

Lendrino, G.

Pecci, J.

Sánchez, D.

English

Repositorio Digital de la Universidad Politécnica de Cartagena

4th Workshop on Agri-food Research - WiA.15. Cartagena, Murcia, Spain. 11-12 May 2015 201 | 269  Calculation of biophysical indicators through the automatic processing of satellite images  M. Erena Arrabal (1), D. Sánchez (1), J. Pecci (2), G. Lendrino (2),  Z. Hernández (1), S. García (3)   (1) IMIDA, C/ Mayor s/n, 30.150. La Alberca. Murcia.manuel.erena@carm.es (2) INDRA Sistemas S.A., c/ Mar Egeo, nº 4. P. Industrial, N º1. 28830. San Fernando de Henares. Madrid. (3) UPCT, Grupo de I+D Gestión de Recursos Hídricos. Paseo Alfonso XIII, 52. 30203. Cartagena.  Abstract  In recent years, remote sensing has emerged as one of the most useful tools in agronomy. A series of biophysical indicators can be derived from satellite images and used as inputs for decision support systems in irrigation management, crop planning or determination of crop yields, thus achieving a better management of resources. An automated system implemented under the IDEaRM services provides these indicators on a daily rate to support in decision-making. Keywords: remote sensing, NDVI, SDI.    1. Introduction The term Spatial Data Infrastructure was first coined in 1993 by the US National Research Council to refer to a framework of technologies, policies and institutional arrangements that, working together, facilitate the creation, sharing and use of geospatial data and resource related information via a distributed information system. Later, in 1994, the US government creates the NSDI (National Spatial Data Infrastructure) and that same year he founded the Open GIS Consortium, later renamed Open Geospatial Consortium (OGC). 13 years after the EU adopted Directive 2007/2 / EC Infrastructure for Spatial Information in Europe-INSPIRE [1].  The Spatial Data Infrastructures -IDEaRM whose main objective is to integrate agriculture data, metadata and geographic information in the regional level. Providing all users with the location services, identification, selection and access to such resources through a geoportal (http://idearm.imida.es), which also integrate interoperable resources producers of geographic information globally, EU, national, regional and local (Fig. 1) [2]. Remote sensing science and technology, and its applications in various fields, have experienced a successful development in recent decades. The Earth observation from space has become an irreplaceable tool for monitoring various environmental processes of great importance. Desertification processes, deforestation and forest loss or damage assessment after flooding are some aspects that can be studied by remote sensing.  One of the most important features of remote sensing is its ability to extract thematic information from certain measurements of the sensor. That is, other factors that help us better understand our environment can be derived from sensor data. For example, the chlorophyll content is a variable not directly measured by the sensor, but affects the spectral reflectance of the vegetation. So, it can be estimated indirectly by observing on which spectral bands its effect is more evident and isolating this component from other factors that may also influence such bands [3].  The IDEaRM objective is to improve the recommendations and monitoring of irrigation practices in major crops of the area of the Murcia Region. To achieve it, we have designed an automatic processing system to generate remote sensing products combining data from NOAA, MODIS and LANDSAT 8 satellite with data from a network of agro-meteorological stations. This system sets in motion a chain of data-driven processes (i.e., triggered by the availability of data). One of the goals of this work is to develop an automatic system for processing remote sensing images from the U.S. Geological Survey-USGS, available at the regional level that is updated using interoperability standards proposed by the OGC, and as a basis for implementing the INSPIRE directive in this subject area.  2. Materials and methods  4th Workshop on Agri-food Research - WiA.15. Cartagena, Murcia, Spain. 11-12 May 2015 202 | 269  2.1 Landsat-8 data  Landsat 8 satellite images the entire Earth every 16 days and carries two instruments: The Operational Land Imager (OLI) sensor and the Thermal Infrared Sensor (TIRS) sensor. Landsat 8 data products are consistent with the all standard Level-1 specifications: OLI multispectral bands 1-7,9: 30-meters; OLI panchromatic band 8: 15-meters; TIRS bands 10-11: 100 meters; Orthorectified; 16-bit pixel value Table 1 [4] .   2.2 Automatic processing system The LANDSAT-8 images are downloaded by http and stored into directory. The products are automatically detected and they are ingested in the products archive.  The sensor network is registering the air temperature observations each hour. This data are collected and stored by the automatic system (Fig. 2).  Each LANDSAT-8 image received is processed to generate the following products covering the area of Murcia: Radiance, Reflectance, NDVI, LSTB10, LSTB11, SAVI, LAI and AT. The descriptions of the products are available on the catalogue of website. http://idearm.imida.es/  2.3 SOS Server The system makes use of OGC standard web services: SOS (Sensor Observation Service) is used to store and make available the in-situ data observations. Its role is to collect the observations from the sensor network. This implementation is foreseen to be based on SOS 52º North open source implementation [5].   2.4 Reflectance The standard Landsat 8 products provided by the USGS EROS Center consist of quantized and calibrated scaled Digital Numbers (DN) representing multispectral image data acquired by both the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). The products are delivered in 16-bit unsigned integer format and can be rescaled to the Top Of Atmosphere (TOA) reflectance and/or radiance using radiometric rescaling coefficients provided in the product metadata file.  