ES2303476A1 - PROCEDURE FOR AUTOMATIC OBTAINING OF AGRONOMIC AND ENVIRONMENTAL INDICATORS OF PLANTATIONS OF TREES THROUGH TELEDETECTION. - Google Patents

Abstract

Procedure for automatically obtaining agronomic and environmental indicators of tree plantations through remote sensing. This procedure allows the quantitative characterization of tree plantations based on remote sensing with high spatial resolution and on the processing of the corresponding images, providing information on each tree and on the plantation as a whole. Thus, it provides individualized information on the coordinates - geographic center of gravity, area and potential productivity, among others - of each tree; and it also characterizes tree plantations as a whole, calculating, among other parameters, the total number of trees, their area and global potential productivity; and coverage indicators of other land uses that are defined, such as vegetation covers and bare soil. Therefore it has application in Agriculture and Environment.

Description

Procedure for automatic obtaining of agronomic and environmental indicators of tree plantations by remote sensing.

Technical sector

First sector: AGRICULTURE and ENVIRONMENT. Second sector AGRICULTURAL TECHNICAL ASSISTANCE COMPANIES OR ENVIRONMENT, or PUBLIC AGRO-ENVIRONMENTAL AUDITS (PUBLIC ADMINISTRATIONS) OR PRIVATE. The second sector is refers to the monitoring of agricultural producers who use precision agriculture technologies in order to reach economic and environmental benefits of the same, such as the reduced application of fertilizers, phytosanitary and / or drip irrigation dose, making such applications not of extensive and uniform shape across the entire plot surface agricultural, but adapted to the needs of each tree, whose characterization and geographical mapping, object of this patent, is carry out previously.

State of the art Remote sensing, basics

Remote sensing is a technology that consists in capturing information about the objects or accidents that occur in Earth's surface or in the atmosphere without coming into contact Physical with them. It includes the measurement and registration of energy electromagnetic reflected or emitted by them, and entails the interpretation and relationship of this information with nature and properties of these. The capture of the reflected energy is taken to out using remote sensors installed on platforms aerospace (satellites and airplanes) that record energy reflected corresponding to various spectrum frequencies Electromagnetic, ranging from low radio waves frequency passing through the visible spectrum (blue, green and red) to x-rays, gamma and even cosmic. Each body or land cover presents a peculiar way of reflecting or emitting energy known as a signature or spectral signature (Chuvieco, 2002). In the last decades the technologies on which the  Remote sensing and its applications have been greatly developed. Today remote sensing is a very important tool in very various areas of science such as meteorology, oceanography, climatology, military sciences, sciences land, and civil protection, among others.

Applications of remote sensing to agriculture

In remote sensing it is essential to know the spectral behavior or signature of each of the different surfaces or land uses at different wavelengths. The energy reflected by the vegetation and bare soil in the red and infrared wavelengths varies very considerably (Cloutis et al ., 1996). Dense and healthy cultures are characterized by a high absorption of red energy / radiation and a high reflectance of infrared radiation. It is often convenient to combine these measures (and others in other bands) in a single index that highlights the sensitivity to variations in the crop. Such combinations are known as vegetation indices. There are a large number of them, as many as mathematical operations are deemed appropriate to define. Its advantages are: 1) increase the relative differences between the digital values that characterize each land use, 2) reduce the number of data obtained to a single characteristic value, 3) obtain dimensionless values that allow its spatial and temporal comparison and, 4 ) sometimes eliminate unwanted effects of lighting, orography, etc. (Jackson and Huete, 1991). One of the best known is the NDVI (" Normalised Difference Vegetation Index "). A high photosynthetic activity, that is to say a healthy and vigorous vegetation, implies a high value of NDVI due to a high reflectivity in the near infrared band and a high absorption of energy in the red band. Therefore, NVDI, calculated with measurements on land (Kanemasu 1990), satellite images (Anderson et al ., 1993) or photographs areas (Denison et al ., 1996) has a high correlation with the final crop production.

