Duncan Golicher’s weblog

El archivo arriba es un gif animado. Haz clic en el mapa para ver la animación, o guardarlo localmente. Nota que los tonos desde amarillo a azul muestra la pluviosidad sobrepuesto.

Our ongoing work with Conservation International has led to the first regional forest cover change map that has been derived using a single consistent methodology. The details of the study have not yet been published. However as we work on validation and interpretation of the results a pattern has become very clear.

Previous deforestation studies in Chiapas have usually concentrated attention on well defined study areas. This could produce the impression that deforestation is a homogeneous process over the whole state. In fact study areas selected for deforestation analysis are usually those with the highest rates when compared to the rest of the region. This is quite natural. Many of the areas where deforestation is no longer occurring have already lost a large proportion of their forest cover. However the overall regional deforestation we have quantified is considerably lower than previous studies imply.

Where deforestation has followed the classic pattern of forest conversion to permanent agriculture or pasture the CI methodology, which is based on Landsat imagery, has provided a remarkably good match with high resolution imagery. However where chronic, low level forest disturbance takes place the overall impact of human activities are much less easily quantified at the resolution of Landsat imagery. The difficulty in accurately evaluating forest cover change increases in areas of dry forest. Nevertheless regional patterns are robust.

The clearest deforestation hotspot in the state of Chiapas remains the Marques de Comillas area in the Southern Lacandon. Deforestation and carbon sequestration in this area has previously been studied in some detail by De Jong et al 2000

The two images below are animated gif files which change when clicked on to enlarge them to full size. The show clearly how Landsat based deforestation analysis coincides in this area with the conclusions drawn from visual analysis of recent high resolution imagery. The visual analysis is produced by overlaying our change analysis in Google Earth using Geoserver, and through the use of QGis.

A very common operation in GIS involves overlaying vector polygons on a raster map and calculating the area of each class in the raster map. The results are then added to the attribute table of the vector map. How can this be achieved in PostGIS?

If the raster layer is quite small it could be imported as points and the result achieved through a point in polygon SQL overlay. However that is not the usual way to achieve the operation. When large raster layers derived from the analysis of satelite imagery are of interest the conventional GIS approach  is to first rasterise the vector layer. The computations involved in the overlay are then simple and fast.

This example taken from our ongoing work. It overlays a map of agricultural units on a raster layer representing deforestation in the last decade derived from analysis of satelite data. Because deforestation in Chiapas has tended to take place on a rather small scale involving only a few hectares at a time, it is not at all easy to see the large scale regional pattern from an image. Calculating the proportion of the total area of each agricultural unit deforested helps to see the pattern. The map above shows the percentage of the total (forested + non-forested) area of each agricultural unit estimated to have been deforested between 1990 and 2000.

The simplest way to achieve this result with PostGIS is to import the vector layer into GRASS, where the raster is also held. There are a few issues that have to be addressed when doing this. The first is that the gid index is lost when the attributes are imported. As a unique identifier for each polygon is needed during the overlay an index should be added to the table first if the gid is the only unique identifier for the table. This is very simple.

ALTER TABLE chiapas.agros ADD COLUMN id int8;
UPDATE chiapas.agros SET id=gid;

Now enter GRASS in a location with the same projection as the PostGIS data base (typically EPSG:4326)

Now there is another technical issue. Clearly vector processing in any GIS involves a combination of the geometery and attribute tables. We have both already in PostGIS. However in GRASS we use  functions that work on GRASS’s own representation of the geometry, so an import is needed. GRASS can use a wide variety of databases as a back end for storing attribute data. Among these is Postgresql. What is not obvious, and  potentially quite confusing, is that we already have the data in a Postgresql data base! So, does it all need to be imported again? The answer seems to be yes and no. It is quite possible to link the GRASS topology with the original table. However it is much easier to import it all in the usual way using v.in.ogr. This produces a new table, that could either stored in an external data base or perhaps even within the original PostGIS data base (as a new copy). Of course this attribute only table lacks the geometry column.

For example to set up GRASS to use the original local PostGIS data base as the backend for vector layers

db.connect driver=pg database=”host=localhost,dbname=gisdb2″
db.login user=Duncan pass=******

Note that this step is not necessary. By default Grass will store the table as in dbf format.

