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Ecology of the Sontecomapan Lagoon, Veracruz

 | 
Maria Elena Castellanos-Páez
, 
Alfonso Esquivel Herrera
, 
Javier Aldeco-Ramírez
, 
et al.

Conservation and restoration

Changing trends in mangrove the forest cover of the Sontecomapan Lagoon System, Veracruz, with Landsat® satellite images

José Luis Hernández-Gaona, Javier Aldeco-Ramírez et Iván Ernesto Roldán-Aragón

Résumé

The mangrove forest area in lagoons tends to be disturbed by different factors and this leads to ecosystem deterioration. This work analyzes and quantifies the changing trends in mangrove forest cover of the Sontecomapan Lagoon, Mexico, through Landsat® satellite imagery of 1979, 1986 and 2000. In Landsat® images with different RGB compositions, a polygon delineation of the different surfaces was depicted. The segments were transformed into polygons and several classes were identified: Water from the Lagoon System, Mangle, Urban Zone, Agricultural Zone, Other Vegetation Type, Other Type of Coverage or Land Use and Sea Water of the Gulf of Mexico. Maps of persistence, and tables of gains and losses between classes were generated. The results indicated a reduction of Mangrove and Other Vegetation Type, with gain in the Agricultural Zone. An annual loss of mangrove forest area of 422 ha was observed between the extreme dates 1979-2000 (at a rate of approximately 1.64 % per year). Due to its commercial value, economic loss was approx. $ 4,194,000 USD ha-1 a year-1. The mangrove forest cover’s dynamics, tendency and explanation, provide signs about elements that require attention in its management.

Texte intégral

Introduction

1The mangrove forest is an ecosystem that works as the interface between the marine and terrestrial worlds. It is especially adapted to saline soils and watery conditions and is distributed geographically in the tropical and subtropical strip, bordering the coast mainly in river mouths, lagoons, estuaries and terrains with flat and muddy relief. In estuaries and islands, this forest is periodically and partially flooded by relatively calm waters, with a small span between high and low tide (Kathiresan & Bingham, 2001). Mangrove species can adapt to different salinity conditions, from brackish to hypersaline water, that’s why they are called halophyte plants, since they can withstand those changes (Arrueta, 2008). In the world’s tropical regions the mangrove forest high productivity is widely recognized. Mangroves provide habitat for fish, mollusks, crustaceans, birds, insects, reptiles and mammals, whose exploitation is carried out by thousands of coastal residents for food and commerce. The mangrove ecosystem also provides environmental services: enhancing water quality in estuaries, protecting coastal areas from flooding, storms and erosion; organic matter production and export to estuarine areas, and retention and accumulation of sediments and heavy metals present in estuary water columns (Bodero & Robadue, 1995). However, because of their location in the coastal zone, mangroves are vulnerable to anthropogenic disturbances and have high rates of deforestation.

2In Mexico the mangroves have an extension of 770,057 ha, and extends along the entire Mexican coast. Mexico’s mangrove area estimated by INEGI in 1976 was 1,041,267 ha, with 69 % located in the Atlantic coast and 31 % in the Pacific. The country area delineated as mangrove in the year of 2000 was about 880,000 ha, with 62 % located in the Atlantic and 38 % in the Pacific. It has been estimated that the annual loss rate of mangrove area on both coasts between 1976 and 2000 was 2.5 % and it is expected that, if the same rate continues, in a 25-year period, about 50 % of the mangrove in Mexico will be lost (INE, 2005; Calderón et al., 2009).

3According to the CONABIO’s National Mangrove Inventory (2009b), the published data for assessing the area occupied by mangroves in Mexico have discrepancies in methods, mapping sources and analysis scales, because it does not allow comparisons between different map or date products. These differences do not allow us to know for sure the total surface occupied by mangrove within the national territory. There is also no clear understanding of the factors that have influenced the mangrove changes over the last three decades. There is a wide discrepancy between the several mangrove extension estimates in Mexico since the 1970’s. The values range from 440,000 ha to about 1.5 million. The differences are basically due to diversity on methods and analysis scales used in each study. That mangrove forest area estimate (770,057 ha, at a mapping scale of 1: 50,000) only took into account mangrove areas larger than 1 ha. Due to the physical characteristics of Mexican territory, remote sensing techniques were needed in projects focused on natural resources study. The land surface is constantly modified, due to deforestation, human and industrial settlements, roads and more; these modifications can be assessed with the application of remote sensing technology (Soria-Ruiz & Fernández-Ordóñez, 2002; Krause et al., 2004).

