WO2008092983A1 - Method for automatic obtaining of agronomic and environmental indicators from tree plantations by remote detection - Google Patents

Abstract

This method quantitatively characterises tree plantations using high resolution spatial remote detection and processing of the related images, wherein information is provided on each tree and on the plantation as a whole. Thus, the method provides individualised information including the geographical baricentric coordinates, surface and potential productivity on each tree, also characterises tree plantations as a whole, calculating among other parameters the total number of trees, their surface and overall potential productivity, and indicators of range of other land uses that may be defined, such as plant cover and bare land. Therefore the method is useful in the agricultural and environmental sectors.

Description

 TITLE

PROCEDURE FOR AUTOMATIC OBTAINING OF AGRONOMIC AND ENVIRONMENTAL INDICATORS OF TREE PLANTATIONS THROUGH REMOTE CONTROL

SECTOR OF THE TECHNIQUE

First sector: AGRICULTURE and ENVIRONMENT. Second sector AGRICULTURAL OR ENVIRONMENTAL TECHNICAL ASSISTANCE COMPANIES, or PUBLIC AGRO-ENVIRONMENTAL AUDITS (PUBLIC ADMINISTRATIONS) OR PRIVATE. The second sector refers to the monitoring of agricultural producers who use precision agriculture technologies in order to achieve their own economic and environmental benefits, such as the reduced application of fertilizers, phytosanitary products and / or drip irrigation doses, making said applications not extensively and uniformly over the entire agricultural plot surface, but adapted to the needs of each tree, whose geographic characterization and mapping, object of this patent, is carried out previously.

STATE OF THE TECHNIQUE Remote sensing, basic concepts

Remote sensing is a technology that consists of capturing information about objects or accidents that occur on the earth's surface or in the atmosphere without coming into physical contact with them. It includes the measurement and recording of the electromagnetic energy reflected or emitted by them, and entails the interpretation and relationship of this information with their nature and properties. The capture of the reflected energy is carried out by remote sensors installed on aerospace platforms (satellites and airplanes) that record the reflected energy corresponding to various spectrum frequencies electromagnetic, ranging from low frequency radio waves through the visible spectrum (blue, green and red bands) to X-rays, gamma and even cosmic. Each body or earth cover has 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 remote sensing is based and its applications have developed enormously. Today, remote sensing is a very important tool in many different areas of science such as meteorology, oceanography, climatology, military sciences, earth sciences, 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 various surfaces or land uses at different wavelengths. The energy reflected by the vegetation and the 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) into 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 lighting effects, 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 indices can be considered as classic in remote sensing and have been carried out in very diverse areas: coastal, natural parks, forest masses, 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 for mapping late weed infestations in crops has even been developed by remote images of high spatial resolution (López-Granados et al. 2006; Peña-Barragán et al., 2007). 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 spatial resolution images for application in precision agriculture. 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 around 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), including increasing the range of the data matrix {"contrast stretching") and 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 own 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 subprogram CLUAS®): The subprogram CLUAS® (. Garcia-Torres et al 2006) has been registered in the Registry of Intellectual Property (N ⁰ Register 200699900440900) is the grouping and integration. digital values of contiguous pixels according to a range of digital values (DV) and defined spatial dimensions Proceed as follows: a) pixels with digital values within a certain range are selected; outside of that range the DV 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 VDs of the pixels occupying adjacent positions are grouped and integrated.

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, VD _max and VD _min , is defined, 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 define the dimensions It will be equal to 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 greater). 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) The agronomic management of tree plantations is still carried out extensively and uniformly, even in technologically highly developed countries: in the tree plots the agricultural operations of fertilizer application, phytosanitary and irrigation doses are carried out uniformly, without taking into account the very frequent differences between zones 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 photo aerial) records what is in the field (objectivity), b) the procedure of analysis of the image obtained is fast once the method has been tuned, c) allows to work sequentially, d) prevents field sampling; and e) make it possible to plan the taking of images in a timely manner and delay their analysis for the necessary time, if necessary, without losing information.