The work on land use classification by means of satellite images of medium / low spatial resolution or aerial photographs using vegetation indexes can be considered as classics in remote sensing and have been carried out in very diverse areas: coastal, natural parks, masses forestry, agricultural areas, among many others. Work has also been carried out to systematically detect anomalies in the development of irrigated crops in Aragon (López-Lozano and Casterad, 2003), and monitor the growth of crops with biophysical data such as plant height, leaf area (LAI) and biomass (Calera et al ., 2001; 2002), or to estimate the long-term effect of changes in land use on crop evapotranspiration using Landsat 5 TM and Landsat 7 ETM + images of 1982 to 2000 (Lanjeri et al ., 2001; 2002) in the area of Castilla-La Mancha. There are also very significant advances in remote sensing of weeds in crops with multispectral airborne sensors (Goel et al ., 2002; Schmidt & Skidmore, 2003; Koger et al . 2004; Smith & Blackshaw, 2003; Girma et al . 2005 ; Felton et al . (2002), Radhakrishnan et al . (2002) and Thorp & Tian (2004) and a methodology has been developed to map late weeds infestations in crops using remote spatial high resolution images (López-Granados et al . 2006; Peña-Barragán et al ., 2007) In order to carry out this work it is necessary that there are differences in the spectral signatures between the crop and the weed species at certain moments of the phenological cycle (Everitt et al . 1994 ; Everitt & Deloach 1990; Lass & Callihan 1997; Peña-Barragán et al . 2006).

There are several works whose objective is to characterize large areas of vegetation / forests through remote images of low spatial resolution, 30 to 100 meters of pixel, or even higher (Kokaly et al . 2003; Schmidt and Skidmore. 2003). Peña-Barragán et al . (2005) has developed a methodology to characterize the vegetation cover in olive groves through aerial photographs of low spatial resolution.

However, there are no known works that characterize tree plantations with high images spatial resolution for application in agriculture of precision.

Remote image management software

ENVI® : Today software programs (" software ") are available commercially for the processing and interpretation of images, among others ILWIS®, ERDAS® and ENVI®. In particular, the ENVI software (" the Environment for Visualizing Images ", ENVI®) is a powerful remote image processing system widely used in many different countries of the world and in many different scientific disciplines. It allows a very diverse handling of the data matrices captured by the remote sensors and their visualization in a coherent and compressive way. ENVI has been developed and is registered by Research Systems International (RSI) Global Services ( http://www.rsinc.com/ ).

The supporting data matrices of each image are made up of rows and columns of spatial units or pixels. The pixel dimension matches the area of its spatial resolution. For each spectral band, each pixel is defined by a digital value. Among the advantages of ENVI are the following: a) it combines through interactive functions the data files of the electromagnetic spectrum bands captured by the sensor / s. In each file, the data of each band is archived independently and they are accessed individually or simultaneously through functions. If multiple files are opened, data from different types of bands can be processed and processed as if they belonged to the same group or image; b) sorts the data of each band in 8- or 24-bit windows; c) develops various windows or screens (interface, " display ") known by the name of Image, Zoom, and Scroll, being able to adjust the size of each of them. The ENVI user has many possibilities for interactive ENVI analysis, displaying each of these windows; d) allows various forms of image overlap in different windows for spatial and spectral comparative study, which is especially useful in multiband and multispectral images; e) provides various interactive tools to visualize and analyze vectors and attributes GIS (Geographic Information Systems), among others the increase in the range of the data matrix (" contrast stretching ") and the two-dimensional scatter plots (" two- dimensional scatter plots "); f) provides an extensive list of functions / algorithms for image processing easily and immediately,
such as transformations, filters, classifications, registration and geometric corrections, and spectral analysis.

IDL : ENVI is written in IDL ( Interactive Data Language, IDL® ), a powerful and systematized computer programming language that allows an integrated imaging process. The flexibility of ENVI is largely due to the versatility of IDL. For the operation of ENVI, the installation of IDL is therefore required, either in a basic version (" runtime version of IDL ") or in a full version (" full version of IDL ") that allows to include the functions / command / functions of the Username. ENVI users can use all ENVI functions, but not write their routines or commands (" custom routines "). The ENVI and IDL manuals contain extensive information about them (" Using IDL and the IDL Reference Guide and IDL Help ").

`` Clustering Assessment IDL.IAS.1 '' ( hereinafter CLUAS® subprogram ): The CLUAS® subprogram (García-Torres et al . 2006) has been registered in the Intellectual Property Registry (Registry Number 200699900440900). It consists of the grouping and integration of digital values of adjacent pixels according to a range of digital values (DV) and defined spatial dimensions. It proceeds as follows: a) pixels with digital values are selected within a certain range; outside of that range the VD makes them equal to 0; b) the size of the groupings is selected; a new grouping begins above a maximum number of columns and rows; and c) the VD of the pixels that occupy adjacent positions are grouped and integrated below.

