Study parallel to the Pacific Ocean. These currents

Study area

The study area corresponds to the hydrological region number 15 – Costa
de Jalisco located between 18° 49′ N, 20°30′ N Latitude and 103°50′ W, 105°50′
W Longitude and covers a total area of 12,967 km2. The study area
encompasses the pacific coast of the states of Jalisco (from the south of
Puerto Vallarta) and Colima (to the south of the Port of Manzanillo) (Figure 1). It has an annual precipitation of 1144 mm
with almost 80% of rainfall occurring between June and October, and a mean
annual surficial runoff of 3606 hm3 per year (INEGI, 2000). The highly heterogeneous landscape has promoted a
high diversity of flora and fauna, especially in the central part, where the
Chamela-Cuixmala Biosphere Reserve, a protected area of 131 km2 of
well-preserved tropical deciduous forest is located (Cotler
and Ortega-Larrocea 2006; Suazo-Ortuño et al. 2008; Avila-Cabadilla et al. 2012).
Most of the coast of Jalisco state is represented by tropical dry forest,
followed by evergreen forest and an extensive area that includes agricultural
fields and pastures (Sánchez-Asofeifa et al. 2009).

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The rivers Tomatlán, San Nicolás, Cuixmala, and
Purificación, are born in the Sierra de Cacoma and descend almost parallel to
the Pacific Ocean. These currents are underdeveloped due to the proximity of
the mountains to the coast. The Chacala river (also known as Cihuatlán or
Marabasco) serves as the boundary between Jalisco and Colima, and when it flows
into the Pacific forms the Barra de Navidad bay (INEGI,
2000).

 

Methods

Landscape changes

The landscape changes were analyzed from land use and land cover (LULC) maps
derived from the classification of satellite images acquired by the Landsat 5 Thematic
Mapper (from 1986), the Landsat 7 Enhanced Thematic Mapper Plus (from 2001) and
the Landsat 8 Operational Land Imager (from 2017) and obtained through the USGS
Global Visualization Viewer (http://glovis.usgs.gov/). All images were captured
during the dry season when the phenological characteristics of the main vegetation
types allow us a good discrimination. Three satellite images by date with
Path-Row: 29-47, 30-46, 30-47 were used to cover the study area.

The basin limit was downloaded from the Flow Simulator
in Drainage Basins (SIATL) scale 1:50,000 from the National Institute of
Statistics and Geography (INEGI, http://antares.inegi.org.mx/analisis/red_hidro/siatl/).
The study area was isolated from each scene by a mask produced by the
rasterization of the basin limits vector. All scenes were individually classified
by using K-means unsupervised classification algorithm run in IDRISI TerrSet
software (Eastman 2016). The classification
scheme involved the following LULC classes: Aquatic surfaces (AS), Evergreen
forest (EF), Tropical Dry Forest (TDF), Exposed soil(ES), Crops (CR), Saltmarsh
(SM), Mangrove(MN), Littoral (LI) and HS (Human settlements). This later LULC class
was hand digitized on-screen over false-color composites of satellite images by
date analyzed.

In order to estimate the accuracy of our more recent
thematic map, we conducted field verifications during 2016 and 2017 to obtains
numbers of verification points and evaluate the LULC map of 2017. Accuracy
assessment was accepted until it reached an 80% or higher in overall accuracy (Congalton and Green 2009).

 

Scenario modelling and
validation

LULC
scenarios were modeled using Cellular Automata (CA) and Markov chain analysis in
IDRISI TerrSet software from Clark Labs. In a first stage, we calculated a
transition probability matrix for the period 1986 – 2001 was calculated using
Markov chains with the LULC map of 1986 as the first land cover image and the map
of 2001 as the later land cover image. The transition areas and the conditional
probabilities created in the step previous were used in a Cellular Automata
analysis to predict the LULC in 2017. This prediction was used for model
validation. A second transition probability matrix was calculated using the LULC
maps of 2001 and 2017 and used to predict LULC in 2033. Finally, we calculated a
transition matrix for the whole period (1986 – 2017) and we used it to predict
the LULC in 2050. These transition probabilities matrices have the information about
the probability that each land cover category will change to every other
category (Eastman 2016;
Wang et al. 2016).
During the Markov chain model setting stage, the number of time periods to forward
projection was set accordingly the number of time periods between the first and
the second image for each period analyzed. In the Cellular Automata/Markov
Change prediction setting, the later land cover image used in the Markov Chain
analysis was used as the starting point for change simulation, a standard 5*5
contiguity filter was applied meanwhile the number of Cellular Automata
iterations it depended on the number of time periods for forward projection
specified in the Markov chain analysis (Takada
et al. 2010; Wang et al. 2018).

To quantify the predictive power of the model, we
compared the result simulation (2017) with the reference map (2017) using Kappa
variations (Pontius 2000): Kappa standard (Kstandard),
Kappa for no information (Kno), and Kappa for location (Klocation). For all of
the Kappa statistics, 0% indicates that the level of agreement is equal to the
agreement due to chance and 100% indicates perfect agreement. In comparing the
map of reality to the alternative map, Kno indicates the overall agreement.
Klocation indicates the extent to which the two maps agree in terms of location
of each category (Eastman 2016). We accepted the
model to make future projections only if the Kappa values were greater than 80%
(Araya and Cabral 2010).