The procedure 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 individualized information for each tree, for certain areas and for the whole of the plantation. The present invention allows to lay solid foundations for the development of precision agriculture in any tree plantation, such as cork oaks, almond trees, holm oaks, citrus, apple trees, olive trees, vineyards, etc. Its objective is therefore to highlight and safeguard the rights to generate agronomic and environmental information on each tree and on the whole tree plantation through remote image processing through the CLUAS® subprogram.

DESCRIPTION OF THE INVENTION Brief Description

An object of the present invention is a process for the quantitative and automatic obtaining of agronomic and environmental indicators of tree plantations by remote sensing, which comprises the following steps

(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 at other times of the year in which trees are differentiated from other land uses such as dried vegetation and / or bare soil, b) Digitization and georeferencing, by differential GPS to assign the geographical coordinates, in the case of non-digitized or geo-referenced aerial photographs, respectively, c) Primary analysis of the image that includes the following stages: el.) Transformation / obtaining of 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 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 simple images selected, c.3) Definition of digital border values (VDF) of each land use and classification / separation of the same in the selected single image, by means of an iterative process of VDF selection statistically contrasted, c.4) Definition of grouping of the 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 subprogram Clustering Assessment IDL software. IAS.1 (CLUAS®) in the ENVI software and implementation of the selected image in CLUAS®, which in turn includes the following stages: dl) 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 the agronomic and environmental indicators of the plantation , d.3) Study of the information generated automatically by CLUAS®

Another object of the present invention is the use of the method to determine in any tree plantation the following indicators (relating to trees, vegetation cover and bare soil): a) coordinates / geographic barycenter, surface area, and overall potential productivity and per unit of area of each tree each tree of the plantation; b) the total number of trees, global area, and overall and unit potential productivity of the tree plantations as a whole; and c) the surface of other land uses that are defined, such as green roofs and bare soil; operations performed automatically by the CLUAS® subprogram.

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 spatial resolution close to 1 meter or less, preferably in late spring or summer, and also at other times of the year in which trees are differentiated from other land uses such as dried vegetation and / or bare soil, b) Transformation of the original images by some index of vegetation and classification of land uses of interest; and 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 tree plantations / plots of olive groves, citrus / citrus and Mediterranean forest (temperate climate of Mediterranean environment), where it has been possible to differentiate spectrum-radiometrically land uses such as trees, vegetation cover and bare soil that characterize any tree plantation, and with satisfactory and reproducible results (Example 1 and 2).

The process of the invention provides information on each tree and on the whole planting. Thus, it provides individualized information of the coordinates / geographic barycenter, surface and potential productivity, among others, of each tree; and also characterizes tree plantations as a whole, calculating among other parameters the total number of trees, their surface area and overall potential productivity; and coverage indicators of other land uses that are defined, such as green roofs and bare soil. CLUAS can be used to contribute to precision agriculture, tree by tree, of any tree plantations, such as cork oaks, almond trees, holm oaks, citrus, apple trees, olive trees, vineyards, etc., and likewise, to determine the comparative effects of productivity potential 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 or Environmental Technical Assistance Companies, or in Public or Private Agro-Environmental Audits. The procedure covered by this patent will allow certain companies, such as those of agricultural or environmental technical assistance, or the services of agri-environmental audits of Public Administrations or private entities, to plan fertilizer, phytosanitary and irrigation application strategies with precision , these operations adapted to the characteristics of each tree, estimate comparatively the potential productivity and agri-environmental indicators such as the percentage of vegetation cover and / or bare soil of certain areas of a plot and of different plots. 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 comprises the following stages (see Figure 1): a) Remote satellite imagery or photography hyperspectral, multispectral or panchromatic aerial, with a spatial resolution close to 1 meter or less, preferably in late spring or summer, and also at other times of the year in which trees are differentiated from other land uses such as vegetation desiccated and / or bare soil, b) Digitization and georeferencing, by differential GPS to assign geographical coordinates, in the case of non-digitized or geo-referenced aerial photographs, respectively, c) Primary analysis of the image which in turn comprises the following stages: the.) Transformation / obtaining of 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 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 single image or selected simple images, c.3) Definition of digital border values (VDF) of each land use and classification / separation of same in the selected simple image, by means of an iterative process of VDF selection statistically contrasted, c.4) Definition of the grouping of land use to be characterized, with the p dimension parameters (Max Columns and Max Rows) and neighborhood (proximity of grouping), according to the spatial resolution characteristics of the image in progress and the objective of the current study, d) Activation of the Clustering Assessment IDL computer subprogram. IAS.1 (CLUAS®) in the computer program

