environmental impact assessment (north luzon segment 10 highway project)

Last semester, I took the EnE 280 class as an elective. This course tackles the step-by-step procedure of conducting an environmental impact assessment and preparing the associated reports, plus the several things to consider when implementing the same. For our Final Project, we had to conduct an EIA for a certain project and prepare the corresponding report.

Our group was assigned the North Luzon Segment 10 Highway Project. The proposed 5,684.70 meters toll operated highway project is categorized under Group II – Non-ECPs in Environmentally Critical Areas (ECAs) based on DAO 2003-30. It is further subcategorized into C.4.a Bridges and viaducts, new construction which requires an Initial Environment Examination Report (IEER) for the project length of ≥ 80 m but < 10.0 km. Using the ADB, JBIC and WB Environmental Categorization as reference, the proposed 5,684.70 meters toll operated project is categorized under Category B of all banking institution.

The North Luzon Segment 10 Highway Project traverses the Cities of Valenzuela, Malabon and Caloocan. Said cities are collectively known to be part of the CAMANAVA district in Metro Manila. The Road Project particularly passes through the southwestern section of Valenzuela, eastern portion of Malabon, and the southern part of Caloocan.

The Project Location

Based from our research, the Proponent for this Project is the Metro Pacific Tollways Corporation and it is actually in the early stage of implementation.

We were able to come up with a 63-page document containing the relevant Baseline Environment Characteristics, Impacts and Mitigation Measures, Environmental Management Plan and the Environmental Monitoring Plan. But I will not post it here since the document is very long and we may not be in the position to disclose further information regarding the Project. But if you have any questions regarding the process, I would be glad to help you. Just send me a note and I’ll try to answer your queries.

What I can say though is that the Project would indeed be beneficial. But several considerations should be made to ensure that adverse environmental impacts would be minimized if not totally eliminated. :)

sulking… again

i very well understand your situation, that you’re alone and perhaps sad…and im really sorry for bringing this up. but don’t you know im alone too? without you, im alone too.. and sad? i dont even know how happy really feels like…

all im saying is dont throw me out, at least not so easily…
i dont usually like being lied to, but im giving you the permission to do just that…

lie about missing me, or really wanting to talk to me, or worrying about me… lie if only to make me feel better. lie if you find it necessary.. lie and i’ll believe you, i love you just the same, just the same…

Study Plan

As I enter the second phase of my Graduate Studies and prepare for my Master’s Thesis, my adviser prepared this Study Plan for me:

2nd Sem 2010-2011 (completed)
- GmE 202 (Core Course)
- GmE 203 (Core Course)
- GmE 205 (Core Course)

Summer (not included in the Program of Study)
- CS 297 Technology Entrepreneurship

1st Sem 2011-2012 (enlisted)
- GmE 220 (Specialized Course)
- EnE 280 (Elective)
- IE 230 (Applied Math, Common Course)

2nd Sem 2011-2012
- GmE 222 (Specialized Course)
- GmE 210 (Applied Math (?) Common Course)
- GmE 300 (Thesis)

1st Sem 2012-13
- GmE 300 (Thesis)

With this plan, it is imperative that I finish my degree on time hahaha :)
Long shot, but let’s see… I

RS IN MONITORING CHANGES IN MINING ENVIRONMENT: A CASE STUDY IN DIZON OPEN PIT MINE, SAN MARCELINO, ZAMBALES

M. J. Villanueva

Department of Geodetic Engineering, University of the Philippines, Diliman, Quezon City 1100
villanueva.mylene@gmail.com

KEY WORDS: RS, image analysis, change detection, mining, mining monitoring

ABSTRACT:

This paper presents the application of various Remote Sensing (RS) techniques for monitoring changes in mining environment. The goal is to provide sound basis for planning and regulation of mining operations. Multi-temporal satellite data of the study area are obtained to facilitate the study. Said data are processed and analyzed in accordance to the standard procedures in RS. Relevant spatial information relating to physical changes in mining environment, particularly in land cover, are obtained from these data. The techniques applied in order to meet the objective of the study include visual interpretation and comparison, NDVI and NDVI change analysis, image differencing, and spectral classification. Sufficient information necessary for the characterization and evaluation of the environmental changes are derived from the implementation of these techniques. . In order to fully utilize the outputs from the said techniques, it is necessary to conduct further accuracy assessment measures and consider integration with other data and information. Upon analysis of the results, it is concluded that RS data and methods, alongside the conventional methods, can be utilized and is useful in planning, monitoring and assessing the impact of mining operations.

1. INTRODUCTION

1.1 Rationale
The Philippines is among the world’s richest in terms of the availability of mineral resources. The Mines and Geosciences Bureau (MGB) reports that the country has about 9 million hectares of mineralized land estimated to be worth $840B. Of this, only 1.4% are covered with mining claims. A vast area of about 8.6 million hectares is still waiting to be explored.

While part of the population argues that exploration of these mineral resources can contribute largely to boosting the economy, another part is more concerned on its long term effects and the apparent adversities to the surrounding environment. The Philippine government believes that mining and environmental protection can co-exist, and that modern and responsible mining does not destroy the environment but only alters it to another land use. The government requires that mining contractors institute an Environmental Protection and Enhancement Program before the mining operation starts in order to protect the environment (MGB, 2010).