4Mexico is among the countries with the highest rate of mangrove deforestation in the Americas; however, there is no reliable data on national mangrove cover, making it impossible to accurately estimate mangrove loss rate over time. It is necessary to obtain accurate information to evaluate the changes in the local and regional scales to detect magnitude, trends and agents of the mangrove transformation (Hirales-Cota et al., 2010).

5Evaluations on the mangrove cover at the Mandinga lagoon system, (Veracruz, Mexico) resulted in 438.43 ha in 1985 and 351.62 ha in 2005 (1.2 % annual loss in a 20 year-period). By 1985, urban use showed 264.68 ha area and by 2005 this urban use was 586.37 ha, which reflects the strong pressure from this growth against the mangrove. Environmental services that were not provided in 20 years by the mangrove was valuated at $ 70.9 million USD; benefits that neighboring communities no longer perceived (Cabrera, 2009). At the Pacific Coast, in the State of Nayarit, Mex., mangrove cover change trends in the Teacapán-Agua Brava lagoon system were evaluated through a multitemporal analysis with Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images from Landsat® satellites, from year 1973 to 2000. A mangrove loss higher than 23,000 ha was observed, mainly due to an agricultural land transition of 40 %. The mangrove cover maintained at the same site was estimated at 57,723 ha during the 27 year-period covered by this study, equivalent to 65 % of the mangrove area estimated in 1973 (Berlanga-Robles & Ruiz-Luna, 2007).

6The lack of an accurate and thorough recognition of the ecological, economic and social importance of mangroves, endorses the loss services provided by these ecosystems (Hirales-Cota et al., 2010). Discordant figures exist on the current deforestation extent and rates, which makes it necessary to assess the current status and mangrove forests change trends. Recording synoptic and recursive coverage at local and regional scales through remote satellite sensing, is a reliable source of information (Berlanga-Robles & Ruiz-Luna, 2007).

7The Sontecomapan lagoon system, has an 891 ha area, with a mangrove cover of about 523 ha, and has three phytoplankton species of the six recorded in Mexico: the red mangrove Rhizophora mangle (Rhizophoraceae), the black mangrove Avicennia germinans (Verbenaceae) and the white mangrove Laguncularia racemosa (Combretaceae) (CONABIO, 2009b). This lagoon system is regarded as biologically important because of the connectivity between mangroves, seagrass and coral reefs, thus allowing flow between endemic species. This ecosystem works as habitat: nesting, spawning, feeding, shelter and as nursery area for alevin fish and crustacean larvae; it also provides refuge for flora and fauna and has a high aesthetic, recreational and research value. Due to the ecological functions provided by this lagoon system, it has been included as part of Los Tuxtlas Biosphere Reserve (Gómez-Marín, 2003; CONABIO, 2009a). The human inhabitants of the surroundings use the lagoon for fishing with commercial and subsistence purposes, and as a recreational area (Olguín-Aguilera, 1993) however, these services are threatened by strong anthropogenic pressure, particularly by deforestation.

8Therefore, this work’s objective is to analyze the mangrove forest cover change trends at the Sontecomapan Lagoon System, as determined from Landsat@ satellite images of the years 1979, 1986 and 2000.

Materials and Methods

Study area

9The Sontecomapan Lagoon is located between 18°29.70’ and 18°35.39’ North latitude, and 94°58.49’ and 95°3.85’ West longitude (Fig. 1). The lagoon system is divided into several zones: the bar that extends from the beach to Roca Morro, El Real channel that covers La Palma river area and ends in a channel that opens and forms the lagoon, with an average depth of 1.50 m (Olguín-Aguilera, 1993). The lagoon is divided into three zones, due to two deltas formed by the Coscoapan river and the approximated water mirror’s area is 891 ha (Portilla-Ochoa, 2005).

10The lagoon is about 12 km length and 1.5 km wide, it lies in the basin formed by San Martin volcano and Sierra Santa Marta mountain range, and feeds from several rivers and streams: Palma, Sumidero, Basura, Sontecomapan, Chuniapan, Coscoapan, Old River Coscoapan, El Fraile, Sábalo, Yahualtajapan, Los Pollos and La Boya. These rivers preserve some riverside forests, wet forests, reeds and some high perennial forests. Due to two deltas formed by the Coscoapan river, the lagoon is partially divided into three zones.

