an approach based on remote detection and gis to

AN APPROACH BASED ON REMOTE DETECTION AND GIS TO ANALYSE FOREST AND AGRICULTURAL ACTIVITIES’ POTENTIALITIES IN THE ALGARVE REGION ANTÓNIO XAVIER CEFAGE-UE (Center For Advanced Studies in Management and Economics) E-mail: [email protected]

MARIA DE BELÉM COSTA FREITAS Professora Auxiliar c/Agregação, Universidade do Algarve, Faculdade de Ciências e Tecnologia. MeditBio. E-mail: [email protected].

RUI FRAGOSO Professor Auxiliar c/Agregação Universidade de Évora e CEFAGE-UE (Center For Advanced Studies in Management and Economics). E-mail: [email protected].

MARIA DO SOCORRO ROSÁRIO Direção de Serviços de Estatística, GPP (Gabinete de Planeamento e Políticas). E-mail: [email protected].

Abstract Agriculture and forestry have a different distribution in the territory depending on agroecological conditions and biophysical constraints. The current available cartographic information is incomplete to analyse the distribution of agricultural and forestry uses. However, there is updated information of free and affordable satellite images that can be used. Thus, this paper proposes a methodological approach based on remote sensing and Geographic Information Systems that allows answering to this challenge by combining multiple sets of information throughout several steps: 1) Supervised classification of agricultural and forestry uses, taking advantage of Land Use Cover Area Frame Statistical Survey observations as "training fields"; 2) Mapping the biophysical suitability of different agricultural crops and forest species; 3) Calculus of the expected productivity of agricultural and forestry activities. Keywords: agriculture, classification.

forest,

Algarve,

biophysical

suitability,

supervised

1

1. INTRODUCTION Algarve is the main citrus producing region in Portugal and this sector plays an essential role in the regional agricultural structure, as well as other permanent crops (Valente, 2013). Recent studies on agricultural dynamics of territories showed that a considerable number of municipalities in the Algarve region continue to be oriented towards permanent crops (Xavier and Freitas, 2014). In the Algarve’s inland there are forestry species, such as Quercus Suber or Arbutus Unedo that have a great relevance for the rural communities and for societal their well-being. Policy changes, lead us to create ways of not only for monitoring the evolution of the agro-forestry areas, but also to better identify areas with different biophysical suitability for agricultural and forest species. In this case forest activities are even more relevant since the forest fires have devastating consequences in the territory, and an effective forestation policy can better valorise the most suitable areas (Xavier and Martins, 2010). However, information regarding the real crop distribution is not up-to-date, being limited to the agricultural census at a more disaggregated level. Spatial models for reading biophysical suitability and potential productivity were not developed. Only Xavier et al. (2016a) presented a methodological approach for disaggregating agricultural data using supervised classifications from satellite imagery and entropy, but it needs a more detailed information prior for obtaining better results. For identifying the land uses and calculating biophysical suitability maps, research suggests that Geographical Information Systems (GIS) and remote detection may be useful tools (Rosendo, 2005). Regarding the identification of land uses, a supervised classification is an image processing technique that allows an image’s identification of materials, according to their spectral signatures. It begins by establishing “training fields” (sample areas) for defining the different spectral signatures to be used by the several kinds of classification algorithms. The supervised classifications evolved, existing today various approaches and several sources of information, such as the Land Use Cover Area Frame Statistical Survey (LUCAS), which may help defining the “training fields” (Congalton, 1991). On the other hand, the spatial raster models in GIS combine different criteria to create maps for decision making (McCoy and Johnston, 2001). At the most rudimentary 2

level, a multi-criteria decision problem involves a set of alternatives that are evaluated on the basis of conflicting and incommensurate criteria according to the decision maker’s preferences (Malczewski and Rinner, 2015). The objective of this article is to propose a methodological approach for developing land use and biophysical maps in the Algarve Region, Southern Portugal. Therefore, processes using supervised classifications for several agro-forestry activities and to test the recent SENTINEL 2A satellite images are developed. Then biophysical suitability maps and expected/potential productivity maps are created. The remainder of this article is as follows. In section 2 the methodological approach is presented. In section 3 the study area is analysed. In section 4, some statements to the available satellite imagery and to the LUCAS survey is made. In section 5 the empirical implementation procedures are discussed. Finally, in section 6 the results and discussion are presented, and in section 7 the concluding remarks are made.