The memories of the past mining accidents, however, raise concerns on the operationalization of several mining projects in different localities. This clouds the intention of the government to fully implement the mining revitalization program. There is a need to ensure that mining operations serve its purpose without causing severe damages to the surrounding environment.

It cannot be absolved that mining can eventually turn into an environmental risk. This subject has attracted the attention of many researchers for its great environmental impacts and the need to come up with mitigation measures to address the same. Mining could possibly have permanent or temporary affects on all the components of surrounding environment. The impacts of mining could vary in severity depending on mining state, the methods used, and the geological conditions. It may cause massive damage to landscapes and biological habitats. The mining operations could result in the reduction of forest cover, erosion of soil in a greater scale, pollution of air, water and land and reduction in biodiversity (Sarma, 2005).

Mining operations usually cover large parcels of land and consequently affects extensive areas. This causes difficulties in mapping mining activities, and evaluating the associated environmental impacts. Adding up to these difficulties are the cost and time needed to come-up with reliable and up-to-date information for monitoring and regulating the changes in the environment (Chitade & Katyar, 2010). Managing the impacts of mine operations to its surrounding environment “requires comprehensive assessment of the changes in environmental variables over both time and space”. This shall provide for the regulation of activities that has been found to be potentially damaging to the environment, and restoring the land that has already been degraded (Limpitlaw, 2006).

Monitoring and evaluation of mining operations and their impacts require observations of the area over a period of time in order to distinguish the natural changes apart from those brought about by human activities. Bounded by spatial and temporal domains, these tasks are better implemented through Remote Sensing (RS) application and the utilization of RS data. RS data are said to be “synoptic, repetitive, and multi-temporal in nature” thus filling the gaps in using the conventional methods for monitoring, evaluation and impact assessment (Chitade & Katyar, 2010).

1.2 Research Objectives
The research will address how RS can be utilized in the planning and regulation of mining operations, as a result of the general assessment of the environmental changes in a mining area over a given period of time. It specifically aims to implement various RS techniques for the characterization of environmental changes in mining areas. The analysis of results shall aid evaluation and assessment mining operations and consequently facilitate monitoring and regulation of said activity.

1.3 Scope and Limitations
The study deals with the implementation of common RS techniques to aid mining evaluation and assessment. Said RS techniques include the characterization of mining environment and changes with respect to land cover, general analysis of the normalized difference vegetation index (NDVI), change detection using visual overlay and image differencing, and classification based on spectral characteristics. The analysis will be based mostly on visual interpretation of the processed images and the results of algorithms applied. Numerical values used as basis for thresholding will be provided as may be applicable or necessary.

The study does not intend to provide the actual guidelines for monitoring and regulations but rather provide insights on how the results of the analysis can aid policy making.

2. STUDY AREA
The Dizon Porphyry Copper-Gold/Silver open pit mine (figure 1) is located in the eastern portion of San Marcelino, Zambales. It covers an approximate area of 19,200 hectares spanning from 14°15′ to 15°05′ N latitude and 120°12′ to 120°22′ E longitude. The open pit mine site is located south of Mount Pinatubo and north east of Subic, Olongapo. Surrounding natural drainages or river systems include the Sto. Tomas and Sta. Fe River fed by the Marella River and the Mapanuepe River in the NW. The former drains the SSW slope of Mt. Pinatubo, while the latter drains the catchment where the open pit is located (Cacdac, 1998).

Figure 1. Dizon (Copper-Gold/Silver) Open Pit Mine,
San Marcelino, Zambales

The mining exploration in the area started on year 1979. It is previously operated by the Benguet Corporation under a profit sharing agreement with the mine’s owner, the Dizon Mines. The actual mining operation/mineral extraction started on year 1988, with initial mining reserve estimated at 140 million tonnes containing 0.43% copper, 0.93g/t gold and 2.5g/t silver. The mine site had been badly affected by the eruption of Mount Pinatubo in 1991. This caused heavy damage on mine equipment and facilities. It had likewise been affected by typhoon and resulting landslide toward the mid 1990’s. These and the operator’s business status then prompted the phasing out of the Dizon Mines in 1994. Operations in the open pit mine stopped in 1997, leaving behind ore substances remaining in the pit floor. (Benguet Corporation, undated & Medusa Mining Limited, 2004)

Efforts for the rehabilitation of the open pit mine started on year 2004. This is beyond the timeframe covered by this study and is thus not considered.

3. METHODOLOGY
The methodology adopted to complete and consequently meet the objectives of the study is illustrated in Figure 2.

Figure 2. Process Flow

3.1 Data Gathering
Multi-temporal Landsat TM satellite data covering the study area are downloaded from the USGS Global Visualization Viewer (GLOVIS) website.

 Landsat 4 TM – L1T, Januray 25, 1989
 Landsat 5 TM – L1G, April 2, 1993
 Landsat 7 ETM – L1T, April 3, 2002

Landsat 5 MSS data acquired on year 1976 is also downloaded for reference.

Other data and information are collected for association and integration with the satellite data. These include among others topographic and land classification maps obtained from the National Mapping and Resource Information Authority (NAMRIA), and reports and documentations gathered from online resources. Google Earth ™ is likewise used as reference.