11From Barra de Sontecomapan and El Real to the Northwest, Jicacal beach extends to the North for about 8 km, inland there is a flat, lowland and floodable area where some mangrove and floodable forest formerly stood, but currently most of it has been converted to pasture field and cropland (Gómez-Marín, 2003). The tidal cycle is diurnal with small tide amplitude (about 70 cm). The lagoon’s water level along the year varies from 2 to 3 m due to rain season and floods, also the water quality is affected because of the river drainage; as a result of the basin deforestation the tributaries drag materials, cause erosion, mudslides and water siltation.

Figure 1. Sontecomapan Lagoon System, Veracruz, Mexico. The lagoon is located in the Southeast coast of the Gulf of Mexico, North America.

Figure 1. Sontecomapan Lagoon System, Veracruz, Mexico. The lagoon is located in the Southeast coast of the Gulf of Mexico, North America.

12According to Köppen’s classification the climate of Sontecomapan Lagoon is in Group A: warm, with annual mean temperature over 22 °C with temperature on the coldest month above 18 °C and an average annual temperature of 24 to 26 ºC. The extreme upper temperature is 34 to 36 °C, and is usually reached in April and May (the driest months). Precipitation is influenced by humid winds from the Gulf of Mexico, with annual rainfall of 3,000 to 4,000 mm and storms occurring on 20 to 40 days a year (García, 1981).

13Currently, the mangrove covers about 523 ha and there is a system of coastal dunes. The fishery in the area is is about 671 mt/year-1, and is equivalent to 0.5 % of Veracruz total fish production (Gómez-Marín, 2003). At an economic level, this mangrove is a source of food and income for many families, and is a Natural Protected Area run by the Universidad Veracruzana. The mangrove of Sontecomapan is integrated by three mangrove species, which are Rhizophora mangle (red mangrove), Avicennia germinans (Verbenaceae) (black mangrove) and Laguncularia racemosa (Combretaceae) (white mangrove); the structure, distribution and abundance of these tree species give this mangrove a great biological importance (Carmona-Díaz et al., 2004). Endemic vegetation is composed of high perennial forest, oak forest and deciduous forest. Plant species introduced by man are induced grasses, fruit trees, grains and forages for local consumption (Galván et al., 1999). The Sontecomapan mangrove also functions as habitat for a large number of other plant forms such as mosses, lichens, ferns, non-vascular and vascular epiphytes, lianas and parasitic plants, which makes it very diverse since it is uncommon to find other plants associated with mangrove ecosystems. The presence of vascular epiphytes is a rare feature in mangroves, not only in Mexico but in the rest of the world. The alkaloids and tannins that mangrove trees possess, generally do not allow the establishment of this type of plants, however, in the Sontecomapan mangrove they are widely represented by the families Pteridaceae, Araceae, Orchidaceae, Cactaceae, Bromeliaceae, Piperaceae, Gesneriaceae and Moraceae (Carmona-Díaz et al., 2004).

Image analysis

14Remote sensing is an approach to ecological study and diagnosis that is firmly framed in the scientific method (Lira, 2003). There are several methods for the digital analysis of satellite images, supported by changes in vegetation cover or changes detection in coastal environments, that help to understand the ecosystem dynamics, and to further explain the mangrove forest change trends. Remote sensing images processed using Geographic Information Systems (GIS) technology are a working tool in landscape ecology since they are used for ecological study and diagnosis of an area affected by anthropic intervention (Lira, 2003; Vázquez, 2010). In the GIS environment different thematic maps are worked and edited by interpreting the land use, thus quantifying the spatial structure according to the degree of landscape modifications (Moizo-Marrubio, 2004; Rosete & Bocco, 2003; Cámara et al., 1996). The high contrast that exists in the energy reflected by vegetation in the red and infrared window of the electromagnetic spectrum is the basis for different quantitative indices used for biomass estimation, environmental changes and identification of soil degradation, among others. The vegetation indices in remote sensing allow to maximize the sensitivity of the plants’ biophysical parameters; normalize or model the effects of the sun’s angle of incidence, satellite’s inclination and atmospheric effect, normalize the soil’s variations effects at the bottom of the tree tops, the topography variations; and to relate the information with some measurable biophysical parameters (Vázquez-Andrade & Álvarez-Béjar, 2011).