The definition of the groupings is therefore flexible and is established according to the established digital value range / s and according to the group size. A range of digital values is defined, VD_ {max} and VD_ {min}, for example between 50 and 88, and digital values outside that range do not consider them (makes them equal to 0). On the other hand, it defines the maximum dimensions of each grouping, maximum number of columns (C_ {max}) and rows (F_ {max}), so that the resulting groupings will contain a number of pixels smaller than M x N pixels. The CLUAS® subprogram thus integrates only the digital values of the selected contiguous pixels, this is with DV not equal to 0 and grouped without exceeding the aforementioned spatial limits. It operates systematically by first processing the rows, from row 1 to row n, integrating the values of the adjacent pixels in the pixel located on the right (whose number value on the right is higher).
Then, similarly, it processes or integrates the adjacent pixels by columns (from column 1 to column m).

Facts that justify this patent

1) The quantitative characterization of tree plantations is traditionally carried out " in situ ", through site visits, and visually, rudely, even in technologically advanced countries. Currently, the determination of the morphological and productive characteristics of the different areas of the same plantation and even more of each tree of the same directly in the field (" in situ ") is practically unfeasible from a technical and economic point of view.

2) Agronomic management of plantations of trees are still carried out extensively and uniformly, even in technologically highly developed countries: in tree plots agricultural operations application of fertilizers, phytosanitary and irrigation doses are performed evenly, regardless of the very frequent differences between areas and / or trees of the same plot.

3) Remote sensing techniques are very suitable for plot characterization, for the following reasons: a) the sensor used (satellite or aerial photography) records what is in the field (objectivity), b) the procedure for analyzing the Image obtained is fast once the method has been tuned, c) allow to work sequentially, d) avoid field sampling; and e) make it possible to plan the taking of images in a timely manner and to delay their analysis for the necessary time, if necessary, without losing
information.

The process object of the present invention  implements, in one of its stages, the CLUAS® subprogram in the process of remote images of tree plantations automatically providing valuable information individualized for each tree, for certain areas and for the Plantation set.

The present invention allows laying a foundation solid for the development of precision agriculture in any tree plantation, such as cork oaks, almond trees, holm oaks, citrus fruits, apple trees, olive trees, vineyards, etc. Your goal is therefore highlight and safeguard the rights of generation of agronomic and environmental information on each tree and on the set of tree planting by image processing  remote using the CLUAS® subprogram.

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Description of the invention brief description

An object of the present invention is a procedure for the quantitative and automatic obtaining of agronomic and environmental indicators of tree plantations by remote sensing, which includes the following stages (see Figure 1):

a) Taking remote satellite images or hyperspectral, multispectral or panchromatic aerial photography, with  a spatial resolution close to 1 meter or less, preferably in late spring or summer, and also in others times of the year in which the trees differ from remaining land uses such as dried vegetation and / or bare ground,

b) Digitization and georeferencing, through Differential GPS to assign geographic coordinates, in the case of non-digitized or geo-referenced aerial photographs, respectively,

c) Primary analysis of the image comprising in turn the following stages:

c.1.)

Transformation / obtaining simple images composed of a single band or index, of the visible spectrum (blue: B, green: G, red: R; and near infrared NIR), panchromatic, or any other band in the case of hyperspectral images, or of any vegetation index that is defined by an algorithm between any of the aforementioned bands,

c.2)

Definition of representative regions (`` regions of interest) of the main uses in the simple image or images simple selected,

c.3)

Definition of digital frontier values (VDF) of each land use and classification / separation thereof in the selected simple image, through an iterative process of VDF selection statistically contrasted,

c.4)

Definition of land use grouping a characterize, with the dimension parameters (Max Columns and Max Rows) and neighborhood (proximity of grouping), according to spatial resolution characteristics of the image in process and the objective of the ongoing study,

d) Activation of the computer subprogram Clustering Assessment IDL.IAS.1 (CLUAS®) in the software  ENVI and implementation of the selected image in CLUAS®, which It also includes the following stages:

d.1)

Introduction to CLUAS® of the parameters of the groupings selected in the previous points c.3) and c.4): VDF, dimensions and neighborhood,

d.2)

Processed by CLUAS® of agronomic indicators and environmental plantation,

d.3)

Study of automatically generated information by CLUAS®

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Another object of the present invention is the use of the procedure to determine in any tree planting the following indicators (relative to trees, vegetation cover and bare soil):

a) geographical coordinates / barycenter, area, and overall potential productivity and per unit area of each tree each tree of the plantation;

b) the total number of trees, area global, and potential global and unitary productivity of the tree plantations as a whole; Y

c) the surface of other land uses that are define, such as green roofs and bare soil; operations which automatically performs the CLUAS® subprogram.