A layer can be imported from PostGIS using

v.in.ogr dsn=”PG:host=localhost dbname=gisdb2 user=Duncan” layer=chiapas.agros output=agros type=boundary,centroid

If the raster layer is in another coordinate system, as it is in this case, it will be necessary to exit the location and reproject in the location of the raster layer.

v.proj input=agros location=Global mapset=mesoanalysis output=agros

Then form a raster layer.

v.to.rast input=agros output=agros use=attr column=id layer=1 value=1

Then in GRASS the overlay is achieved quite simply using r.stats and sending the output to a text file.

r.stats -a input=agros,defor >agros.txt

This forms a file in which the first column holds the id of the vector layer, the second the class in the raster layer and the third the area in square meters of each combination. For example…

1 111 1319094.000000
1 112 159201.000000
1 122 1041304.500000
1 211 4026323.250000
1 222 85285437.750000
1 244 89347.500000
1 422 52796.250000
1 444 1749586.500000
2 111 565326.000000

So the task is now done, apart from the job of adding the data back into the original PostGIS table. There are many ways to do this. Often only one, or a few focal classes are of sufficient interest to merit a new column. So an easy way to process the output and send it back to PostGIS involves R.

Connect to the PostGIS data base using RODBC and set the schema

library(RODBC)
con<-odbcConnect(”mydb”)
odbcQuery(con,”SET search_path =chiapas, pg_catalog;”)

Read in the data and set the first column as numeric, removing any missing data (by default it will probably have been interpreted as a factor as GRASS will use a * for missing data).

d<-read.table(”agros.txt”)

d$V1<-as.numeric(as.character(d$V1))
d<-na.omit(d)

Now a subset of the data can be taken for each of the columns of interest, named and added back to the data base. In this case code “111″ represents area that was classified as forest in 1990,2000 and 2005. “122″ pixels that were deforested in the period between 1990 and 2000.

odbcQuery(con,”ALTER TABLE agros ADD COLUMN forest int4;”)
d2<-subset(d,d$V2==111)
d2<-d2[,-2]
names(d2)<-c(”gid”,”forest”)
d2$forest<-round(d2$forest/10000,0)
sqlUpdate(con,d2,”agros”)

odbcQuery(con,”ALTER TABLE agros ADD COLUMN defor2000 int4;”)
d2<-subset(d,d$V2==122)
d2<-d2[,-2]
names(d2)<-c(”gid”,”defor2000″)
d2$defor90<-round(d2$defor2000/10000,0)
sqlUpdate(con,d2,”agros”)

We are rather concerned that this cover change analysis, that has been achieved with the standard methodology used by Conservation International, tends to quite seriously underestimate small scale disturbance and canopy opening. Nevertheless in comparative terms it does demonstrate that absolute deforestation in Chiapas is certainly not a homogeneous process. Extensive deforestation is not occurring, although if the map is zoomed out further to show the Marques de Comillas area a very clear hotspot of deforestation is shown. In this part of Chiapas (Central Valley, Sierra and Altos) comparatively few agricultural units have deforested more than 2% of their total area in the last decade.

The other way of looking at this would be as the proportion of remaining forest lost. This woud tend to emphasise deforestation rates in ejidos and agricultural units with a very small ammount of forest in 1990. However at this level the approach would be somewhat unfair as it would be biased by classification errors in small areas.

To put the map in context a map with the proportion of the total area classified as forest can be used. In R the column can be added using

odbcQuery(con,”ALTER TABLE agros ADD COLUMN propforest float;”)

odbcQuery(con,”update agros set propforest=100*forest/hectares;”)

A surprising number of the agricultural units have over 40% tree cover. This has to be placed in the context of a classification method that draws no distinction between a pixel within a pasture with sufficient cover of secondary vegetation to be classified as forest and untouched primary forest. The next step clearly involves analysis of fragmentation in order to clarify this.

While backing up some old information on my hard drive I rediscovered a document I produced for Pronatura at the end of November 2003. It lies somewhere between the status of informal field notes and a formal technical report. I presented it to Pronatura a week after a field trip in a light plane. The idea of the document was to record the discussion during the flight.

This was very much at the end of the “pre-google earth” era. Spatial analysis was still rather more of a specialised activity than it is today and I had only begun to become interested in the subject myself. My previous research was generally non-spatial.

Now a “fly over” of the region can be achieved rather more comfortably and safely with a laptop than a four seater plane. The experience was stomach churning at times with a few moments of sheer terror as the turbulence caused by thermals rising from the central depression hit.