ENVI and implementation of the selected image in CLUAS®, which in turn includes the following stages: dl) Introduction in CLUAS® of the parameters of the selected clusters in the previous points c.3) and c.4): VDF, dimensions and neighborhood, d.2) Processed by CLUAS® of the agronomic and environmental indicators of the plantation, d.3) Study of the information generated automatically by CLUAS® The data / report generated by CLUAS® provides individualized information of the geographic coordinates / barycenter, surface area and potential productivity, among others, of each tree; It also characterizes tree plantations as a whole, calculating among other parameters the total number of trees, and indicators of their overture and overall potential productivity, and the area of other land uses that are defined, such as vegetation cover and bare soil. The objective of this invention is to generate agronomic and environmental information such as the aforementioned in tree plantations such as olive, citrus, almond, apple, cork oaks, holm oaks, etc., etc. Its objective is therefore to highlight and safeguard the rights to generate information on each tree and on the whole tree planting by processing remote images with the CLUAS® subprogram.

The remote images are taken at the moment when it is possible to differentiate spectroradiometrically the uses of trees, vegetation cover and bare soil that characterize any tree plantation. In temperate climates with a Mediterranean atmosphere, the images are preferably taken at the end of spring or during the summer. Another object of the present invention is the use of the method to determine in any tree plantation the following indicators (relating to trees, vegetation cover and bare soil): a) coordinates / geographic barycenter, surface area, and overall potential productivity and per unit of area of each tree each tree of the plantation; b) the total number of trees, global area, and overall and unit potential productivity of the tree plantations as a whole; Y c) the surface of other land uses that are defined, such as green roofs and bare soil; operations performed automatically by the CLUAS® subprogram. Likewise, the procedure can be used to discriminate and quantify by means of remote sensing the land uses that are defined in simple images of a single band or Vegetative Index, based on the method of grouping pixels of each land use and estimating its geographical center , number of integrated pixels (NP) or surface, digital values integrated in each grouping (VDAG) or global productivity, and VDGA / NP or global unit productivity, operations that the CLUAS® subprogram automatically performs. The use of this procedure to design and implement a precision agriculture program related to the application of fertilizers, phytosanitary products or irrigation water doses in any tree plantation, is also another object of the present invention. This procedure can also be used to estimate agri-environmental indicators according to relative surfaces of tree land uses, vegetation cover 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), green roof (gray) and bare ground (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.

EXAMPLE OF THE EMBODIMENT OF THE INVENTION

Examples of the realization of the patent in olive groves, citrus and Mediterranean forest are described. Example 1. Processing of individual plots of tree plantations of various species

The images corresponding to an olive grove plot (Figure 1), citrus fruits (Figure 2) and Mediterranean forest (Figure 3) have been processed using CLUAS®. The results obtained in these processes are shown in Tables 1, 2 and 3, respectively. Table 1 indicates 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 area (NP, number of pixels / m ² ), potential production (integrated digital values (VDAG) and Productivity Index (VDGA / NP).

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 image corresponds to the green band, from 520 to 600 nm with a pixel size of 25 cm. which generates its processing. Its processing characteristics were as follows: Digital values border from 40 to 99, neighborhood 8, and grouping will be 28 rows and 28 columns.