15The different spectral bands recorded are analyzed individually to obtain information about natural resources, but the analysis is enriched with the use of band combinations (Rosete & Bocco, 2003). The acquisition period is crucial for multitemporal analyses, for either detecting changes between two reference dates or to study the plant’s cover seasonal cycle (SAGPA, 1998). Changing detection in coastal environments provides indicators of ecosystem dynamics and information on key elements that require special attention in their analysis and management. Different methods for change detection have been developed from the digital analysis of satellite images, all of them based on the premise that changes in the aquatic and terrestrial coverage must generate changes in the images’ radiance values that are larger than changes generated by other factors such as differences in atmospheric conditions, sun’s axis and soil’s moisture conditions (Singh, 1989; Yuan et al., 1998; Berlanga-Robles & Ruiz-Luna, 2007).

16The satellite high-quality images needed for the analysis were found on the Landsat.org website, where dates with the characteristics necessary for the study, cloudless and with low moisture in the atmosphere, were found: 1979/02/28, 1986/03/18 and 2000/01/04. The three selected dates correspond to states of the system: 1979, a quasi-pristine state; 1986, the beginning of the enthusiastic urban development and 2000 the beginning of environmental awareness from the government.

17The remote sensors used in this study were those of the Landsat® satellite family described below (Table 1): Landsat MSS for 1979 image, Landsat TM for 1986 and Landsat ETM+ for 2000. The path/row (p/r) were 25/47, 23/47, and 23/47 for the pictures of 1979, 1986 and 2000, respectively. These images are part of NASA’s contribution to the global research community, and are provided through the Landsat.org website and Tropical Rain Forest Information Center, a member of NASA’s Federation of Earth Information Science Partners (Landsat, 2010).

Table 1. Characteristics of the Landsat® satellite images used in this work. Image from year 1979 is a MSS, image from 1986 is a TM y and the 2000 image is an ETM+ (from LANSAT, 2010)

Table 1. Characteristics of the Landsat® satellite images used in this work. Image from year 1979 is a MSS, image from 1986 is a TM y and the 2000 image is an ETM+ (from LANSAT, 2010)

18Based on information from the study area of the Sontecomapan Lagoon and its surroundings, including the Los Tuxtlas region, a working legend was developed (Table 2), which allowed the cover class assignments to perform an interpretation of what was detected from the Landsat satellite. Hybrid maps were created from the combination of satellite imagery and land cover class maps from representative samples of known objects present in the image according to field tests. Classification was performed once the images’ spectral classes were defined, that is, each element of the scene or pixel was classified, assigning maps’ elements or more related photos (Bocco et al., 1991).

Table 2. Work legend used in class type classification. First column: Arabic number is the class number for the named class, each class has an assigned color, second column is the type and/or the land use

Class

Vegetation type and/or land use

1. Water/Blue

Lagoon System Water

2. Mangrove/Green

Red Mangrove
Black Mangrove
White Mangrove

3. Urban Zone/Gray

Villages

4. Agricultural Zone/Brown

Agriculture
Cattle ranch
Aquiculture

5. Other Type of Vegetation/Orange

Riverside woods
Moist evergreen rainforest.
Low land flood forest

6. Other Type of Coverage or Land Use/Yellow

Routes of terrestrial communication
Flood zones
Sand/beach/eroded soil

7. Water/Royal Blue

Gulf of Mexico sea

8. Less than 4 ha.

Less than 4 ha polygons

19A classification system was used for the different classes that were assigned to the process of each of the satellite images from the different dates. The land use of the study area was reviewed and a hierarchical system was generated. This working legend (Table 2) allowed us to solve three problems: (1) the coverage classification at various levels of spatial resolution, (2) a hierarchical organization covering the study area that includes defined criteria for each of the seven aggregation levels, and (3), an objective representation of vegetation dynamics, to include classes in continuous transformation as to the change direction of each one. That’s why this working legend was adopted as the baseline for land-use/land-cover change estimates. According to the definition given by Singh (1989), detection change is: “the process of identifying differences in the state of an object or phenomenon by observing it at different times”.