Despite the fact that some of the analytical methods used were hurriedly applied and could certainly be refined, I still generally agree with most of the conclusions in the document.So I have placed it here in case it could be useful as a general introduction to the region. It is not directly citable, but similar statements to those included in the document have been made in peer reviewed work I have published together with Luis Cayuela, although there have been very slight differences in emphasis between my interpretation of patterns and causes of deforestation in the region and those adopted by Luis Cayuela and some other colleagues.

Here is the document in PDF form ….

reflections-after-a-flight-over-the-altos-of-chiapas

One of my first posts to this weblog mentioned the statistical correlations between low mid pacific sea surface temperatures and early rainfall in Chiapas.

http://duncanjg.wordpress.com/2008/02/06/checking-on-the-el-nino-effect-using-r/

Heavy rains often do not begin in the state until mid May. However this weekend we have experienced a major rain storm (12 April). Persistent rain fell throughout the night and early morning (14 April). Rerunning the code shows that la Niña effect is still strong.

Of course as any linkages are statistical in nature I do not expect my temporary success at long term weather forecasting to continue.

The animation above first shows an accessibility index calculated as a cost surface with the entrance to the watershed as a starting point. There is an excellent “How To” on calculating cost surfaces in GRASS available here. The cost surface (warm colours accessible, cold colours inaccessible) is followed by an animation of deforestation estimated by supervised classification of a series of landsat images taken in1975, 1987, 1999 and 2003. The area is a focus case study, the Tablon watershed in the Sepaltura biosphere reserve. The set of images in Google Earth format are available here.

Landsat imagery is the most frequently used for tracing patterns of deforestation over time. Landsat has been acquiring coverage of the Earth’s surface since 1972 when Landsat 1 was launched. Since then, four other satellites have been in operation. Landsat 1, 2, and 3 flew in a circular orbit 913 kilometers (570 miles) above the Earth’s surface and circled the Earth every 103 minutes (14 times a day). Landsat 4 and 5 fly about 705 kilometers (440 miles) above the Earth and circle every 98 minutes.

Landsat 4 and 5 are still operating. Landsat 6 was launched in 1993. Thus the sensors used to provide images have changed over time and are not easily comparable. The current Thematic Mapper (TM) class of Landsat sensors began with Landsat-4 (1982). This series continued with the nearly identical sensor on Landsat-5 (1984). Landsat-7 Enhanced Thematic Mapper Plus (ETM+) was carried into orbit in 1999. Currently, both the Landsat-5 TM and the Landsat-7 ETM+ are operational and providing data. Landsat-7 ETM+ experienced a failure of its Scan Line Corrector mechanism in May 2003. Data products have been developed to fill these gaps using other ETM+ scenes.

The images used for this analysis were downloaded from the global land cover facility and processed in GRASS. We are currently engaged in a classifying deforestation over a much larger area using similar imagery. Quantifying patterns of deforestation from these historical satellite images is never straightforward, for many reasons. Differences in the conditions when the images where taken can lead to misclassification of pixels leading to errors when documenting the overall spatial pattern. However even though it can be difficult to tell with certainty if a specific small area has changed, the general regional trends detected are quite reliable.

Recently high resolution images of this particular study region have become available and we are acquiring more. It is encouraging to find that at least the classification into “forest-non-forest” that has been achieved using Landsat imagery provides a very acceptable match with the visual impression provided by high resolution images. This can be seen below. You can check your own visual impression of the match by downloading the KML files and changing the transparency in Google Earth. The following lines load the layers into R.

urlstring<-”ftp://anonymous@200.23.34.16/simulacion/Datos”
a<-paste(urlstring,”tablondefor.rob”,sep=”/”)
load(url(a))

Vireos are quite difficult to photograph and identify and this is not a great photo. They are small, active birds that don’t remain in one place for long. This individual seems to be the plumbeous form of the solitary viero Vireo solitarius plumbeus, sometimes assumed to be a distinct species Vireo plumbeus. The photograph was taken in the grounds of Ecosur. The species is a winter migrant

The similar Cassin’s vireo is a resident of Baja California but winters north of the Isthmus. The identity of the birds in San Cristobal requires checking.

Un elemento de la vida en Chiapas que nunca puedo aceptar es ineficiencia, falta de logica y la ausencia de sentido común.