3.2 Pre-processing
The ENVI 4.7 image processing software is used to calibrate and process the satellite data. Radiometric calibration is performed using the data specific calibration utilities. The Digital Number (DN) values are first converted to reflectance values. Atmospheric correction is then performed to get the surface reflectance using the Quick Atmospheric Correction (QuAC) utility. This implements a method similar to dark subtraction.
To meet the requirements for change detection, the co-registration of the multi-temporal satellite data is verified using dynamic overlay. The subsets of images covering exactly the same geographic area are obtained for subsequent processing. Lastly, relative radiometric correction is performed using Multivariate Alteration Detection (MAD) and Automatic Relative Radiometric Normalization (Canty & Nielsen, 2008). This provides for the normalization of multi-temporal images with respect to a reference image, minimizing the effects of changes in atmospheric conditions and solar conditions in the process.

3.3 Data Processing
For the actual image processing, the Spectral Processing Exploitation and Analysis Resource (SPEAR) tools available in ENVI 4.7 is used mainly.

3.3.1 Visual Interpretation: This involves identification and characterization of features and feature changes through visual inspection of multi-temporal satellite images. The focus is particularly on the land cover features and the associated perceptible changes.

3.3.2 NDVI: The NDVI values for each satellite image are computed using the following formulae:

NDVI defines the relationship between spectral reflectance measurements of vegetation acquired in the near-infrared and red regions. (Son, 2009) This is useful in measuring and characterizing the amount, structure, and condition of vegetation.

3.3.3 Change Detection: The change detection algorithms used are the two-color multi view and image differencing. These are both included in the SPEAR Tools package of ENVI.

In the two-color multi view change detection, a composite image is created with one band from time 1 image loaded in the red band, and the same band from time 2 image loaded in the green and blue bands. The change areas or features are shown in red or cyan. These colors indicate potential changes in the study area (ENVI Workflow Tools Whitepaper, undated).

On the other hand, image differencing is done simply by subtracting the initial state image and the final state image. This is used to identify the change and no-change pixels in the multi-temporal images. The nature of change however cannot be explicitly defined (Lillesand, 2000).

Image differencing is likewise applied to the resulting NDVI images to characterize the extent of change with respect to vegetation and biomass.

3.3.4 Classification: The spectral angle mapper is used to classify pixels based on the similarity of the spectral characteristics. This is useful in identifying areas within the mine site where the same spectral responses as the open pit are recorded. This can be related to either the presence of mine tailings in the natural drainages or similarities in mineral compositions.

3.4 Data Analysis
The results of data processing are used as basis for determining the critical matters relating to monitoring and regulation of mining operations. These further serve as reference for the formulation of relevant course of actions to address some pressing concerns relating to the same.

4. DISCUSSION
4.1 Visual Interpretation of Satellite Images
One of the initial concerns in the conduct of this study is the availability of pre-mining operations satellite imageries. Ideally, the comparison and assessment shall be referred from a satellite image acquired before the mining operations have started. This will give a more comprehensive view and information on the changes of features through time. This is not possible, however, since most of the large-scale open-pit mining operations in the country have started either prior to the launch of earth observation satellites or during the era where only low resolution satellite data are available.

In the case of Dizon Mine, the earliest available satellite image is the Landsat MSS data acquired on year 1976 (figure 3a). This has a spatial resolution of 60 m and is comprised of only 4 bands. Mineral extraction in Dizon Mine had started in 1988 (late 1987 in some reports). The earliest image acquired which can be used for further processing and assessment is dated January 1989, a year after the start of extraction of minerals from the ground (figure 3b). Therefore, a pre-mining evaluation and assessment of the area cannot be facilitated. This limits the information on how the operationalization of the mining project caused abrupt changes in the environment. The only discernible change is the presence of the open pit in the 1989 image.

Figure 3.Early Landsat Images of Dizon Mine

Another consideration is the availability of satellite data acquired right after the closure of the mine site. The mine site was closed in 1997. Unfortunately, there are no available Landsat data for this date. To facilitate evaluation of the area during the course of mining operation, a satellite data acquired on an intermediate date is used, in this case the 1993 image, and another acquired years after the closing of mine site, a 2002 image. These images (figure 4) were used in the characterization of changes in the mining environment using different RS techniques.

Figure 4. Multi-temporal Satellite Image Dizon Mine
A number of change features are noticeable from the comparison of the multi-temporal images. The 1989 image is used as the initial or reference image. In the 1989 image, the Mapanuepe River is system is heavily silted by Lahar flows. The river channel extending towards the open pit has been flooded, the adjacent lake and the open pit itself have changed its physical appearance. The 2002 image shows further changes in the state of the open pit, and the adjacent lake. The flooded channel still exists but the visual appearance with respect to the 1993 image has not changed much.

4.2 NDVI Analysis
NDVI analysis is used to assess the health of vegetation in the area surrounding the open pit mine and the mining environment in general. The ENVI SPEAR tool is used in the assessment. The said tool enables the identification of the presence of vegetation and visualization of the level of vigor. NDVI images are derived from the radiometrically corrected and normalized multi-temporal images.