20Given the small size of the study area, it was decided to carry out a visual interpretation of the satellite images. The steps performed were as follows:

  • Images from three dates, 1979 MSS, 1986 TM and 2000 ETM+, were downloaded from Landsat.org and read with the IDRISI® ANDES v15 software.

  • The images were georeferenced, while a subscene of the originals was obtained, which allowed homogeneity among the images to be processed.

  • With each date’s subscene, different vegetation indexes or transformations (Normalized Difference Vegetation Index [NDVI], PCA, Tasseled Cap) were generated, which were used later to obtain RGB compositions. The generated combinations were compared to pick the best choice to evaluate land use classes and vegetation of the area.

  • The ILWIS® software v. 3.4 was used to develop land use maps use and vegetation. RGB compositions were imported to ILWIS considering UTM coordinates (zone 15Q) and WGS84 spheroid. For the visual interpretation phase, a digital polygon delineation (segmentation) of the observed surfaces on the different RGB images was performed for each year. Study area segmentation for each year was done at a
    1: 30,000 scale, and the final images were built at a 1: 100,000. The minimum mapping unit was stated at 1 ha (10 000 m
    2).

  • From these images, each polygon’s assignment to its corresponding class proceeded (previously established in the work legend). Class assignments were corroborated with INEGI maps and Google Earth images, and a raster image was generated for each date.

  • The previous generated images of segments and polygons were in vectors, and each one of them had its own class legend. These images were exported to raster format and read again in IDRISI for its final processing. As this process eliminated smaller areas (polygons) of less than 4 ha, this step generated one more class, which was called “Less than 4 ha”.

  • The classes and colors used were: Lagoon System Water (Blue), Mangrove (Green), Urban Zone (Gray), Agricultural Zone (Brown), Other Type of Vegetation (Orange), Other Type of Coverage or Land Use (Yellow) and Gulf of Mexico Sea Water (Royal Blue) (Table 2).

  • With the IDRISI application “Land Change Modeler for Ecological Sustainability” a project with the images from each date was created, but only a pair can be compared at time (Eastman, 2006). Therefore, three groups were generated: first, from 1979 to 2000 (extreme dates), second, from year 1979 to year 1986 and, third, from year
    1986 to year 2000. The last two groups were called “intermediate dates”. This function calculated the changes in ground class coverage (changes in the work legend) for each period. In this process, only seven classes were considered: Water from the Lagoon System, Mangrove, Urban Zone, Agricultural Zone, Other Type of Vegetation, Other Type of Coverage or Land Use, Sea Water from the Gulf of Mexico.

  • From the extreme and intermediate dates the change analysis was obtained. Class gains and losses, net change per class, contributions to the net change expressed by each category and persistence were evaluated. From this information, gains and losses tables from one category to another, and persistence were generated.

Results

21The results obtained from the analysis of Landsat satellite images from three dates are shown below. First, the change direction of each class is shown, and then the coverage of each class with respect to the extreme dates, from year 1979 to 2000, and on intermediate dates, from year 1979 to 1986 and from year 1986 to 2000.

22Table 3 shows the loss, gain and net change in coverage (in hectares) of each class for the intermediate dates range from 1979 to 1986. The class with the greatest area loss was Other Type of Vegetation, with 1,473 ha, and a gain of 579 ha giving a net change of -893 ha (the minus sign means loss). In this interval the mangrove cover lost 325 ha, gained 144 ha, and the net change was -181 ha; the Agricultural Zone lost 311 ha, gained 1,345 ha, and the net change was 1,034 ha; the Urban Zone lost 11 ha, gained 71 and the net change was 60 ha.

Table 3. Loss, gain and net change in coverage, in hectares (ha), of each class for the intermediate dates range from 1979 to 1986

Classes

Lost (ha)

Gain (ha)

Net Change (ha)

1 Lagoon System Water

- 31

59

28

2 Mangrove

- 325

144

- 181

3 Urban Zone

- 11

71

60

4 Agricultural Zone

- 311

1,345

1,034

5 Other Type of Vegetation

- 1,473

579

- 894

6 Other Type of Coverage or Land Use

- 103

54

- 49

7 Gulf of Mexico Sea Water

0

2

2

23Table 4 shows the contributions in hectares of each class to the net change in the intermediate dates of 1979-1986. These were as follows: Class 2 Mangrove yielded 24 ha to class 1 Water of the Lagoon System. The class 4 Agricultural Zone removed 49 ha from class 2 Mangrove. Class 5 Other Type of Vegetation removed 113 ha from the class 2 Mangrove. Class 5 Other Type of Vegetation yielded 966 ha to 4 Agricultural Zone.