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Detailed description

The present invention of the invention is based on the fact that the inventors have found that it is possible to optimally and quantitatively characterize agronomic and environmental indicators of tree plantations based on remote sensing of high spatial resolution and the processing of the corresponding images by means of the computer program " Clustering Assessment IDL.IAS.1® " (hereinafter CLUAS®), developed by the inventors and consisting of the following:

a) Taking remote images with a resolution space near 1 meter or less, preferably in spring late or summer, and also at other times of the year when differentiate trees from other land uses such as dried vegetation and / or bare soil,

b) Transformation of the original images through some vegetation index and classification of the uses of soil of interest; Y

c) Processing of the selected images through the CLUAS® software.

In this sense, the procedure object of this  invention has been applied in remote images of plantations of trees / plots of olive groves, citrus / citrus and forest Mediterranean (temperate climate of Mediterranean environment), where it has it was possible to differentiate spectrum-radiometrically land uses such as trees, vegetation cover and soil bare that characterize any tree plantation, and with satisfactory and reproducible results (Example 1 and 2).

The process of the invention provides a  information about each tree and the whole plantation. Thus, it provides individualized information of the coordinates / geographic barycenter, surface and productivity potential, among others, of each tree; and also characterizes tree plantations as a whole, calculating among others parameters the total number of trees, their surface and potential global productivity; and other coverage indicators land uses that are defined, such as plant coverings and soil naked. CLUAS can be used to contribute to agriculture precision, tree by tree, of any tree plantation, such as cork oaks, almond trees, holm oaks, citrus fruits, apple trees, olive trees, vineyards, etc., and likewise, to determine for purposes comparative potential productivity of certain areas of a plot or between plots of any tree plantation.

It has application in Agriculture and Environment, and more specifically in Agricultural Technical Assistance Companies or Environmental, or in Public Agro-Environmental Audits or Private. The procedure covered by this patent will allow certain companies, such as technical assistance agrarian or environmental, or audit services Agri-environmental of Public Administrations or entities private, plan the implementation strategies of fertilizers, phytosanitary and irrigation with precision, adapted these operations to the characteristics of each tree, estimate of comparative form the potential productivity and indicators agro-environmental such as the percentage of plant cover and / or bare soil of certain areas of a plot and plots different. The latter may become a necessary requirement. to obtain the right to receive certain agro-environmental grants / subsidies.

Thus, the object of the present invention is a procedure for the quantitative and automatic obtaining of agronomic and environmental indicators of tree plantations by remote sensing, which includes the following stages (see Figure 1):

a) Taking remote satellite images or hyperspectral, multispectral or panchromatic aerial photography, with  a spatial resolution close to 1 meter or less, preferably in late spring or summer, and also in others times of the year in which the trees differ from remaining land uses such as dried vegetation and / or bare ground,

b) Digitization and georeferencing, through Differential GPS to assign geographic coordinates, in the case of non-digitized or geo-referenced aerial photographs, respectively,

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c) Primary analysis of the image comprising in turn the following stages:

c.1.)

Transformation / obtaining simple images composed of a single band or index, of the visible spectrum (blue: B, green: G, red: R; and near infrared NIR), panchromatic, or any other band in the case of hyperspectral images, or of any vegetation index that is defined by an algorithm between any of the aforementioned bands,

c.2)

Definition of representative regions (`` regions of interest) of the main uses in the simple image or images simple selected,

c.3)

Definition of digital frontier values (VDF) of each land use and classification / separation thereof in the selected simple image, through an iterative process of VDF selection statistically contrasted,

c.4)

Definition of land use grouping a characterize, with the dimension parameters (Max Columns and Max Rows) and neighborhood (proximity of grouping), according to spatial resolution characteristics of the image in process and the objective of the ongoing study,

d) Activation of the computer subprogram Clustering Assessment IDL.IAS.1 (CLUAS®) in the software  SEND and implementation of the selected image in CLUAS, which It also includes the following stages:

d.1)

Introduction to CLUAS® of the parameters of the groupings selected in the previous points c.3) and c.4): VDF, dimensions and neighborhood,

d.2)

Processed by CLUAS® of agronomic indicators and environmental plantation,

d.3)

Study of automatically generated information by CLUAS®.

