Classification and modeling of trees outside forest in Central American landscapes by combining remotely sensed data and GIS [Elektronische Ressource] / vorgelegt von Bernal Herrera-Fernández

Bernalhf

Sujets

• Astronomie

Informations

• Publié par: albert-ludwigs-universitat_freiburg
• Publié le: 01 janvier 2003
• Nombre de lectures: 15
• Langue: English
• Poids de l'ouvrage: 5 Mo

Extrait

Classification and modeling of trees outside forest in
Central American landscapes by combining remotely
sensed data and GIS
Inaugural-Dissertation zur
Erlangung der Doktorwürde
der Forstwissenschaftlichen Fakultät
der Albert-Ludwigs-Universität
Freiburg i. Brsg.
vorgelegt von
Bernal Herrera-Fernández
aus Costa Rica
Freiburg im Breisgau
2003Acknowledgements
I wish to express my gratitude to Prof. Dr. Barbara Koch for accepting me as a graduate
student at the Department of Remote Sensing and Landscape Information Systems
(FELIS), and for supporting this research initiative. I also want to thank Dr. Matthias
Dees for his supervision, contributions, and support during my studies and work at
FELIS. I wish to thank Dr. Claus Peter Gross for commenting on an earlier draft of this
document. I am indebted to Prof. Dr. Christoph Kleinn, who offered me the opportunity
to work within the framework of TROF-Project, for contributing to the definition of the
subject of this dissertation, and for providing invaluable scientific feedback that improved
the quality of the research. Gracias Christoph.
Thanks to my colleagues at FELIS, especially to Felipe Guanaes Rego and Raymundo
Villavicencio, for their friendship, patient and support, and for the continuing discussions
on topics of remote sensing, GIS, forestry, geography, and otherwise. These
acknowledgments would be incomplete without thanking Teresa Méndez, Anka
Zimmerman, Christi Bianchini, Carolina Gebler, Verena Bushle, Guillermo Navarro,
Robinson Cruz, and Randolph Welte for their friendship and support during my stay in
the unforgettable city of Freiburg.
I am deeply grateful to Jessica Deis for help in editing English grammar and style, and to
Gernot Ramminger for the translation of the German executive summary.
I want to thank the scientists involved in the TROF project, especially to Tatjana Koukal
and Prof. Dr. Wegner Schneider (IVFL, Austria), who kindly provided the TOF
classification on IRS images. I extend my grateful to David Morales (CATIE, Costa
Rica), Guillermo Suazo (IHCAFE, Honduras), Tomas Pap-Vary, Jan Schultz, and Kai
Türk (FELIS, Germany). Vicente Watson (CCT, Costa Rica) for providing most of the
thematic information used in this research.
I would like to thank to German Academic Exchange Service (DAAD) that contributed a
scholarship and the European Community through TROF-Project (Project number
ERB3514PL973202) for providing most part of the raw data.
iiTo the memory of my father
…A never-ending source of inspiration
iiiBernal Herrera-Fernández
Department of Remote Sensing and Landscape Information Systems (FELIS)
Faculty of Forestry and Environment
University of Freiburg, Germany
Executive Summary
The present research was conceived and developed within the framework of the “Tree
Resources Outside the Forest” (TROF) project funded by the European Commission
(Project number ERB3514PL973202). Most of the raw data was provided for this
research initiative and some of the results obtained in the project were used with the
consent of the authors.
Study sites are located in Costa Rica and Honduras, both covering an area of 199,600ha.
Both areas are covered by an IRS 1-D panchromatic image with 5.8m spatial resolution.
In the case of Costa Rica, a smaller area of 127,500ha was also selected for investigation.
Twenty-three scanned aerial photos cover this area.
For the purposes of this study, TOF comprises as all trees outside the legal forest borders
comprising an area < 2ha. In this research, an algorithm was developed for TOF
extraction on digital aerial photos in a study area located in Costa Rica. Furthermore, the
effect of the biophysical and spatial covariables on the spatial distribution of TOF in the
two study sites selected. The effects of the spatial resolution at which TOF was extracted,
the effect of TOF absence event, and covariables scale on factors affecting TOF spatial
distribution were also assessed.
TOF information was extracted from scanned aerial photos 1:40,000 with 3m spatial
resolution. Firstly, a multi-resolution segmentation method was applied, and then the
classification was performed using an object-driven approach. The results of this
classification were compared with a TOF classification based on an IRS-1D panchromatic
image, and Landsat ETM+. A set of biophysical and spatial covariables was evaluated as
a potential determinants of the spatial distribution of TOF. All covariables were stored