Table 4. Class contributions of coverage, in hectares (ha), to the net change expressed by class for the intermediate dates of 1979-1986

Table 4. Class contributions of coverage, in hectares (ha), to the net change expressed by class for the intermediate dates of 1979-1986

24Table 5 shows the loss, gain and net change in coverage of each class for the intermediate dates range from 1986 to 2000. The class with the greatest area loss was 5 Other Type of Vegetation, with 1,644 ha, and gained 620 ha, resulting in a net change of -1,024 ha; the class 2 Mangrove cover lost 263 ha, gained 80 ha, and the net change was -183 ha; class 4 Agricultural Zone lost 471 ha, gained 1,588 ha, and the net change was 1,117 ha; Urban Zone lost 8 ha, gained 50 and the net change was 42 ha.

Table 5. Loss, gain and net change in coverage, in hectares (ha), of each class for the intermediate dates range from 1986 to 2000

Classes

Lost (ha)

Gain (ha)

Net Change (ha)

1 Lagoon System Water

- 27

26

- 1

2 Mangrove

- 263

80

- 183

3 Urban Zone

- 8

50

42

4 Agricultural Zone

- 471

1,588

1,117

5 Other Type of Vegetation

- 1,644

620

- 1,024

6 Other Type of Coverage or Land Use

- 91

129

38

7 Gulf of Mexico Sea Water

0

11

11

25Table 6 shows each class contributions to the net change, in the time interval of the intermediate dates of 1986-2000. These were as follows: Class 2 Mangrove yielded 8 ha to Water of the Lagoon System. Agricultural Zone took 18 ha from class 2. Other Type of Vegetation removed 150 ha from Mangrove. Other Type of Vegetation yielded 1,123 ha to Agricultural Zone, etc.

Table 6. Contributions, in hectares (ha), to the net change in coverage expressed by class for the intermediate dates of 1986-2000

Table 6. Contributions, in hectares (ha), to the net change in coverage expressed by class for the intermediate dates of 1986-2000

26Table 7 shows the loss, gain and net change in coverage for each class in the range of extreme dates. The class with the greatest loss of area was Other Type of Vegetation with 2,528 ha, and gained 611 ha, this is a net change of -1,917 ha. Mangrove cover lost 422 ha, gained 57 ha, and the net change was -365 ha. Agricultural Zone lost 304 ha, gained 2,455 ha, and the net change was 2,151 ha. Urban Zone lost 7 ha, gained 109 and the net change was 103 ha.

27Table 8 shows the contributions of each class to the net change during the extreme dates interval. These were as follows: Mangrove yielded 32 ha to Water of the Lagoon System. Agricultural Zone removed 54 ha from Mangrove. Other Type of Vegetation removed 277 ha from Mangrove. Other Type of Vegetation yielded 2,102 ha to Agricultural Zone, etc.

Table 7. Loss, gain and net change in coverage, in hectares (ha), of each class for the extreme dates from 1979 to 2000

Classes

Lost (ha)

Gain (ha)

Net Change (ha)

1 Lagoon System Water

- 34

60

27

2 Mangrove

- 422

57

- 365

3 Urban Zone

- 7

109

103

4 Agricultural Zone

- 304

2,455

2,151

5 Other Type of Vegetation

- 2,528

611

- 1,917

6 Other Type of Coverage or Land Use

- 123

112

- 12

7 Gulf of Mexico Sea Water

0

13

13

Table 8. Coverage contributions, in hectares (ha), to the net change in coverage expressed by class for the extreme dates from 1979 to 2000

Table 8. Coverage contributions, in hectares (ha), to the net change in coverage expressed by class for the extreme dates from 1979 to 2000

28Table 9 shows the coverage in hectares of each class and on each date. It also shows the difference between intermediate and extreme dates. Class 2 Mangrove presented a coverage area of 1,245 ha in 1979, an area of 1,063 ha in 1986 and an area of 880 ha in year 2000. The loss in its coverage area from 1979 to 1986 was 181 ha; from years 1986 to 2000 a reduction of 183 ha was observed, and between 1979 and 2000 the Mangrove cover decreased by 364 ha. The class 4 Agricultural Zone had a loss of coverage of 1,590 ha in the intermediate dates from 1979 to 1986, gained 1,116 ha in the intermediate date from 1986 to 2000, and between extreme dates the area had a loss of 473 ha. The class 5 Other Type of Vegetation, in the range of extreme dates (1979-2000), had a gain of its coverage of 707 ha.