2. METHODOLOGICAL APPROACH Figure 1 presents the structure of the methodological approach proposed, which comprises three main steps. The first step is a supervised classification of agricultural and forestry uses, taking advantage from LUCAS observations as "training fields". The second step aims mapping the biophysical suitability of agricultural and forest activities. Finally, the third step where the expected productivity of agricultural and forestry activities are calculated.

Figure 1– The methodological approach 3

According to previous studies (Congalton, 1991; Jeon et al., 2004; Martins, 2012; Graciani et al., 2003, Gabriel, 2013; Congedo, 2015) one considered the following phases of implementation to develop the supervised classifications: 1) Collection of all the available information and selection of the most adequate according to the objectives of the study; 2) Defining the “training fields” using the LUCAS survey samples as references, and addition of new ones using also the empirical knowledge of the analyst; 3) Defining the spectral signatures and implementing the supervised classification algorithm, which is able to provide better results, since it may include the minimum distance algorithm, maximum likelihood algorithm or the spectral angle algorithm; 4) Validation by using confusion matrixes, training fields and reality knowledge.

For developing the biophysical suitability maps, we consider that spatial decision problems typically involve a large set of feasible alternatives and multiple, conflicting and incommensurate evaluation criteria (Malczewski, 2006). The procedures for tackling spatial multi-criteria problems involve three main concepts: value scaling (or standardization), criterion weighting, and combination (decision) rule (Malczewski and Rinner, 2015). Therefore, for constructing the biophysical suitability maps, a spatial raster model is proposed, which combines the several criteria to be considered in a biophysical suitability analysis. This model tries to present a reality that cannot be directly observed, since we can only observe each criteria individually (Trocado, W.D.). In GIS that representation is made by the combination of several spatial information layers to generate a final biophysical suitability map. These models and their development are detailed in various authors such as Trocado (S.D.) and McCoy and Johnston (2001). These authors divide the construction and implementation of these models in several phases: 1) Stating the problem; 2) Breaking the problem down; 3) Exploring input datasets; 4) Performing analysis; 5) Verifying the model’s result; 6) Implementing the result.

4

In practice, after normalizing all criteria in a same scale (eg. 1 to 5), biophysical suitability maps can be obtained from the sum of all criteria, such being shown in the following formulation for a pixel p: C

PC =  CI cp p

c =1

(1)

Where PC p is the biophysical suitability in pixel p and CI is the suitability criterion c in the pixel p. The potential/expect productivity maps are constructed by redistributing the regional productivities according to known variation on the criteria used in the spatial analysis raster model.

3. THE STUDY AREA The Algarve region in the south of Portugal with an area of 4996.8 km2 and composed by 16 municipalities was selected for implementing this study (figure 2). Two pilot areas were chosen to implemented some specific types of supervised classifications: Silves municipality (pilot area 1) and a set of parishes from the Tavira Municipality: Luz, Santa Luzia, Santiago Santo Estevão e Santa Maria, and will be called simply as Pilot area 2. The pilot area 1-Silves has about 680.1 km2 and it was divided in 2009 in 8 parishes which were later reduced to 6, while the pilot area 2 has less than half of this area. In the Algarve region, as in both pilot areas considered, there are several biophysical contrasts both on the littoral, which shows some heterogeneity, and inland with lesser fertile areas and higher slopes (Xavier et al., 2016a). In the Algarve region there are also several areas in demographical decline, mainly inland (Xavier and Martins, 2010). In this area permanent crops have a great importance, namely the citrus (Valente, 2013; Xavier and Freitas, 2014). Also inland there is a considerable relevance for forest activities in terms of area, where Quercus Suber and Arbutus Unedo have a great relevance in several areas, where the soils are poor and mainly from the family Ex.