The data / report generated by CLUAS® provides  individualized coordinate information / barycenter geographical, surface and potential productivity, among others, of each tree; it also characterizes tree plantations in its set, calculating among other parameters the total number of trees, and indicators of their overture and potential productivity global, and the surface of other land uses that are defined, such as green roofs and bare soil.

The objective of this invention is to generate agronomic and environmental information such as the one referred to in tree plantations such as olive, citrus, almond trees, apple trees, cork oaks, holm oaks, etc., etc. Your goal is therefore highlight and safeguard the rights of generation of information about each tree and the whole plantation of trees by processing remote images with the CLUAS® subprogram.

Remote images are taken at the moment that it is possible to differentiate spectroradiometrically the uses of soil trees, vegetation cover and bare soil that characterize Any tree plantation. In temperate ambient climates Mediterranean images are preferably taken at the end of the Spring or during the summer.

Another object of the present invention is the use of the procedure to determine in any tree planting the following indicators (relative to trees, vegetation cover and bare soil):

a) geographical coordinates / barycenter, area, and overall potential productivity and per unit area of each tree each tree of the plantation;

b) the total number of trees, area global, and potential global and unitary productivity of the tree plantations as a whole; Y

c) the surface of other land uses that are define, such as green roofs and bare soil; operations which automatically performs the CLUAS® subprogram.

Also, the procedure can be used to discriminate and quantify by means of remote sensing the uses of ground that are defined in simple single-band or index images vegetative, based on the pixel grouping method of each land use and estimate of its geographic center, number of integrated pixels (NP) or surface, integrated digital values in each grouping (VDAG) or global productivity, and VDGA / NP or global unit productivity, operations performed automatically the CLUAS® subprogram.

The use of this procedure to design and implement a precision agriculture program concerning the application of fertilizers, phytosanitary products or doses of irrigation water in any tree plantation, constitutes also another object of the present invention. This procedure It can also be used to estimate agri-environmental indicators according to relative surfaces of the land uses trees, cover Vegetable and bare soil.

Description of the figures

Figure 1.- Diagram of the process of the invention .

Figure 2.- View of the land uses of a citrus plantation: orange trees (black), vegetation cover (gray) and bare soil (white) . Panchromatic image of the Quick Bird satellite, taken on May 10, 2005, pixel size 0.7 m, a) Plot of 0.07 ha; b) Enlargement of the previous one, zoom x 8.

Figure 3.- View of the uses of the Mediterranean forest soil: holm oaks / cork oaks / Quercus spp ., (Black), vegetation cover (gray) and bare soil (white) . Panchromatic image of the Quick Bird satellite, taken on May 10, 2005, pixel size 0.7 m, a) Plot of 0.15 ha; b) Enlarged part of the previous one, zoom x 7.

Figure 4.- Panchromatic image of olive plantations of the Quick Bird satellite of 18.2 ha (x = 351037; y = 4156992) . In this image five plots have been delimited for the processing of their agri-environmental characteristics through the CLUAS subprogram.

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Example of the embodiment of the invention

Examples of the embodiment of the patent in olive grove, citrus and Mediterranean forest.

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Example 1 Processing of individual plots of plantations trees of various species

The images have been processed using CLUAS® corresponding to an olive grove (Figure 1), of citrus (Figure 2) and Mediterranean forest (Figure 3). The results obtained in these processes are shown in Tables 1, 2 and 3, respectively. Table 1 shows the information obtained by CLUAS® of the image shown attached to said Table. CLUAS® provides individualized information on each olive tree, such as its geographical coordinate, surface (NP, number of pixels / m2), potential production (digital values integrated (VDAG) and productivity index (VDGA / NP).