NTP ¹ 8729

AG X Y NPAG VDAG VDAG / NPAG

AG1 373712.31 4151 109.75 270 20144 74.6

AG2 37371 1, 81 4151 102.75 273 20887 76.5

AG3 373710.84 4151095.5 186 15324 82.4

AG4 373710.06 4151089.25 136 10593 77.9

AG5 373708.44 4151082.25 288 22337 77.6

AG6 373708 4151075.5 355 26747 75.3

AG7 373706.53 4151068.5 313 23302 74.4

AG8 373705.41 4151061, 25 388 29924 77.1

AG9 373704.97 4151054.75 462 33870 73.3

AG10 373703.69 4151048 378 27645 73.1

AG1 1 373702.5 4151041 426 31645 74.3

836.5

NTAG 3475

NTAG / NTP 0.40

IVDA 262418

VDAM 76.05

¹ Abbreviations: NTP, total number of pixels of the processed image; AG, groupings (olive trees); xey, geographic coordinates of each olive tree; NPAG, number of grouped pixels of each olive tree / cluster; VDAG, digital values integrated by olive tree; NTAG, total number of pixels in the set; IVDA, digital values integrated in the set of olive trees; VDAM, average digital value per olive pixel.

For example, the grouping or fourth olive tree (AG4) is the smallest size (136 pixels / / 8.5 m ² ) 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 of the set of olive trees in the image, for example of the total number of trees (11), total area of trees (3475 pixels / 217.1 m ² ), the percentage of the area of olive grove over the total area of the plot (NTAG / NTP, 0.40 / 40%), and 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 individualized information on each citrus and the plantation as a whole. Thus, the grouping or tree 25 (AG25) is the smallest size (4 pixels / 2.0 m ² ) with a potential production of 2049; and the tree / cluster 4 ^or

(AG4) is the largest size (56 pixels / 27.7 m ² ) with a potential production of 28144. In addition CLUAS® obtains / provides information on indicators of the image tree set, for example the total number (30), Total area of trees (1479 pixels), the percentage of the area of trees over the total area of the plot (NTAG / NTP, 0.59 / 59%), and the overall potential productivity (IVDA, 427784), among others . Table 2.- Information of a citrus (orange) plantation [C02] generated by the CLUAS® subprogram, taken in the Panchromatic Quick Bird satellite image, of 0.7 m spatial resolution, taken on May 10, 2005 ( Figure 2). Its processing characteristics were the following: digital border values from 368 to 559, neighborhood 8, and maximum grouping of 7 rows and 10 columns.