5

Figure 2– The study area

4. THE AVAILABLE SATELLITE IMAGERY AND THE LUCAS SURVEY There are available several sources of free satellite imagery with moderate resolution. For the scientific community we must highlight the following: the LANDSAT data and the SENTINEL 2 data. LANDSAT is a set of multispectral satellites developed since the early 1970’s National Aeronautics and Space Administration (NASA) (Congedo, 2015) and is very used for environmental research. Several satellites were launched through time following this initiative, including LANDSTAT 5 and 7. LANDSAT 8 is a continuity Mission on February 11, 2013. It contains the push-broom Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). OLI collects data with a spatial resolution of 30 meters in the visible, near-IR, and SWIR wavelength regions, and a 15-meter panchromatic band (USGS, 2015). Today, the Landsat 7 and Landsat 8 satellites both orbit the Earth at an altitude of 705 Kilometres in a sun synchronous orbit (USGS, 2015), although LANDSAT 7 has some operational problems (USGS, W.D.). The Sentinels are a fleet of satellites designed specifically to deliver data and imagery that are central to the European Commission’s Copernicus programme (ESA, W.D.). This is a step change to manage the environment, understand and tackle the effects of climate change (ESA, W.D.).

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The SENTINEL 2 is a multispectral satellite developed by the European Space Agency (ESA) in the frame of Copernicus land monitoring services, which acquires 13 spectral bands with the spatial resolution of 10m, 20m and 60m (Congedo, 2015). The SENTINEL 2A was launched on 23 June 2015 (ESA, W.D.). Figure 3 presents an example of the quality of the resolution images for agricultural management.

Figure 3- Example of e true colour SENTINEL 2A image (Source: ESA, SENTINEL 2A, 17-02-2016)

Other source of information is the LUCAS (Land Use/Cover Area frame Statistical survey), which may be used for the definition of training fields in a supervised classification. It’s a collection of land cover/land use, agro-environmental and soil data in a point frame, i.e. by field observation of referenced points, using field form and by taking several photographs (EUROSTAT, 2013) and it normally has a time frame of around 3 years. The LUCAS survey is co-ordinated by the Statistical Office of the European Commission (Eurostat) and executed in the territory of all EU Member States for all kinds of land uses using a two-stage sampling design, having the first level a regular grid with a size of 18 × 18 km (Kempen et al., 2005). In 2012 around 271,000 points were visited by the field surveyors and in the Algarve region more than 400 survey units were identified.

5. EMPIRICAL IMPLEMENTATION As was stated before in the section dedicated to the methodological approach, the framework proposed comprises the following three steps: a supervised

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classification, mapping the biophysical suitability, and calculate the productivity of activities in the territory. Therefore, for implementing the supervised classification process several aspects had to be defined, namely the choice of training fields and the satellite images to be used. The training fields selected for implementing the supervised classification process considered the information of the LUCAS survey for an initial definition. This basis may be used in part or totally for supervised classifications depending on the objectives and classes considered. In this study, not all the identified training fields from the LUCAS survey were used. Then, the “training fields” were also selected based on empirical knowledge. Figure 4 depicts the training fields considered.

Figure 4- The training fields used in the supervised classification

The LANDSAT 8 image used for the supervised classification of the Algarve region is from 29 June 2013 and from 31 May 2014. In the Silves pilot area a LANDSAT 8 image from 29 June 2013 was used. The LANDSAT images were obtained

from

the

NASA

system

by

using

the

Earth

Explorer: 8

http://earthexplorer.usgs.gov. As regards to the second pilot area, which includes the set of parishes from the Tavira municipality, a SENTINEL 2A image from 17 February 2016

was

used.

The

SENTINEL

2A

image

used

was

obtained

from

https://scihub.copernicus.eu/dhus/. The supervised classifications developed focused on real data needs and the need of having a more detailed distribution of some crops for other studies. The implementation of the approach was made using QGIS and the SemiAutomatic Classification Plugin (SCP) developed by Congedo (2015) based on the use of the Minimum distance algorithm and the Maximum Likelihood algorithm. Although, the Spectral Angle Algorithm was also tested. Regarding the biophysical suitability maps, the criteria to be used were selected by experts and scholars, and they are different according to the crop considered. The information sources used include the following: soil cartography at a 1:25.000 from the IDRHA (Hydraulic, Rural Engineering and Environment Institute); a digital terrain model with a cell size of 25 m provided by the ICNF (Institute for Nature Conservation and Forests), maps of slopes, aspect, hypsometry derived at the same resolution; cartography of the Environment Atlas regarding temperature, total precipitation, insolation, hoar-frost, number of days with rain. The sources from the environment Atlas have a limited resolution, but the information available regarding these subjects was scarce. From the COS 2007 classes regarding urban areas aquatic, humid and other improper were excluded. Also areas without climatic suitability, namely by the influence of winds and other were also excluded in some kinds of crops such as the citrus. Areas with certain soils without suitability were also excluded from several crops, as well as for the crops with slope limitation. For forest activities we also limit them to the actual area of forest and shrubs. Each criteria was reclassified in terms of suitability. Each criteria was ranked in a five classes scale ranging from 1 (less suitable) to 5 (maximum suitability), for several crops, while in some crops due to limitations, this rank was from 1 to 4. The biophysical suitability maps were developed in a raster model using QGIS and GRASS GIS. The final maps were made using a simple additive model. The different types of suitability were divided in: Very low, Low, Median, High and Very 9