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TABLE 1 Individualized information for each olive tree corresponding to the image of 11 olive trees, which is generated through its processing by the CLUAS® subprogram. The picture corresponds to the green band, from 520 to 600 nm with a size of 25 cm pixel which generates its processing. Its characteristics of Processing were as follows: digital border values of 40 to 99, neighborhood 8, and maximum grouping of 28 rows and 28 columns

one

For example, the fourth grouping or olive tree (AG4) is the smallest size (136 pixels // 8.5 m2) with a potential production of 10593; and the ninth olive cluster (AG9) is the largest size (462 pixels / 28.8 m 2), with a potential production of 33870. In addition CLUAS® obtains / provides information on indicators from the set of olive trees in the image, for example the total number of trees (11), total area of trees (3475 pixels / 217.1 m 2), the percentage of the olive grove area over the total of the plot area (NTAG / NTP, 0.40 / 40%), and the Global potential productivity (IVDA, 26418), among others.

Table 2 shows the information obtained  through CLUAS® of the citrus / citrus plantation image indicated in Figure 2. CLUAS® provides information individualized of each citrus and the whole of the plantation. Thus, grouping or tree 25 (AG25) is the smallest size (4 pixels / 2.0 m2) with a potential production of 2049; and the 4th tree / cluster (AG4) is the largest size (56 pixels / 27.7 m2) with a potential production of 28144. In addition CLUAS® obtains / provides information on set indicators of trees in the image, for example of the total number (30), total area of trees (1479 pixels), the percentage of the surface of trees over the total area of the plot (NTAG / NTP, 0.59 / 59%), and global potential productivity (IVDA, 427784), among others.

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TABLE 2 Information on a citrus plantation (orange trees) [CO2] generated by the CLUAS® subprogram, taken in the image Quick Bird satellite panchromatic, 0.7 m resolution Space, taken on May 10, 2005 (Figure 2). Their Processing characteristics were as follows: values digital border from 368 to 559, neighborhood 8, and grouping maximum of 7 rows and 10 columns

2

3

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Table 3 shows the information obtained  through CLUAS® of the Mediterranean forest image (holm oaks / cork oaks) indicated in Figure 3. CLUAS® provides individualized quantitative information of each tree and of the whole plantation. Thus, the grouping or 10th tree (AG10) is the smallest size (8 pixels / 3.9 m2) with a potential production of 4140; and the 17th tree / cluster (AG17) is the largest size (138 pixels / 67.6 m2) with a potential production of 67793. In addition CLUAS® obtains / provides Indicator information for the image tree set, for example the total number of trees (22), their total area (3024 pixels), the percentage of the area of trees on the total plot area (NTAG / NTP, 0.3 / 30%), and the Global potential productivity (IVDA, 428853), among others.

TABLE 3 Information from a Mediterranean forest ( Quercus spp) generated by processing the CLUAS® subprogram, taken in the Panchromatic Quick Bird satellite image, with a spatial resolution of 0.7 m, taken on May 10, 2005 (Figure 3). Its processing characteristics were the following: digital border values from 319 to 515, neighborhood 8, and maximum grouping of 14 rows and 14 columns

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CLUAS® provides individualized information of each tree, such as its geographic coordinate, surface (NP, number of pixels / m2), potential production (digital values integrated (VDAG) and productivity index (VDGA / NP). further CLUAS® obtains / provides information on set indicators of trees in the image, for example the total number of trees, total area of trees, the percentage of the area of olive grove on the total area of the plot and the potential global productivity, among others.

Obtaining quantitative data through CLUAS® outlined may be useful for the implementation of precision farming techniques in various operations agricultural such as fertilizer application, phytosanitary and irrigation water at a variable dose, this is adapted to the productive needs / requirements of each tree. Sayings requirements will be proportional to indexes estimated by CLUAS®, such as the surface of each tree or its productivity potential.

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Example 2 Comparative processing of various adjacent plots of a certain species of trees

The images have been processed using CLUAS® corresponding to several adjacent olive groves (Figure 4). The results obtained in these processes are shown extensively in Table 4. CLUAS® provides information individualized quantitative of each plot. Thus, plot D is the one of smaller extension (0.209 ha), with a total of 97 olive trees, with an average area and productive capacity per olive tree of 22.2 m 2 and 313, respectively; while the smaller plot A extension (12.5 ha), with a total of 948 olive trees, with an area and average productive capacity per olive tree of 12.5 m 2 and 2059, respectively.