Trees / AG X Y NPAG VDAG VDAG / NPAG m

AGl 315688.6 4186001.3 29.0 14172.0 488.7 14.2

AG2 315689.0 4185996.3 30.0 14673.0 489.1 14.7

AG3 315689, 7 4185991.0 29.0 14288.0 492.7 14.2

AG4 315690, 7 4185986.3 56.0 28144.0 502.6 27.4

AG5 315696.1 4185986.3 22.0 10952.0 497.8 10.8

AG6 315691.6 4185983.8 7.0 3675.0 525.0 3.4

AG7 315696.0 4185984.3 4.0 2019.0 504.8 2.0

AG8 315694.9 4186001.5 29.0 14423.0 497.3 14.2

AG9 315694.8 4185997.0 31.0 15199.0 490.3 15.2

AGIO 315695.4 4185991.8 32.0 15143.0 473.2 15.7

AGIl 315700.4 4186002.5 28.0 13964.0 498.7 13.7

AG12 315700.8 4185997.5 41.0 19932.0 486.1 20.1

AG13 315701.0 4185992.5 38.0 18027.0 474.4 18.6

AGl 4 315701.4 4185987.5 39.0 19935.0 511.2 19.1

AG15 315702.3 4185984.8 6.0 3112.0 518.7 2.9

AG16 315707.1 4186003.0 35.0 17240.0 492.6 17.2

AGl 7 315712.6 4186003.0 27.0 13310.0 493.0 13.2

AGl 8 315707.8 4185998.3 44.0 22044.0 501.0 21.6

AGl 9 315713.2 4185998.8 34.0 16638.0 489.4 16.7

AG20 315707.9 4185993.3 37.0 18382.0 496.8 18.1

AG21 315713.5 4185993.8 27.0 13217.0 489.5 13.2

AG22 315709.0 4185988.0 54.0 27508.0 509.4 26.5

AG23 315714.2 4185988.5 31.0 15184.0 489.8 15.2

AG24 315709.5 4185985.5 8.0 4219.0 527.4 3.9

AG25 315714.1 4185986.0 4.0 2049.0 512.3 2.0

AG26 315718.9 4186004.0 36.0 17551.0 487.5 17.6

AG27 315718.8 4185999.5 31.0 15063.0 485.9 15.2

AG28 315719.4 4185994.3 27.0 12943.0 479.4 13.2

AG29 315719.6 4185989.3 40.0 19956.0 498.9 19.6

AG30 315719, 7 4185986.8 6.0 2875.0 479.2 2.9

NTP: 1479 Continuation

NTAG: 866

NTAG / NTP: 0.59

IVDA: 427784

VDAM: 494

¹ Abbreviations: NTP, total number of pixels of the processed image; AG, groupings (citrus); x and y, geographic coordinates of each citrus; NPAG, number of grouped pixels of each citrus / cluster; VDAG, digital values integrated by citrus; NTAG, total number of pixels in the set; IVDA, digital values integrated in the citrus set; VDAM, average digital value per citric pixel.

Table 3 shows the information obtained through CLUAS® of the Mediterranean forest image

(holm oaks / cork oaks) indicated in Figure 3. CLUAS® provides individual quantitative information on each tree and the plantation as a whole. Thus, the grouping or 10 ° tree (AGIO) is the smallest size

(8 pixels / 3.9 m ² ) with a potential production of 4140; and the 17 ° tree / cluster (AG17) is the largest size (138 pixels / 67.6 m ² ) with a potential production of 67793. In addition CLUAS® obtains / provides information on indicators of the image tree set, for example the total number of trees (22), their total area (3024pixels), the percentage of the area of trees over the total area of the plot (NTAG / NTP, 0.3 / 30%), and the overall potential productivity

(IVDA, 428853), among others.

Table 3. Information on 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.

AG XY NPAG VDAG VDAG / NPAG m ²

AGl 315827.84 4186954.5 17 8045 473.2 8.3

AG2 315836.06 4186956.5 24 11303 471 11.7

AG3 315850.84 4186960 42 21070 501.7 20.5

AG4 315842.66 4186954.75 10 5305 530.5 4.9

AG5 315826.25 4186945.75 42 18799 447.6 20.5

AG6 315835.81 4186947.75 49 22764 464.6 24.0

AG7 315843.09 4186950.75 14 6688 477, 7 6.86

AG8 315854 4186952.75 10 4973 497.3 4.9

AG9 315848.88 4186948.5 44 20083 456.4 21.5

AGIO 315858.59 4186952 8 4140 517.5 3.9

AGIl 315861.88 4186948.5 61 28624 469.2 29.8

AG12 315827.97 4186938.25 30 14165 472.2 14.7

AG13 315840.16 4186940 30 13880 462.7 14.7

AGl 4 315848.81 4186942 31 14279 460.6 15.1

AG15 315836.19 4186932.25 93 42669 458.8 45.5

AG16 315844.31 4186932.75 59 27414 464.6 28.9

AGl 7 315854.5 4186936.5 138 67793 491.3 67.6

AGl 8 315859.63 4186937.25 28 13145 469.5 13.7

AGl 9 315855.91 4186931 56 27062 483.3 27.4

AG20 315861.16 4186932.5 12 6070 505.8 5.8

AG21 315865.16 4186941.25 22 10271 466.9 10.7

AG22 315867.59 4186935.75 51 24125 473 24.9

NTP: 3024

NTAG: 903

NTAG / NTP: 0.3

IVDA: 428853

VDAM: 474.9

¹ Abbreviations: NTP, total number of pixels of the processed image; AG, groupings (Quercus tree); xey, geographic coordinates of each Quercus tree; NPAG, number of grouped pixels of each Quercus / grouping tree; VDAG, digital values integrated by Quercus tree; NTAG, total number of pixels in the set; IVDA, digital values integrated in the Quercus tree set; VDAM, average digital value per Quercus tree pixel.

