Application of geographic Information system and remote sensing in multiple criteria analysis to identify priority areas for biodiversity conservation in Vietnam

There has been an increasing need for methods to define priority areas for biodiversity conservation since the effectiveness of biodiversity conservation in protected areas planning depends on available resources (human resources and funds) for the conservation. The identification of priority areas requires the integration of biodiversity data together with social data on human pressures and responses. However, the deficit of comprehensive data and reliable methods are key challenges in zoning where the demand for conservation is most urgent and where the outcomes of conservation strategies can be maximized. In order to fill this gap, the environmental model Pressure–State–Response (PSR) was applied to suggest a set of criteria to identify priority areas for biodiversity conservation. The empirical data have been compiled from 185 respondents, categorizing into three main groups: Governmental Administration, Research Institutions, and Protected Areas in Vietnam, by using a well-designed questionnaire. Then, the Analytic Hierarchy Process (AHP) theory was used to identify the weight of all criteria. These results show that three main factors could identify the priority level for biodiversity conservation: Pressure, State, and Response, with weights of 41%, 26%, and 33%, respectively. Based on the three factors, seven criteria and 17 sub-criteria were developed to determine priority areas for biodiversity conservation. In addition, this study also indicates that the groups of Governmental Administration and Protected Areas put a focus on the “Pressure” factor while the group of Research Institutions emphasized the importance of the “Response” factor in the evaluation process. Then these suggested criteria were applied by integrating with Geographic Information System (GIS) and Remote Sensing (RS) to define priority areas for biodiversity conservation in a particular conservation area (Pu Luong-Cuc Phuong area) in Vietnam. The results also reveal the proportion of very high and high priority areas, accounting for 84.9%, 96%, and 65.9% for Cuc Phuong National Park, Pu Luong Nature Reserve, and Ngoc Son Ngo Luong Nature Reserve, respectively. Based on these results, recommendations were provided to apply the developed criteria for identifying priority areas for biodiversity conservation in Vietnam.:Acknowledgement I Abstract III Table of contents IV List of figures VI List of tables X Acronyms and Abbreviations XII Chapter 1. Introduction 1 1.1. Problem statement and motivation 1 1.2. Research objectives and questions 2 1.3. Study contribution 3 1.4. Thesis structure 6 Chapter 2. Literature review 8 2.1. Background information on Vietnam 8 2.2. Environmental Pressure-State-Response model 11 2.3. Defining criteria for biodiversity conservation 13 2.4. Application of GIS and RS for biodiversity conservation 16 Chapter 3. Research methodology 19 3.1. Study areas 19 3.2. Data collection 23 3.3. Analytic Hierarchy Process 25 3.4. Remote Sensing 27 3.5. Geography Information System 35 3.6. Climate change scenarios 40 Chapter 4. Establishment of criteria 42 4.1. Summary of responses 44 4.2. Statistic of pairwise comparison 46 4.3. Weights of criteria based on all respondents 48 4.4. Weights of criteria based on groups 60 Chapter 5. Application of Criteria 64 5.1. Mapping criteria 64 5.2. Synthesis of multiple criteria 144 Chapter 6. Conclusions and recommendations 158 6.1. Establishment of criteria 158 6.2. Application of criteria 161 6.3. Recommendations 165 References 167 Appendix I. Questionnaire 197 Appendix II. Establishment of criteria 207 Appendix III. Application of criteria 23

Application of artificial neural networks in modeling deforestation associated with new road infrastructure projects

Tropical deforestation is an ongoing process mainly caused by the construction of new roads, which, without proper environmental planning, contribute to biodiversity loss. Given that the artificial neural networks (ANNs) have the ability to capture nonlinear relationships, they were used to predict deforestation associated with new roads, such as the “Variante Porce” road and the “El Bagre-San Jacinto del Cauca” road in the department of Antioquia. ANN Training was carried out online using the back-propagation algorithm, part of the R software. The predictive capacity was evaluated using the area under the receiver operator characteristic curve (AUC). Also, a network that showed the best predictive capacity for the deforestation surface was generated for the baseline scenario and the simulated scenario incorporating the new roads. The comparison of scenarios suggested that new roads would increase the probability of deforestation for approximately 103.729 ha of forest.La deforestación tropical es un proceso continuo causado principalmente por la construcción de nuevas vías, las cuales sin una planificación ambiental adecuada contribuyen a la pérdida de biodiversidad. Dado que las redes neuronales artificiales (RNAs) tienen la capacidad de capturar relaciones no lineales, se utilizaron para predecir la deforestación asociada a nuevas vías, como la Variante Porce y la vía El Bagre-San Jacinto del Cauca, en el departamento de Antioquia. El entrenamiento de las RNAs se realizó en modo on line con el algoritmo de retropropagación, en el software R. La capacidad de predicción se evaluó con el área bajo la curva ROC (AUC) y con la red que presentó mejor capacidad predictiva se generó la superficie de deforestación para el escenario base y el escenario simulado incorporando las nuevas vías. La comparación de escenarios indica que las nuevas vías incrementarían la probabilidad de deforestación de aproximadamente 103.729 ha de bosque