The value of pixels in NDVI images range from -1.0 to 1.0. Pixels having values close to -1.0 indicates area with no vegetation, while pixels with values close to 1.0 show area with dense or healthy vegetation. Through density slicing, areas with no, sparse, moderate and dense vegetation are delineated. The ranges used are based on the NDVI threshold defined in the SPEAR tool. The range of values for no vegetation is from -1.0 to 0.249, sparse vegetation is from 0.25 to 0.499, moderate vegetation is from 0.50 to 0.659, and dense vegetation is from 0.66 to 1. The results of density slicing provide information on the condition of vegetation within the mining environment (figure 5).

Figure 5. NDVI Analysis

In the 1989 image, the areas with no vegetation correspond to the path of the river system. The open pit in particular is surrounded by moderate vegetation with sparse areas of dense vegetation. This can be accounted from the bulk of construction activities, including establishment of mining equipment and facilities and excavation of the open pit, at the early stage of mining operations.

In the 1993 image, there is an increase in area with no vegetation which corresponds to the river channel flooded and affected by the lahar flow. In areas surrounding the open pit mine, however, there is a noticeable increase in NDVI values which indicate healthier vegetation. This phenomenon should be analyzed further to determine the cause and consequently contribute to the planning of mining operations. This can discount the idea that all mining operations badly affect the surrounding vegetation, or can prove that mining operators are indeed taking initiatives of protecting the environment by performing tasks assigned such as forest preservation or reforestation. The 2002 image show further increase in vegetation density.

NDVI difference images (figure 6) are computed to characterize the change between the initial and final state NDVI images. The color representations in the resulting difference classification image indicate the magnitude of the change between the two images. Shades of red indicate positive changes or an increase in brightness values, while shades of blue indicate negative change or decrease in brightness values. NDVI difference image of 1989 and 1993 (a) image shows relatively higher rate of decrease in brightness values while the other difference image shows relatively lower rate of increase and decrease in brightness values. Brightness values can be related to the spectral response of vegetation and thus can be used to analyze the effects to vegetation as well.

Figure 6. NDVI Difference

4.3 Change Detection
Two change detection algorithms are applied. First is the two-color multi view change detection where an image composite is created using the same band from two images. Change features are highlighted in the image composite. Objects which increased in brightness appear cyan in the composite image while those which decreased in brightness appear red. These color representations can be used to indicate potential areas of change but cannot give direct explanation on the nature of change.

In the resulting image composites (figure 7) specific areas of interest can be identified to serve as focus of the analysis. For instance, in the 1989-1993 color composite (top row) shows high degree of change in the open pit. This can be related to both the physical and geophysical changes in the open pit mine. Further processing and analysis may be necessary to prove the same. In the 1993-2002 color composite (bottom row), change which can be characterized as siltation of the large portion of the lake adjacent to the open pit is evident. Other change features can be analyzed using this method of change detection.

Figure 7. Two-Color Multi View Change Detection

The other method of change detection used is the image differencing. Similar to the NDVI image differencing, it simply produces a difference image where the resulting pixel values correspond to the difference of DN values for a particular band of time1 and time2 images. There are different ways of presenting the result of the difference image. First is through thresholding or density slicing. The range of values used to delineate various differences or degree of change is defined based on some level of analysis (figure 8 b). In this case, three ranges are assigned and default threshold values are applied. The ranges used are: 187 (red). Determining the appropriate threshold values are not part of the study. Same density slice ranges are applied in the 1989-1993 (top row) and 1993-2002 (bottom row) difference maps. This shows the rate and behaviour of change from one period to another.

Another way to present the result of image differencing is by defining change and no change pixels (c). Change pixels can either be positive (red) or negative (blue). This likewise shows the rate and behaviour of changes but does not give information on the nature and probable causes of change.

Figure 8. Image Differencing (Band 4)

Another way to present the result of image differencing is by defining change and no change pixels (column 3) foregoing the need to define threshold values. No change pixels correspond to zero difference in DN values. Change pixels can either be positive (red) or negative (blue). This likewise shows the rate and behaviour of changes but does not give information on the nature and probable causes of change.

Image differencing is able to provide the amount of change but further processing and analysis is needed to determine and qualify the nature of change. This can be useful in identifying critical areas of change within a mining environment to aid decision making for actual ground inspection and other measures for monitoring and evaluation.

4.4 Classification
The last RS technique applied is the classification using spectral angle mapper. This classification scheme takes into consideration the similarities in spectral characteristics of features in an image. Considering that the concern is on the effects of mining operation, determining areas with similar spectral characteristics as that of the open pit is useful in monitoring the spread of mine deposits, byproducts, tailings or wastes in the surrounding environment. The idea is that pixels with similar spectral characteristics have similar composition. The intention is to map out the areas where similar spectral responses as that of the open pit mine, are recorded.

In the 1989 image classification (figure 9a), the pit is represented as red pixels, while the walls are assigned the yellow pixels. Upon classification, traces of yellow pixels are found in the Mapanuepe River channels. This implies that the elements present in the walls are eroded to the river channel, while the elements in the pit are most likely contained. This can be an indicator in further research for determining the qualitative effects of mining operations to water systems and likewise to other units within the mining environment. The same classification regions are applied to the 1993 image (figure 9b). The resulting classified image shows change in spectral responses in the open pit and other areas particularly the river system. This can be due to the presence of lahar in the 1993 image. Change in water depth or perhaps composition is likewise notable in the lake adjacent to the open pit.