CLUAS® provides individual information on each tree, such as its geographical coordinate, surface area (NP, number of pixels / m ² ), potential production (integrated digital values (VDAG) and Productivity Index (VDGA / NP). CLUAS® also obtains / provides information on indicators of the set of trees in the image, for example of 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 overall potential productivity, between others.

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

Example 2. Comparative processing of several adjacent plots of a given tree species

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

Through CLUAS®, various parameters of each plot are estimated, such as the area, potential production 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 the agro-environmental characterization of each plot, and can be used by the farmer to plan specific agricultural operations for each plot, such as the application of fertilizers, fertilizers and irrigation water, proportional to the parameters estimated in each of them, as for the administrative monitoring of certain agri-environmental measures, such as the percentage of bare soil.

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

¹¹ In each zone: ⁽²⁾ % total plot; ⁽³⁾ integrated DV ⁴⁾ Average DV per pixel of average olive tree DV: Digital values; VDO: digital values set plantation; VDm: digital values of the medium olive.

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Claims

1.- Procedure for the automatic obtaining of agronomic and environmental indicators of tree plantations by remote sensing, which includes the following stages: a) Taking satellite image or hyperspectral, multispectral or panchromatic aerial photography, with a spatial resolution of approximately 1 meter or lower, preferably in late spring or summer, and also at other times of the year in which trees are differentiated from other land uses such as dried vegetation and / or bare soil, b) Digitization and georeferencing, using GPS differential to assign geographic coordinates, in the case of non-digitized or geo-referenced aerial photographs, respectively, c) Primary analysis of the image which in turn comprises the following stages: cl) 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 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 single image or selected simple images, c.3) Definition of digital border values (VDF) of each land use and classification / separation of them in the selected simple image, through a process iterative selection of statistically contrasted VDF, c.4) Definition of the grouping of land use to be characterized, with the dimension parameters (Max Columns and Max Rows) and neighborhood (proximity of grouping), according to the spatial resolution characteristics of the image in progress and the objective of the current study, d) Activation of the Clustering Assessment IDL computer subprogram. IAS.1 (CLUAS®) in the ENVI computer program and implementation of the image selected in CLUAS®, which in turn includes the following stages: dl) Introduction to CLUAS® of the parameters of the clusters selected in the previous points c. 3) and c.4): VDF, dimensions and neighborhood, d. 2) Processed by CLUAS® of the agronomic and environmental indicators of the plantation, d.3) Study of the information generated automatically by CLUAS® 2.- Procedure 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 the 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, vegetation cover and bare soil that characterize any tree plantation. 4. Use of a method according to claims 1, 2 and 3 to determine in any tree plantation the following indicators (relating to trees, vegetation cover and bare soil): a) coordinates / geographic barycenter, surface, and overall potential productivity and per unit area of each tree each tree of the plantation; b) the total number of trees, global area, and overall and unit potential productivity of the tree plantations as a whole; and c) the surface of other land uses that are defined, such as green roofs and bare soil; operations that the subprogram automatically performs CLUAS®. 5. Use of a method according to claims 1 to 3, to discriminate and quantify by means of remote sensing the land uses that are defined in simple images of a single band or Vegetative Index, based on the method of grouping pixels of each use of soil and estimation of its geographic center, number of integrated pixels (NP) or surface, digital values integrated in each grouping (VDAG) or global productivity, and VDGA / NP or global unit productivity, operations that the CLUAS® subprogram automatically performs. 6. Use of a method according to claims 1 to 3 to design and implement a precision agriculture program related to the application of fertilizers, phytosanitary products or irrigation water doses in any tree plantation. 7. Use of a method according to claims 1 to 3 to estimate agri-environmental indicators according to relative surfaces of the land uses trees, vegetation cover and bare soil.