ivand processed in a GIS and georeferenced to the respective national map projection
system. Explicit spatial models were constructed by using logistic regression techniques.
The multi-resolution segmentation method applied proved very efficient in extracting the
segments required for the classification of forest, non-forest, and TOF on 3m spatial
resolution scanned color aerial photos. TOF-land (i.e. land where is likely to observe
TOF) area estimated from aerial photos was 3709.77ha, representing 3% of the total
study area (127,500ha).
In the case of Costa Rica, the most important factors affecting TOF presence classified
using 3m spatial resolution images are the altitude above sea level, distance to nearest
settlement, and distance to nearest forest edge. The logistic model adjusted using TOF
classified on IRS image in the same study site, shows that the most important factors
affecting TOF are the mean annual rainfall, distance to nearest paved road, distance to
nearest human settlement, and distance to nearest forest edge. Distance to the nearest
paved road was the most important covariable determining TOF spatial distribution in
the study site of Costa Rica, accounting for 57.1% of the probability to observe TOF. In
the case of the study site located in Honduras, the most important factors affecting TOF
spatial distribution are the altitude, distance to nearest paved road, and distance to nearest
human settlement. The most important factors affecting TOF presence in each study
site, reflected by the logistic model, showed differences between study site not only in the
significant covariables selected, but also in its magnitude and sign.
The results indicate a low to moderate capacity of the logistic models to discriminate
between TOF presence/absence observations. In the case of Honduras, its value was
62.9%, while in the model fitted for the study site of Costa Rica it was 60.2%. The model
fitted using TOF classified on aerial photos showed the moderate discriminatory power
with a value of 68.1%. It should be borne in mind that there are many other factors
affecting the presence of TOF in a landscape that were not used available for this study
due to information restrictions. Some of these factors are, among others, land cover and
distance to markets. Furthermore, there are other factors affecting TOF that are more
difficult to quantify, such as the individual decision of farmers on where a tree should be
vplanted, which trees, for instance, growing on pasture should be cut and when these
activities should take place. An increase in the precision of some biophysical covariables
such as soil, altitude, slope, and annual rainfall could improve the results obtained here. It
is recommended that future research efforts in this area take into consideration these
types of factors as determinants of TOF spatial distribution.
The model-goodness-of fit of the logistic model, its predictive power, the most important
covariables determining TOF spatial distribution, and the structure of the spatial
autocorrelation were dependent on the spatial resolution at which TOF were extracted.
The capacity to discriminate TOF presence/absence observations resulted 8% higher in
the model fitted using TOF classified on aerial photos.
The results obtained support the hypothesis that the number of TOF absence events and
covariable observations in fact affects the logistic model. Such an effect was observed in
the number of covariables entering the models, and in the capacity to discriminate
between TOF absence and TOF presence observations. Although the magnitude of the
changes was different according to each study site, the trend in the variation was
approximately the same.
Spatial accuracy of the covariables used to fit a logistic model showed important effects
on statistics both in the univariable analysis and in the multivariable procedures. The
most important effect of the lack of spatial accuracy occurs in the final set of factors
entered in the TOF presence model. Fitting a model using the original covariables,
additional factors will be considered as determinants of TOF presence. This situation
could produce an overestimation or a underestimation of the relative importance of the
factors affecting TOF presence, thereby leading the model user to misinterpretations.
Therefore, for modeling purposes it is imperative to establish clear standards of data
collection, storage, and transformation in the establishment of a GIS set up.
The TOF spatial logistic model based on aerial photos is definitely the most precise, not
only for analysis of the most important factors affecting TOF, but also for its usefulness
in the prediction of TOF probability. The differences in terms of the most important
vicovariables when compared to the model fitted on an IRS model could