Figure 9. SAM Classification Image Comparison

The 1993 image is classified further by determining other classes which are not considered when the same classification regions are used. The resulting classified image (figure 10) shows similarities in some areas of the river channel and the open pit but this may be due to the presence of lahar and not from the mining operations alone.

Figure 10. 1993 SAM Classification Image

In this study, SAM classification is used to present pixel relationships based on spectral response for illustration alone. This shows how the said technique can be applied in monitoring of mining environment and other subsequent activities such as environmental impact assessment.

4.5 Matters for Consideration
In view of the results and discussions presented above, there are several matters to be considered in coming up with a sound monitoring scheme for mining operation to aid regulation, assessment and even site rehabilitation. It should be noted that the results of RS techniques may require further processing and analysis to serve the purpose. Said results may also need to be integrated with other data and information for its full utilization.

Identification of features with highest potential for change within a mining environment is also necessary. Among the most important components are soil, water, and vegetation. This shall facilitate the identification of threshold of what changes are acceptable and what are not. Alongside the identification of change features is the determination and consideration of possible causes. The changes may be induced by natural changes, hazards, human activities, and other operations not necessarily part of a mining project. Defining these changes will aid change analysis. Lastly, the need for accuracy assessment and ground validation should likewise be a major consideration. With the aid of RS data, the extent of ground validation can be more focused and minimized, but not totally eliminated.

5. CONCLUSION
Upon analysis of the results, it is concluded that RS data and methods, alongside the conventional methods, can be utilized and is useful in planning, monitoring and assessing the impact of mining operations. It can as well be employed in planning for the rehabilitation of mining area. In the absence of pre-mining data, analysis of changes in a mining environment over a period of time can provide insights on pre-condition of mining operations and consequently aid decision-making for monitoring and regulation. Among the RS methods or techniques which can be utilized or applied include the following:

• visual inspection using satellite images;
• NDVI analysis;
• change detection analysis; and
• classification based on spectral characteristics of features

Recommendations for further study include the consideration of other variables in the analysis such as surface mineralogy, air, water and soil quality, and other geophysical aspects. Parallel ground surveys or assessment is also recommended to further substantiate the results of image processing and analysis.

6. REFERENCES
Cacdac, J. (1998) Application of Change Detection Algorithms for Mine Environment. Retrieved on 15 February from http://www.gisdevelopment.net/aars/acrs/1998/ts9/ts9006b.asp

Canty, M. et. al (2004). Automatic Radiometric Normalization of Multitemporal satellite imagery. Remote Sensing of Environment 112 (2008) 1025–1036. Retrieved on 10 March 2011 from

http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/5362/pdf/imm5362.pdf

Chitade A.Z. and Katyar S.K. (2010). Impact Analysis of Open Cast Coal Mines on Land Use/Land Cover Using Remote Sensing and GIS Technique: A Case Study. International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7171-7176

Lillesand, T. and Kiefer, R. (2000). Remote Sensing and Image Interpretation. New York, USA, pp 470-583.

Limpitlaw, D. (2006). Use of Remotely Sensed Imagery and Methods for Mapping and Planning of Mine Wastes Facilities. Retrieved on January 5, 2011 from the Centre for Sustainability in Mining and Industry (CSMI) Retrieved on 15 February 2011 from http://www.csmi.co.za/l/papers/minewaste_SAIMM_colloquium_Limpitlaw.pdf

Sarma, K. (2005). Impact of Coal Mining on Vegetation.: A Case Study in Jaintia Hills District of Meghalaya, India. Retrieved on January 5, 2011 from the Faculty of Geo-information Science and Observation Website: http://www.itc.nl/library/papers_2005/msc/ereg/sarma.pdf

Son, T. et.al (2009). Land Cover Change Analysis Using Change Vector Analysis Method in Duy Tien District, Ha Nam Province in Vietnam. Retrieved on 10 March 2011 from http://www.fig.net/pub/vietnam/papers/ts01g/ts01g_son_etal_3666.pdf

Benguet Corporation. http://www/benguetcorp.com

Medusa Mining Limited, 2004. Report on Dizon Project. Accessed in 10 March 2011 from

http://202.66.146.82/listco/au/medusamining/announcement/a040602.pdf

Mines and Geosciences Bureau (MGB). Primers on Mining and Mining Operations. Accessed on March 2011. http://www.mgb.gov.ph

Yale Center for Earth Observation © 2010. Documentation on Radiometric Calibration. Retrieved on 10 March 2011 from

http://www.yale.edu/ceo/Documentation/ceo_faq.html

daddy

dear dy,

i love you.
i dont know what else to say.
i know that the past months have never been easy…
i know how much you struggle, and how up to now you still do.

but we’ll get through this… i know we will… hope, trust, love and Faith
never let go dy…

good things come to those who wait…
we have been waiting for quite sometime already… let’s wait some more

God never forsakes. hang on please…. I love you.

my :

GIS in Flood-Risk Management: A Case Study in Cainta, Rizal

GIS IN FLOOD-RISK MANAGEMENT:
THE CASE OF CAINTA, RIZAL

R.C. Gatchalian *, M.T. Pandan, E. Tamayo, M.J. Villanueva

Department of Geodetic Engineering, University of the Philippines, Diliman Quezon City, 1100 ronaldogatchalian@yahoo.com

KEY WORDS: Flood, GIS, hazard, mapping, risk-management

ABSTRACT:

This paper presents a flood-risk management plan developed for the Municipality of Cainta in Rizal Province. The plan is founded from existing local conditions such as location, topography and demography. Geographic data and information defining said conditions are gathered and processed using a GIS to facilitate identification of high-risk areas and consequently draw out a sound flood-risk management plan. The identification of high-risk areas are based on natural factors relating to hazard and exposure while the formulation of flood-risk management plan is based on the evaluation and analysis of these natural factors vis-à-vis the existing contingency plan prepared by the local government. The assessment is made on a per barangay or community level. Five out of the seven barangays in the Municipality have been found highly vulnerable to risks of flooding and its adversities. Corresponding flood-risk management strategies are defined to address this and other related concerns. As a final output, a comprehensive flood-risk management plan comprised of thematic maps is prepared. This can aid the local government officials in their planning activities relating to the community-level flood-risk management and disaster response. A GIS database comprised of geographic and social data is also available for use in other applications or in furthering this study.

1. INTRODUCTION

1.1 Rationale

Floods are considered as one of the most common disaster phenomena or natural hazards and probably even the most devastating of all (Guarin, et.al., 2004 and Hailin & Baoyin, 2009). Flood is a recurring natural phenomenon usually brought about by heavy rains. Other causes of flooding include overflowing of river, inflow of tides, storm surges and other indirect source such as seismic activities (PAG-ASA).

Over the years, problems associated with flooding incidents have greatly increased. The effects have spread to wider population and jurisdictions. These effects can be related to the physical and social changes in the environment particularly in the changes in landscape, land cover and land use, urbanization, increase in population density, illegal occupancy of unsafe land, and many others (Guarin, et. al, 2004 & Adeaga, 2008).

Several flooding incidents have been experienced in the Philippines. These are caused mainly by heavy rains brought about by tropical storms or typhoons passing each year. Mitigating flood incidents has been one of the major concerns in several cities and municipalities in the country. There is an urgent need to manage flooding incidents to minimize, if not totally eliminate, the losses and damages it brings.

Since flooding occurs at the local level, the local government units (LGUs) are keen in instituting measures to address the problems associated with it. However, in order to come up with improved plans and programs, the LGUs should be able to consider physical and social factors, integrate information from diverse sources, and make decisions which are location-specific. These can be facilitated through the use of Geographic Information System (GIS). GIS provides spatial visualization and analysis tools which are very useful in planning, strategizing, decision-making and plan execution.

1.2 Objectives of the Study

The study is aimed to develop a GIS-based flood-risk management plan for an LGU. Said plan shall aid the LGU officials in the planning and implementation of activities relating to flood-risk management and disaster response.
Among the specific objectives of this study is to (1) gather information related to flood-risk such as topography, geography, infrastructure and demography of the study area, and other relevant documentations; (2) conduct spatial analysis to determine high-risk or vulnerable areas and evacuation or emergency areas; (3) determine flood-risk management strategies for the identified high-risk areas and evaluate mitigation measures prepared by the local government; and (4) produce a comprehensive flood-risk management plan.

II. RELATED LITERATURE

In many researches, identifying the risks associated with flood incidences are based on several natural and social factors. The scope of study and level of assessment are influenced by the availability and type of data and information, thus the differences in models and methodologies used. The fields to which the results of studies should be applied are likewise a consideration.

The scope of such assessment is usually made on a provincial or regional level. Results of these can be applied to disaster prevention planning and economic analysis (Guarin et al, 2004), land use planning (Bapalu and Sinha, 2005) and development impact assessment (Rahman and Alkema, 2006).

In the study conducted by Fano (2009), the over-all Philippine Flood-Risk Index (P-FRIc) by province is determined by using five key indices with both natural and social factors as indicators. The indices, namely Hazard (H), Exposure(E),Vulnerability (V), Coping Capacity using Soft countermeasures (CS) and Hard countermeasures (CH) were used to compute for the P-FRIc using the formula

This flood risk assessment utilizes present time data and presented the structure of flood risk in a comparative and quantitative approach.

The calculated P-FRIc values were verified to be in agreement with past actual flood damage data and the methodology can be considered as a source of new knowledge for disaster managers.

III. DESCRIPTION OF STUDY AREA

The Municipality of Cainta (figure 1) is among the fourteen (14) municipalities/cities comprising the Rizal Province. This first-class urban municipality lies in the boundary of the country’s metropolis and serves as the secondary gateway to the rest of the Province.

Based on the 2007 National Census, the municipality has a total land area of 4,299 hectares and a population of 289,833 individuals. The municipality is comprised of seven (7) barangays.

Figure 1. Geographic Location of Cainta

The geographic location of the municipality and physical condition makes it generally prone to flooding and flashfloods. It is located in the Marikina Valley where elevation is significantly lower than the neighboring towns. Adding to this are the river and water systems surrounding the municipality including the Cainta River, Marikina River and the Floodway. It is further aggravated by the informal settlers occupying the banks of the waterways, and the siltation of rivers and streams.

On year 2009, Cainta is one of the municipalities/cities badly affected by the Tropical Storm Ketsana (local name Ondoy). According to the incident report prepared by the Municipal Disaster and Coordinating Council (MDCC), the rains brought about by the storm submerged 98% of Cainta in flood water with heights ranging from 3 to 10 feet. It has affected 45,000 families representing more or less 285,000 individuals. It has consequently damaged roads, buildings, facilities, properties and other infrastructures, and ultimately affected the livelihood of the residents of Cainta.