Table 9. Total coverage, in hectares (ha), of each class in each year. The differences between the evaluated years are also shown

Table 9. Total coverage, in hectares (ha), of each class in each year. The differences between the evaluated years are also shown

29With the results of gains, losses and net changes in the study area, it was found that there was a persistence for each of the classes regarding the different dates, which were reduced over time from 1979 to 1986 and 2000 According to one date to another, the study area has undergone a transformation of its classes (Fig. 2). The mangrove had a persistence of 73.86 % (919 ha) for the period 1979-1986, for the period 1986-2000 to 75.26 % (800 ha), and finally in the period 1979-2000 it had a persistence of 66.13 % (823 he has). The same is found with a decrease Other Type of Vegetation that presented 61.31 % (2,333 ha), 43.56 % (1,268.92 ha) and 33.57 % (1,277 ha) for the periods 1979- 1986, 1986-2000 and 1979-2000, respectively. With the Agricultural Zone class, a persistence of 73.65 % (870 ha), 78.74 % (1,744 ha) and 74.27 % (877 ha) was obtained for the periods 1979-1986, 1986-2000 and 1979-2000, respectively.

Discussion

30The land cover/use change analysis is usually particular or include several ground types. In the first case, as long as there is a suitable reflectance of the ground type to be analyzed over time, and the spectral signature is known, it is possible to make a temporal analysis of how much this land cover type changes. Long & Skewes (1996) used only two classes, one for mangrove and the other for water/land. In another context, if several factors or pressures are present, it will be necessary to consider several types of land coverages to see their evolution over time. The selection of classes is not always easy; minimum area requirements, good reflectance and a well-defined spectral signature are required. Berlanga-Robles and Ruiz-Luna (2007) used four kinds of natural wetlands, an artificial wetland and a land cover class; with ground truth (training areas) generated spectral signatures for each class.

Figure 2. Class coverage change around Sontecomapan, Veracruz, through the three recorded images dates (1979, 1986, 2000).

Figure 2. Class coverage change around Sontecomapan, Veracruz, through the three recorded images dates (1979, 1986, 2000).

31In the present work it was considered that the pressure or implication of several land cover classes were necessary to understand the process of land use change around the Sontecomapan lagoon, for this the following classes were included in the work legend: Sea water of the Gulf of Mexico, Water of the Lagoon System, Mangrove, Other Type of Coverage or Land Use, Other Type of Vegetation, Agricultural Zone and Urban Zone. With these classes it was possible to differentiate the change occurred in the study area between different years: 1979, 1986 and 2000. The software tools allowed to observe and quantify the transformation of the mangrove’s forest coverage in each class, as well as the coverage transformation of the different classes.

32The changes’ analysis observed on the vegetation coverage, including deforestation, degradation and revegetation, among others, caused by the anthropic activities, is a fundamental element in the landscape characterization and is expressed as the persistence (Palacio-Prieto et al., 2004). Since the change rate in vegetation has been considered as one of the anthropic influence indicators, then the rates of deforestation of mangrove can be assessed, and thus the disturbance by different factors (pressures) that lead to the deterioration of the ecosystem. This supports the use of vegetation change rates as the basis for land use ordination and management policies (Blanco-Libreros & Estrada-Urrea, 2015). The results indicated that a reduction of Mangrove and Other Vegetation Type persisted, with gain in the Agricultural Zone (based on Tables 2, 4, 6 and 9 it is shown that between 1979 and 2000 agricultural zone increased whereas other type of vegetation decreased). A mangrove forest area total loss of 422 ha was observed between the extreme dates 1979-2000 (1.64 % per year approximately); this implies that the mangrove forest coverage dynamics, its tendency and explanation, provide a sign about elements that require attention in its management; if its present trend (1.64 % per year loss) is not reverted, a loss of additional 247.8 ha in mangrove coverage in the 2020 year can be predicted.