high. Limits for each class resulted from a detailed analysis. Figure 5 depicts the procedures for the implementation of the suitability maps using as example the citrus crop.

Figure 5- The technical construction process of the suitability map for the citrus crop

Finally, the productivity maps are potential/ expected productivity maps and result from the redistribution of the regional average productivities according to expected variation of experts in some biophysical conditions as well as information from previous studies and bibliographic references. Sources such as Firmino (1979) were also consulted and provided variations for cereals productions according to soils. Therefore, the production maps are limited to some criteria such as soils, slope and aspect. For the forest activities such as Quercus Suber and cork production, information

10

from the GPP (Policy and Planning Cabinet) was used regarding production and a density of 170 trees per hectare.

6. RESULTS AND DISCUSSION 6.1. SUPERVISED CLASSIFICATIONS The process of supervised classification for several important crops and land uses was carried out for the Algarve and the pilot areas mentioned before. For the Algarve, some examples of the supervised classifications carried out are presented in figure 6 for the LANDSAT 8 in 2013 and 2014. In detail, the first map of the figure 6 is focused in the citrus crop in 2013. The second regards the location of the different types of permanent crops in 2014, while on the third is focused on determining the location of the Pinus Pinea in 2014. Validation of the results was carried out. In the first example presented, the overall accuracy and for the citrus was around 74%. For the supervised classification carried out to identify several general types of permanent crops using the maximum likelihood algorithm, the overall accuracy was 55.5%. For the last map, the supervised classification of the 2014 imagery has used the minimum distance algorithm, being the overall accuracy 55.1% and for the Pinus Pinea, it was 38.1%.

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Figure 6 – The supervised classifications carried out for the Algarve (source: model results)

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Results for the Silves municipality (pilot area 1) are presented next (figure 7) with focus on the citrus crop using the 2013 LANDSAT 8 imagery. Comparison with real satellite image, aerial photographs and empirical knowledge, shows a satisfactory consistency. Validation using the training fields shows an overall accuracy superior to 70% and a 74.6% accuracy for the citrus crop. True colour

Sup. Classification-Min. Dist. algorithm

Figure 7– The supervised classification and the true colour image-LANDSAT 8-2013 (source: model results)

The results using the second pilot area and the SENTINEL 2A image are presented in figure 8. Comparison between the real image and the supervised classification can be made, and the analysis aerial photographs shows that results tend to be generally consistent. Also, an overall accuracy of 58% was obtained using the training fields. Results seem promising and show that this new source of information with a higher resolution may be a good starting point for analysing agricultural land use with more detail. This type of results may be a source of information for developing the model proposed by Xavier et al. (2016a) at a municipality level, being improved further.

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True colour

Sup. classification-Max. Lik. algorithm

Figure 8– The supervised classification and the true colour image-SENTINEL 2A-2015 (source: model results)

6.2. THE BIOPHYSICAL SUITABILITY MAPS Examples of the biophysical suitability maps developed are presented in figure 9. These results show the areas with suitability for developing citrus crops and that there are several areas with high suitability which may be used further by the farmers. These are mostly in the near littoral area in the so called “Barrocal”. Basically, the inland has no biophysical suitability for this crop. In the case of cereals there are just a few areas with high suitability, while the most common class is of median potential. Inland, classes are mostly of low and median suitability. The contrasts of horticultural crops have some similarities with cereals, and it’s possible to identify some high and very high potential in the south areas near the littoral. Finally, regarding to the Quercus Suber allocation, results show that there are a dominance of areas with a median potential. The areas with high potential seem to be located in the Caldeirão and Monchique areas. However, this analysis should be careful, since the climate data used has a low resolution and are from the Environment Atlas Information. In addition this kind of allocation is limited to the area of Forest and Shrubs identified in the COS 2007, being not considered the other areas.