IV. MATERIALS AND METHODS

4.1 Materials

Both spatial and non-spatial data and information are used in this study. These are enumerated as follows:

a. Spatial Datasets
• Scanned Topographic Map of Cainta (2005)
o Sheet 3230 III 13 & 3230 III 18
o Scale: 1: 10,000
o Source: National Mapping and Resource Information Authority (NAMRIA)
• Vector Dataset derived from scanned maps
o comprised of points, line and polygon features
o In .dxf format
• Barangay Boundary Shapefiles
o based on Barangay Index Map
o Source: Cainta Assessor’s Office
• Cainta Tourist Map
o Source: Cainta Municipal Planning and Development Coordinating Office (MPDC)

b. Non-spatial Datasets
• Demographic Profile
o Source: NSCB – NSO, 2007
• Municipality of Cainta Socio-Economic Profile
o Source: MPDC
• Municipality of Cainta Annual Report for CY 2009
o Source: MPDC
• Municipality of Cainta Contingency Plan on Natural Disasters 2009
o Source: MPDC

The ArcGIS software is used to integrate, process and analyze the data and information gathered. Other spatial technology resources used are the AutoCAD 2004, Global Mapper 9, Google Earth ™ application, and other online street maps for conversion of data and validation of geographic information. Handheld GPS is also used in obtaining the geographic location of some points of interest within the study area.

4.2 Methods

The study has two major considerations. First is the identification of high-risk or vulnerable areas and second is the actual development of a flood-risk management plan. To achieve these, four major activities corresponding to the data and process flow (figure 2) are defined.

Figure 2. Data and Process Flow

4.2.1 Data Inventory/Collection

This refers to the search and collection of both spatial and non-spatial datasets from respective data sources for use in the processing and analysis. The initial goal is to gather as much information as possible regarding the natural and social factors which serve as indicators in defining the flood-risk index. Since most data are disaggregated only up to the municipal level and some are not readily available from the identified sources, majority of the indicators are disregarded. The data collection is instead focused on three major factors namely topography, geography and demography.

Most of the spatial datasets gathered are in digital format while the non-spatial datasets are in form of documentations, reports and map print-outs. Among the data collected are the geographic locations of some points of interest within the study area. These data are obtained through ground survey using handheld GPS. The complete list of data collected is already presented in the preceding section.

4.2.2 GIS Databanking

This incorporates data clean-up and conversion, selection and extraction of relevant data layers, omission of data which is not critical to the objectives of the study, and population of geographic database.

Raster data are georeferenced. Vector data are converted to acceptable GIS format and referred to the raster data. Individual shapefiles are generated for each layer. All relevant information derived from the non-spatial datasets are entered into the database to incorporate the same with the spatial data.

A total of seven data layers were produced including the base map, elevation, river and water systems, road, government offices, general services and schools. The first three layers are used in the identification of high-risk areas while the remaining are used in the development of the flood-risk management plan components.

4.2.3 GIS Processing/Analysis

This focuses on the implementation of vector and raster datasets operations to aid spatial analysis and formulation of outputs. There are two major stages in GIS processing and analysis. The first relates to the identification of high-risk areas where the base map, elevation, and rivers and water systems layers are used. The second relates to the development of the flood-risk management plan where the roads, government offices, general services, and school shapefiles are used in conjunction with the resulting risk map from the first stage.

Data are extracted first from the vector and raster datasets to form separate shapefiles or data layers as discussed under the GIS databanking. Raster operations are mostly applied in the determination of high risk-areas. The population data are incorporated in the base map and the resulting population density map is converted to raster; triangulated irregular network (TIN) is derived using the elevation spot heights; and Euclidean distances to rivers and water systems are defined. The datasets are reclassified using the natural breaks (jenks) method of classification. These are finally combined using weighted overlay function with equal influence setting to derive the flood-risk map.

On the other hand vector operations are used in the processing of the remaining shapefiles or data layers. Selection by attributes, buffering and creation of new data layers are applied to initial vector datasets in order to come up with or derive the component maps of the final flood-risk management plan.

The raster and vector operations applied in this study are available in and supported by the ArcGIS software. Using the results of said operations and the corresponding maps produced the analysis and formulation of conclusions is performed.

4.2.4 Production of Map

The final output of this study is a comprehensive flood-risk management plan. This plan is comprised of thematic maps resulting from the vector and raster data processing and spatial analysis.

V. DISCUSSION

5.1 High-Risk Area Identification

The flood-risk indicators used in GIS processing and analysis are based on natural and social factors specifically relating to hazard and exposure. Hazard is associated with slope or elevation and the proximity to rivers or water systems while exposure is associated with population and population density among others (Fano, 2010). Other indicators for hazard and exposure are disregarded since the data available correspond to the municipal level (i.e. annual rainfall, typhoon proneness, population growth, etc.). Other natural and social factors are also not considered because of the lack of data.