El gobierno del estado implementa multiples programas de subsidios y beneficios sociales para enfrentar la pobreza. Uno de los mas destacado es la ayuda por parte del programa Amanecer para gente de “tercer edad”. No es mi intención comentar sobre esta programa en general. La población con mas de 65 años de edad merece respeto y ayuda economica. Es la forma de pago que no es logico. Esta mañana fui a hablar con la gente esperando para recibir su ayuda. El video dice todo …..

But, for English speakers I will explain briefly anyway. The state government of Chiapas has started a  program to provide a small pension to all residents over 65 years old. They receive 50 dollars per month. This is certainly not a great deal, but could be a useful supplement for the poorest residents given that a kilo of the staple tortillas still costs less than a dollar. However in order to receive this they have to wait over five hours in the tropical sun. I am at least twenty years younger than most of those in the queue, but I would probably faint on my feet if I tried to last that long. There is no point to this torture, as there are many other options available to pay the money. The simplest would be to open the desks throughout the month instead of a single day. The best would be to pay the money into a bank account and provide a cash point card.

The Golden Cheeked Warbler (Dendroica chrysoparia) is a migratory species of particular conservation concern. In 1990 only an estimated 2,200 to 4,600 birds remained in their Texas breeding sites. There are many internet sites with information regarding the species. A quick google provides ..

http://www.tpwd.state.tx.us/huntwild/wild/species/endang/animals/birds/gcw.phtml

http://animaldiversity.ummz.umich.edu/site/accounts/information/Dendroica_chrysoparia.html

http://www.tpwd.state.tx.us/huntwild/wild/species/gcw/

Some particularly good photographs of the bird are available from

http://www.greglasley.net/gcwarblr.html

The decline in the golden-cheeked warbler is due primarily to loss of mature Ashe juniper habitat in Texas. The expansion of the cities of Austin, San Antonio, and Waco has been reported as having an impact, which might suggest that unlike many warblers the species does not use the habitat provided by domestic gardens. This preference may also be shown in its overwintering areas. This could partially explain my frustrating failure to have ever seen this species.

I live surrounded by pine oak woodland that should be suitable over wintering habitat for the species. Both my garden and the trees and shrubs around my workplace are visited by flocks of warblers daily from September through to late March. There are two common species that are somewhat similar to the Dendroica chrysoparia. They are Townsend’s warbler, D. townsendii, an extremely common bird in the winter months, and the black throated green warbler, D. virens. The second species tends to overwinter at slightly lower altitudes and is more common in the dry forests of the central depression. The Blackburnian warbler (Dendroica fusca) is also fairly frequently seen in San Cristóbal and also has a superficial similarity to the Golden cheeked.

I have developed the habit of very carefully observing every bird I see in the hope of a definite sighting of D. chrysoparia in San Cristóbal. To date I have still not observed a single individual of D. chrysoparia after having checked many hundreds of similar looking birds. Below are some illustrations taken from Edwards’ field guide of the three species.

I am placing pictures taken in my garden on this site in order to show the difficulties involved in obtaining a clear, definitive ID of this species. All the photographs below are of D. townsendii.

(Click on the thumbnails to see a full size picture. THESE ARE CLEARLY NOT GOLDEN CHEEKED!)

I have a broad range of research interests focussed largely but not exclusively on tropical forest ecology. I am currently mainly concerned with the potential effects climate change on the distribution and abundance of tropical organisms. This on-going research involves developing and testing methods for effectively modelling tree species distributions at a regional scale.

My 2001 doctoral thesis entitled “The dynamics of disturbed Mexican Pine Oak Forest: A modelling approach” combined individual based computer modelling at the University of Edinburgh with field research in Chiapas in order to further understanding of the complex successional patterns of highland forests. Some of the main findings have been summarised in a recent book chapter (see publications)

In order to generalise understanding to a wider regional scale I became interested in the use of Geographical Information Systems, image classification and spatial modelling. This led to a productive, ongoing collaboration with my colleague and friend Luis Cayuela, currently employed at the University of Alcala, Madrid.

During the time I have spent in Mexico I have come to understand that ecological research in the tropics has to address many common challenges related to data quality and quantity. A theme to my work has thus been the use of contemporary computer modelling and statistics in order to extract inference from “difficult” data sets. This has led to an interest in the use of Bayesian methods in the search for more robust forms of inference under uncertainty.

I am responsible for a course the use of computer simulation as a research tool at post graduate level. I also gain a great deal of satisfaction from participating in more applied research and courses on Ecological Restoration and Conservation.