The MTL file contains the thermal constants needed to convert TIRS data to the at-satellite brightness temperature.  Further details can be found in the LDCM Cal/Val algorithm description document and the Landsat 8 Science Users’ Handbook available from the Landsat website [4].  2.5 Products  The Normalized difference vegetation index (NDVI), proposed by Rouse [5], is one of the most widely used vegetation indices. This index is based on the distinctive radiometric behaviour of vegetation throughout certain spectral windows. Healthy vegetation shows a characteristic spectral signature with a clear contrast between the visible bands, especially the red band (0.630 - 0.680 µm) and near-infrared (0.845 - 0.885 µm). This marked difference between the absorption spectrum in the visible and near-infrared (NIR) of healthy vegetation allows for distinguishing it from those suffering some kind of stress (water stress, for example, caused by drought), in which there is less reflectance in the NIR and greater absorption in the visible. Thus we can conclude that the greater the contrast between the reflectance of both bands, the greater the vigour of the vegetation cover, a lower difference indicating unhealthy or sparse vegetation. On the other hand, the radiometric spectrum of soil usually does not show this clear difference between the above mentioned spectral bands and, therefore, NDVI makes difficult to distinguish between vegetation and bare soil [6]. To overcome this drawback, indices such as Soil Adjusted Vegetation Index-SAVI were created, in order to highlight the vegetation response and reduce that of the soil [7]. The NDVI is calculated using the expression proposed by Rouse [6] [8]:        Where ρ= reflectance in the corresponding band. 2.6 Calculation of cadastral statistics  The system calculates automatically the basic statistical parameters: mean, maximum, minimum and typical deviation of NDVI values for each plot and each image received. The results are stored in a geodatabase postgis for exploitation by a web viewer.   2.7 Interoperability   Interoperability can be defined in different ways. For example, the Institute of Electrical and Electronics Engineers [9] defines interoperability as the ability of two or more systems to exchange information and to use the information exchanged. The system makes use of OGC redNIRredNIRNDVIρρρρ+−=  4th Workshop on Agri-food Research - WiA.15. Cartagena, Murcia, Spain. 11-12 May 2015 203 | 269  standard web services: SOS (Sensor Observation Service) is used to store and make available the in-situ data observations (Fig. 2). 3. Results and discussion The NDVI are calculated for all available dates and plot level, for this initial study - 30.04.2013 to 08.09.2014- are available 26 images without clouds Table 2, (Fig. 3) from which one can follow the evolution the state of vegetation over one million registered plots. With the processing system is activated, the database of statistics on all parcels are updated every 16 days (Fig. 4).   4. Conclusions The automatic processing of biophysical indicators Landsat 8 is a very interesting tool for monitoring crops irrigated allowing us monitoring the state of vegetation at plot level for the Region of Murcia.   5. Acknowledgments This work has been done through the project “Telerieg – Remote sensing use for irrigation practice recommendation and monitoring in the SUDOE space” financed by Programme (SUDOE), which supports regional development through European Regional Development Fund (ERDF) co-financing of transnational projects. 6. References [1] INSPIRE 2007. Directive 2007/2/EC, Official Journal of the European Union, ISSN 1725-2555, L 108, Volume 50, 25 April 2007 (http://www.ec-gis.org/inspire/) [2] FGDC. 2015. Geospatial Metadata Standards. Accessed:  7 April. 2015. http://www.fgdc.gov/ [3] Chuvieco E. 2002. Teledetección ambiental :La observación de la Tierra desde el espacio. Ariel, 586p. ISBN-8434480476. Barcelona. 2002 [4] USGS. 2015. Accessed:  7 April. 2015. http://landsat.usgs.gov/landsat8.php [5] 52north. 2015. Accessed:  7 April. 2015  [6] Rouse J.W., Haas R.H., Schell J.A., Deering D.W., Harlan J.C. 1974. Monitoring the vernal advancement of retro-radation of natural vegetation. In: NASA/GSFC, Type III, Final Report, Greenbelt, MD, 371. [7] Karnieli A., Agam N., Pinker R.T., Anderson M.C., Imhoff M.L., Gutman G.G., Panov N., Goldberg A. 2010. Use of NDVI and land surface temperature for assessing vegetation health: merits and limitations. In: Journal of Climate. 23:618-633. [8] Sobrino J.A., Raissouni N., 2000. Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. In: International Journal of Remote Sensing. 21(2). http://hdl.handle.net/10201/42748  [9] IEEE, 1990. Institute of Electrical and Electronics Engineers. IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. New York, NY: 1990.    4th Workshop on Agri-food Research - WiA.15. Cartagena, Murcia, Spain. 11-12 May 2015 204 | 269  Tables and figures  Table 1. Spectral Band Landsat 8 (OLI/TIRS) Spectral B. Wavelength Resolution B1 - Aero. 0.433 - 0.453 µm 30 m B2 - Blue 0.450 - 0.515 µm 30 m B3 - Green 0.525 - 0.600 µm 30 m B4 - Red 0.630 - 0.680 µm 30 m B5 - NIR 0.845 - 0.885 µm 30 m B6 - IR 1.560 - 1.660 µm 30 m B7 - IR 2.100 - 2.300 µm 30 m B 8 - Pan 0.500 - 0.680 µm 15 m B 9 - Cirrus 1.360 - 1.390 µm 30 m B10  -  LI 10.30 - 11.30 µm 100 m B11  -  LI  11.50 - 12.50 µm 100 m Table 2. Product overview.   Sensor Indicators   Dates Products  NOAA 6 808 4852 MODIS 3 46 138 Landsat 5 3 5 15 Landsat 8 8 64 517   Figure 1. Automatic processing system Figure 2. Interoperability model   Figure 3. Web data browser.    Figure 4. Evolution of NDVI mean +-sd for cadastral parcel- 30019A02400111.    

Calculation of biophysical indicators through the automatic processing of satellite images

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Calculation of biophysical indicators through the automatic processing of satellite images

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