Linking Land Use Changes to Policy Decisions: The Case of Northeastern Iran

Land use change is the most important cause of disturbances in the natural environment. It increases the severity of natural disasters such as floods, dust storms, etc. Moreover, it also leads to major unnatural events such as water, soil, and air pollution and land subsidence. Land use change can take many forms in different parts of the world. The vast majority of these changes are the result of erroneous and unscientific policies that may be beneficial in the short term, but have negative long-term impact on human societies and the environment. Wrong policies lead to erroneous and short-term development and, in the long run, irreversible socioeconomic and environmental challenges. In this chapter, the process of land use change, driving forces (political decisions, technological development, etc.), causes, and effects of changes were all considered in a socio-ecological system in northeastern Iran (As a representative of the hyper-arid, arid, and semiarid regions of Iran). The discussion is captured in a framework reflecting driving forces, pressures, state of affairs, responses, and impacts (DPSIR framework)

Inductive pattern-based land use/cover change models: A comparison of four software packages

International audienceLand use/cover change (LUCC), as an important factor in global change, is a topic that has recently received considerable attention in the prospective modeling domain. There are many approaches and software packages for modeling LUCC, many of them are empirical approaches based on past LUCC such as CLUES , DINAMICA EGO, CA_MARKOV and Land Change Modeler (both available in IDRISI). This study reviews the possibilities and the limits of these four modeling software packages. First, a revision of the methods and tools available for each model was performed, taking into account how the models carry out the different procedures involved in the modeling process: quantity of change estimate, change potential evaluation, spatial allocation of change, reproduction of temporal and spatial patterns, model evaluation and advanced modeling options. Additional considerations, such as flexibility and user friendliness were also taken into account. Then, the four models were applied to a virtual case study to illustrate the previous descriptions with a typical LUCC scenario that consists of four processes of change (conversion of forest to two different types of crops, crop abandonment and urban sprawl) that follow different spatial patterns and are conditioned by different drivers. The outputs were compared to assess the quantity of change estimates, the change potential and the simulated prospective maps. Finally, we discussed some basic criteria to define a " good " model

Pemodelan Spasial Perubahan Penggunaan Lahan di Taman Nasional Gunung Halimun Salak dan Daerah Penyangganya

Land use activities in Gunung Halimun Salak National Park (GHSNP) that does not comply with the zoning plan of GHSNP cause degradation, deforestation and decreasing GHSNP size, while land use activities intensively in the surrounding of GHSNP (buffer area) that does not comply with the spatial allocation plan may alter landscape configuration that influence ecological processes and biodiversity within national park. Predicting land use and land cover (LULC) change patterns in the future provides important information for identifying areas that vulnerable to changes. Multi-temporal remote sensing data was used to identify LULC, a multi-layer perceptron neural network with a Markov chain model (MLPNN-M) was used to predict LULC in 2025 and to analyze LULC trend, Overlaying analysis was used to analyze the consistency between LULC and spatial allocation regulation in 2025. The results show that LULC in GHSNP and its buffer area consist of prmary forests, secondary forests, mixture crops, plantations, settlements, agriculture, shrubs, and water. The primary forests, secondary forests, mixture crops, and agriculture were predicted to decrease while settlements, plantations and shrubs were predicted to increase. Land conversion trends into secondary forests, plantations, agriculture and shrubs that begin to show centralized patterns within and the boundaries of GHSNP need to be anticipated. In 2025, inconsistency between land use and GHSNP zonation is the existence of mixture crops, plantations, settlements and agriculture outside the special zone whereas inconsistency between land use and spatial allocation regulation is existence of plantations and agriculture in conservation forest, protection forest and production forest

Cadernos de geoprocessamento 10: como proceder na detecção de mudanças de uso e cobertura da terra.