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BIOPHYSICAL SUITABILITY

Figure 9– The biophysical suitability maps (source: model results)

15

6.3. THE PRODUCTIVITY MAPS The potential/expected productivity was calculated for several crops and the results are presented in the figure 10. Note that not all the biophysical criteria considered in the elaboration of the biophysical potential maps were integrated since information is lack regarding the main general variation of production (or general breaks), being considered mostly the following ones: aspect and soils. Based on these criteria one is able to identify some major contrasts regarding the several crops. In the example of the citrus it seems to be coincident the highest productions with the areas of highest biophysical suitability. In soft wheat and cabbage, the most representative crops in their groups, contrasts are also seen between the littoral and inland, with lower expected productivities. Also, we must highlight that in the case of cork production, it seems to be a more homogenous distribution.

6.4. DISCUSSION The methodological approach using the recent satellite imagery is a good tool for agricultural planning and analysis. It may be used for a general comparison with the biophysical potential maps created, namely by observing if the potential is fulfilled. The information provided by supervised classifications may be then included in the model proposed by Xavier et al. (2016a) and improved further. The analysis of the SENTINEL 2A imagery and the supervised classification carried out is of great value for agricultural analysis in spite the errors that may be identified. However, there are some issues that still need a careful discussion. An issue regards the several classes used and subdivisions (microclasses) considered (Congedo et al., 2015), since the ones chosen were in agreement with the agricultural census, but still revealed some heterogeneity among them. A better division will surely improve the results and the required data have to be discussed.

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PRODUTIVITY

Figure 10– The potential/expected productivity maps (source: model results)

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The research developed in this paper also approached a way for creating suitability maps for different crops in the form of biophysical suitability maps. The methodological approach developed considers equal weights for the different criteria. However, that’s not the reality, and different weights can be attributed to each criteria, which needs to be better defined in the methodological approach. Hence comparison matrixes used by the Analytical Hierarchal (AHP) process may be a good solution or the Extended Goal Programming approach, which has the great advantage in identifying the majority and minority consensus (González-Pachón and Romero, 2007; DiazBalteiro et al., 2009; Xavier et al., 2016b). Other issue to analyse regards the relation between the areas with biophysical suitability and the areas actually used by the farmers or landowners. Preliminary studies for citrus show that farmers follow a logical allocation and tend to have their crops in the areas that offer the best biophysical conditions for them. This rational needs a further analysis. Biophysical suitability maps may have a considerable value for farmers, namely for providing an informative and logical choice of crops. However an important question arises related to the best way to insert this information for improving the allocation of future projects. This study used information from the Environment Atlas, which has a limited resolution and it’s not up-to-date. However the Regional Bureau of the Ministry of Agriculture (DRAPALG) has a network of meteorological stations. Therefore, in the future this set of information should be considered to better derive maps. Finally, the expected/potential production maps presented in this work are limited due the lack of knowledge about variation on criteria.

7. CONCLUDING REMARKS This paper showed that the use of freely available satellite imagery may be very interesting for the analysis of agricultural and forest areas, since it allows the development of supervised classifications. In spite the limitations, prior information may be used, such as in the approach proposed by Xavier et al. (2016a). The more recent SENTINEL 2A satellite imagery offers a great possibility at this level, since it 18

has a greater resolution and a good periodicity. The approach proposed is a good tool for agricultural and forest management and should be used in these studies. The biophysical suitability maps presented in our work are a relevant source of information that may be used in agricultural projects and are a good complement for the soil capacity cartography, which is only directed for cereals production. The production maps may be a complement with information provided, and will allow a good spatial distribution of the results presented in the statistics. To improve these preliminary results and to develop better and more consistent cartography, new researches are being carried out with the help of experts and technicians.

ACKNOWLEDGEMENTS: The authors want to acknowledge the advice of some technicians of DRAPALG namely José Tomás.

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