33In a period of 21 years (1979-2000) the mangrove of Sontecomapan had lost an area of 364 ha which correspond to a 17.3 ha/year loss rate; the total mangrove coverage for this last year was of 880 ha. Digital classifications were made to obtain maps showing the mangrove’s spatial distribution in the zone, and the proposed classes used seem to be representative for the analysis. Observing the spatial patterns of mangrove change, it is possible to report the different causes and processes that have generated its degradation, fragmentation and loss. Thus, for the periods 1979-1986-2000, the classes that increased were the Agricultural Zone (including cattle growth) and the Urban Zone, indicative of the anthropic influence, while the trend for coverage area was negative, that is loss for the classes Other Type of Coverage or Use and Mangrove forest. These results indicated a disturbance in the study area whose causes obey, as expected, to the great economic activity of this lagoon that has been transforming the landscape in the last 21 years. Despite the satellite images limitations used in this work, coherent results were obtained and were confirmed in the field trip. Krause et al. (2004), in a similar change of land use study in Brazil, a loss rate of 17 ha/year-1 of mangrove was computed; they found that agricultural and cattle growth classes were the main cause of this loss. They also pointed that results are useful elements for making territorial decisions in their area, in addition to show the geographical distribution of mangroves and other types of vegetation, and establish the basis for a permanent monitoring of the mangrove.

34The deforestation rate, as one of the most important indicators of the territory natural conditions, is very high. Pristine-condition areas, like forest soil and water, are reduced due to the anthropic influences which generate a transformation in the ecosystem, causing a high concern since the ecological services that the mangrove yields are diminishing. Due to its use value of $8,535 USD ha-1/year-1 (Pascal, 2014), ecological services (or environmental services) like protection of the shrimp larvae or fish alevins that sustain fisheries, and coast protection from storms, the total economic loss for this 21-year period due to the 17ha/year loss is about $3,053,225 USD.

Acknowledges

35This work was carried out thanks to the ECOS-ANUIES-CONACYT M10-A01 agreement between France and Mexico, and the “Diagnostic evaluation of the planktonic microbial circuit structure and the water dynamics of the Sontecomapan Lagoon, Veracruz” project (Agreements of the General Rector 12/008). Drawings by Samantha Cruz.

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Table des illustrations

Titre Figure 1. Sontecomapan Lagoon System, Veracruz, Mexico. The lagoon is located in the Southeast coast of the Gulf of Mexico, North America.
URL http://books.openedition.org/irdeditions/docannexe/image/35589/img-1.jpg
Fichier image/jpeg, 32k
Titre Table 1. Characteristics of the Landsat® satellite images used in this work. Image from year 1979 is a MSS, image from 1986 is a TM y and the 2000 image is an ETM+ (from LANSAT, 2010)
URL http://books.openedition.org/irdeditions/docannexe/image/35589/img-2.jpg
Fichier image/jpeg, 56k
Titre Table 4. Class contributions of coverage, in hectares (ha), to the net change expressed by class for the intermediate dates of 1979-1986
URL http://books.openedition.org/irdeditions/docannexe/image/35589/img-3.jpg
Fichier image/jpeg, 30k
Titre Table 6. Contributions, in hectares (ha), to the net change in coverage expressed by class for the intermediate dates of 1986-2000
URL http://books.openedition.org/irdeditions/docannexe/image/35589/img-4.jpg
Fichier image/jpeg, 30k
Titre Table 8. Coverage contributions, in hectares (ha), to the net change in coverage expressed by class for the extreme dates from 1979 to 2000
URL http://books.openedition.org/irdeditions/docannexe/image/35589/img-5.jpg
Fichier image/jpeg, 29k
Titre Table 9. Total coverage, in hectares (ha), of each class in each year. The differences between the evaluated years are also shown
URL http://books.openedition.org/irdeditions/docannexe/image/35589/img-6.jpg
Fichier image/jpeg, 41k
Titre Figure 2. Class coverage change around Sontecomapan, Veracruz, through the three recorded images dates (1979, 1986, 2000).
URL http://books.openedition.org/irdeditions/docannexe/image/35589/img-7.jpg
Fichier image/jpeg, 70k

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