- Mapear os padrões de uso e cober­tura da terra é essencial para o plane­jamento e a execução de ações que en­volvem a gestão do território. - Quantificar e monitorar as mudanças de uso e cobertura da terra são elementos-chave no estudo de mudanças globais. - Os resultados de uma análise de detecção de mudança fornecem subsí­dios para apoiar o planejamento estra­tégico de diretrizes que contemplem a gestão do território ao longo do tempo, especialmente o rural. - Apresenta procedimen­tos que podem ser replicados em trabalhos de monitoramento do território.bitstream/item/184715/1/CT-418-1616-final.pd

Routledge Handbook of Southeast Asian Development

This Handbook traces the uneven experiences that have accompanied development in Southeast Asia. The region is often considered to be a development success story; however, it is increasingly recognized that growth underpinning this development has been accompanied by patterns of inequality, violence, environmental degradation and cultural loss. In 30 chapters, written by established and emerging experts of the region, the Handbook examines development encounters through four thematic sections: • Approaching Southeast Asian development, • Institutions and economies of development, • People and development and • Environment and development. The authors draw from national or sub-national case studies to consider regional scale processes of development – tracing the uneven distribution of costs, risks and benefits. Core themes include the ongoing neoliberalization of development, issues of social and environmental justice and questions of agency and empowerment. This important reference work provides rich insights into the diverse impacts of current patterns of development and in doing so raises questions and challenges for realizing more equitable alternatives. It will be of value to students and scholars of Asian Studies, Development Studies, Human Geography, Political Ecology and Asian Politics

Modelos geománticos aplicados a la simulación de cambios de usos del suelo. Evaluación del potencial de cambio

En el marco de un proyecto de investigación, y como continuación de trabajos precedentes, se está llevando a cabo una labor de comparación de modelos de simulación de cambios de uso del suelo, con el objetivo de obtener conclusiones acerca de los principales avances temáticos y metodológicos que pueden extraerse de su utilización. Aquí se presentan los primeros resultados obtenidos en un área-test de la Región de Murcia de aproximadamente 2300 km² de extensión. Para la fase de calibración (t0 y t1) se han utilizado los mapas de usos del suelo del proyecto Corine Land Cover 1990 y 2000, y para la simulación de los resultados (T), el año 2006. Las variables descriptivas y explicativas utilizadas proceden de distintas fuentes y bases de datos oficiales, siendo transformadas como factores o restricciones de las categorías de ocupación del suelo y de las transiciones detectadas en la fase de calibración. Las herramientas TIGs utilizadas (Land Change Modeller, CA_MARKOV) están incluidas en el software IDRISI 16, versión Taïga. El objetivo es poder conocer las ventajas y limitaciones de estos modelos y comparar la especificidad de cada uno en la fase de calibración (estimación y localización de los cambios, métodos para transformación de variables).The presented results come from a framework of projects focussing on comparison of various geomatic simulation models in order to get information about their degree of generalization and land use / land cover changes (LUCC) to which they may be applied.Here we present first results from one test area located in Murcia region with an extent of 2300 km². Corine Land Cover maps of 1990 and 2000 were used for model calibration. The simulation was done for 2006 with the possibility to validate model outputs by Corine Land Cover data from the same year. Data about identified drivers for LUCC have different sources, in particular land planning agencies and the Department of Environment. These drivers were used as constraints and factors explaining the location of land use categories and transitions during the calibration process. The presented simulation results were operated with model functions available in Idrisi 32, Taïga: Land Change Modeler and CA_Markov. The aim of this work is to better understand advantages and limitations of applied models and, particularly the specificities of each one during the calibration process (estimation and localization of changes and methods to driver transformation)

Modelos geomaticos aplicados a la simulacion de cambios de usos del suelo. Evaluacion del potencial de cambio

En el marco de un proyecto de investigación, y como continuación de trabajos precedentes, se está llevando a cabo una labor de comparación de modelos de simulación de cambios de uso del suelo, con el objetivo de obtener conclusiones acerca de los principales avances temáticos y metodológicos que pueden extraerse de su utilización. Aquí se presentan los primeros resultados obtenidos en un área-test de la Región de Murcia de aproximadamente 2300 km² de extensión. Para la fase de calibración (t0 y t1) se han utilizado los mapas de usos del suelo del proyecto Corine Land Cover 1990 y 2000, y para la simulación de los resultados (T), el año 2006. Las variables descriptivas y explicativas utilizadas proceden de distintas fuentes y bases de datos oficiales, siendo transformadas como factores o restricciones de las categorías de ocupación del suelo y de las transiciones detectadas en la fase de calibración. Las herramientas TIGs utilizadas (Land Change Modeller, CA_MARKOV) están incluidas en el software IDRISI 16, versión Taïga. El objetivo es poder conocer las ventajas y limitaciones de estos modelos y comparar la especificidad de cada uno en la fase de calibración (estimación y localización de los cambios, métodos para transformación de variables)