Through CLUAS®, various estimates are estimated parameters of each plot such as surface, production potential and average productivity index of each tree and the set of trees, and also the relationship between the set of trees and the total area of the plot or other land uses. These parameters are useful for characterization agro-environmental of each plot, and can be used by the farmer for operations planning specific agricultural for each plot, such as the application of fertilizers, fertilizers and irrigation water, proportional to Estimated parameters in each of them, as for monitoring administrative of certain agri-environmental measures, such as percentage of bare soil.

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TABLE 4 Agronomic and environmental indicators of several olive groves of the municipality of Montilla 1 (Córdoba x = 351011, y = 4156896). The quantification of the olive grove has led to out using a panchromatic image taken by the Quick satellite Bird, with a spatial resolution of 0.7 m with a range of values Digital frontier (VDF) from 50 to 89, 90 to 125 and 126 to 200 for olive grove, vegetation cover and bare soil, respectively, and a maximum olive size of 14x 14 pixels

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Claims (7)

1. Procedure for automatic obtaining of  agronomic and environmental indicators of tree plantations by remote sensing, which includes the following stages: a) Satellite image or aerial photography  hyperspectral, multispectral or panchromatic, with a resolution space of approximately 1 meter or less, preferably in late spring or summer, and also at other times of the year in the that trees differ from other land uses such as dried vegetation and / or bare soil, b) Digitization and georeferencing, through Differential GPS to assign geographic coordinates, in the case of non-digitized or geo-referenced aerial photographs, respectively, c) Primary analysis of the image comprising in turn the following stages:

c.1.)

Obtaining simple images composed of a single band or index, of the visible spectrum (blue: B, green: G, red: R; and near infrared NIR), panchromatic, or any other band in the case of hyperspectral images, or any index of vegetation that is defined by an algorithm between any of the bands mentioned above,

c.2)

Definition of representative regions (`` regions of interest) of the main uses in the simple image or images simple selected,

c.3)

Definition of digital frontier values (VDF) of each land use and classification / separation thereof in the selected simple image, through an iterative process of VDF selection statistically contrasted,

c.4)

Definition of the grouping of land use to be characterized , with the parameters dimension (Max Columns and Max Rows) and neighborhood (proximity of grouping), according to the spatial resolution characteristics of the image in process and the objective of the current study,

d) Activation of the computer subprogram Clustering Assessment IDL.IAS.1 (CLUAS®) in the software  SEND and implementation of the selected image in CLUAS, which It also includes the following stages:

d.1)

Introduction to CLUAS® of the parameters of the groupings selected in the previous points c.3) and c.4): VDF, dimensions and neighborhood,

d.2)

Processed by CLUAS® of agronomic indicators and environmental plantation,

d.3)

Study of automatically generated information by CLUAS®

2. Method according to claim 1, characterized in that the tree plantations to be processed by CLUAS® can be any tree plant species, such as olive, almond, citrus / citrus, cork oaks, holm oaks, among others. 3. Method according to claim 1, characterized in that the remote images are preferably taken at the end of spring or during the summer in temperate climates of a Mediterranean environment, and / or when it is possible to differentiate spectrum-radiometrically the uses of trees, cover, soil bare plant and soil that characterize any tree plantation. 4. Use of a procedure according to claims 1, 2 and 3 to determine in any plantation of tree the following indicators (relative to trees, cover bare soil and vegetable): a) geographical coordinates / barycenter, area, and overall potential productivity and per unit area of each tree each tree of the plantation; b) the total number of trees, area global, and potential global and unitary productivity of the tree plantations as a whole; Y c) the surface of other land uses that are define, such as green roofs and bare soil; operations which automatically performs the CLUAS® subprogram. 5. Use of a procedure according to claims 1 to 3, to discriminate and quantify by remote sensing the land uses defined in simple images single-band or vegetative index, based on the method of pixel grouping of each land use and estimate of its geographic center, number of integrated pixels (NP) or surface, integrated digital values in each grouping (VDAG) or global productivity, and VDGA / NP or global unit productivity, operations performed automatically by the CLUAS® subprogram. 6. Use of a procedure according to claims 1 to 3 to design and implement a program of precision agriculture related to fertilizer application, phytosanitary or irrigation water dose in any plantation of trees. 7. Use of a procedure according to claims 1 to 3 for estimating agri-environmental indicators according to relative surfaces of the land uses trees, cover Vegetable and bare soil.