A risk-map (figure 3) is derived by overlaying three raster maps/datasets: the reclassified population density map, slope map, and river and waterways map. For all layers or datasets used, ten classes are created applying the natural breaks (Jenks) method of classification. In this method, class breaks are determined statistically. The value of 1 is assigned to the area with highest risk or most vulnerable to floods, while the value of 10 has the least risk. Expected high-risk areas are areas with high population density, near the river or waterways and have low elevation or slope. The final risk map is created by combining the layers using the weighted overlay function and applying the equal influence setting. The resulting map has a scale of 1 to 7, with the value of 1 corresponding to the area with highest-risk of flooding.

The high-risk barangays are identified from the resulting risk map. These include the barangays of San Andres, San Juan, San Roque, Sta. Rosa and Sto. Nino. Since the assessment done is for the barangay level, actual flood risk of specific sites or zones within a barangay are not considered. The analysis is based on the coverage of high-risk areas with respect to the total land area of a barangay. The risk is gauged using high or low criteria. Degrees of risk between the highest and lowest values are not qualified.

Figure 3. Flood-Risk Map

5.2 Flood-risk Management Plan

The municipal government through the Municipal Engineering Office has prepared a Contingency Plan on Natural Disasters. This incorporates the LGUs search and rescue execution plan, evacuation reports, mitigation plans, and early warning system. The spatial information derived in these plans and reports are incorporated in the working GIS layers and database. Particularly, the staging areas, critical routes, communication centers, evacuation centers, and areas for rehabilitation (i.e. dredging, improvements) are mapped out. Integrating this with the flood-risk map, four component maps were prepared for integration to the comprehensive flood-risk management plan.

First is the Search and Rescue Map (figure 4) which presents the five staging areas located in different points in the municipality. These staging areas serve as the center of search and rescue operations of the Municipal Disaster Coordinating Council. Overlaying the present staging areas on the flood-risk map shows that most of these areas have a risk of 3 or moderate.

It is further observed that most staging areas are located on the western portion of the municipality. This may have implications on the level of emergency response and services to the other areas or barangays which are relatively farther from the existing staging areas. Additional staging areas are thus needed to service the eastern parts of the municipality. The potential locations for said areas are identified through GIS. Locations selected are those near the major road and with moderate or low risk. These are likewise presented in the Search and Rescue Map.

Figure 4. Search and Rescue Map

The Communication Map (figure 5), on the other hand, presents the existing communication centers across the municipality. The communication map corresponds to the early warning system of the municipality. This covers information dissemination, pre-emptive evacuation, and actual operations during typhoon and flooding incidents. Assumptions were made to come up with an analysis of the LGUs early warning system. Buffers are computed to estimate the coverage or reach of each communication center.

Drawing out one-kilometer radius from each of the pre-identified communication centers is still not enough to cover or service the entire municipality. To address this, additional communication centers need to be set up. The suggested locations of which are identified through GIS. Correspondingly, equipment (megaphones) must be secured by the LGU for each center. Specific areas of concern must likewise be assigned and to each center to facilitate speedy information relay.

Figure 5. Communication Map

The Evacuation Map (figure 6) presents the distribution of the evacuation centers in the municipality. The evacuation centers used are mostly public schools and government offices. These are where relief good and other resources are lodged for distribution to the affected families. Important considerations should be given to the level of flood-risk in the area and proximity to road for access in selection and assignment of evacuation centers. Overlaying the present evacuation centers on the flood-risk map shows that these have risk of either 2 (high) or 3 (moderate).

The use of evacuation centers in high-risk area should be avoided. Additional evacuation centers must be identified in other parts of Cainta since all existing centers are concentrated in the Floodway area. GIS can be used to identify appropriate sites for additional evacuation centers. It can further aid the management of operations in terms of defining the accommodation capacity of evacuation centers and properly organizing relief distribution.

Figure 6. Evacuation Map

Lastly, the Mitigation Map (figure 7) presents the LGU’s plan to rehabilitate through dredging some portions of the river and water systems surrounding the area. Overlaying the river and water systems and the specific areas of interest subject to dredging to the flood-risk map confirms the need to perform such operation. Thus it is recommended that said operation be done immediately subject to evaluation by the office concerned.

In addition to the rehabilitation of river and water systems, the LGU should look at other existing physical factors such as presence of informal settlers and the drainage system. Information related to these can be incorporated to the existing GIS database to aid analysis and further improve the mitigation plan of the LGU.

Figure 7. Mitigation Map

All these maps including the flood-risk map are integrated to come up with an integrated Flood Risk Management Plan (figure 8). This shall serve as a tool for the local government officials in planning, strategizing, decision-making and executing plans on flood-related concerns. In addition, the LGU can likewise use the GIS database to come up with other disaster-risk management plans as may be needed, subject to additional data and information.

Figure 8. Flood-Risk Management Plan

The assessment results are validated by comparing it with the reports of the LGU on the flooding incident during the Tropical Storm Ketsana and personal accounts of the residents. The accounts on the actual events, the records of the number of families affected, and also of the infrastructures affected, show that the barangays of San Andres and San Juan are among the most affected. Water levels in some portions said barangays reached the highest range and are last to subside. Other barangays are also affected with varying degrees of damages incurred.

VI. CONCLUSION AND RECOMMENDATIONS

Upon evaluation and validation of the results of the study and the corresponding outputs produced, it can be concluded that GIS is indeed a valuable and robust tool for flood-risk management. This can be specifically used in the following:
• Consolidating spatial and non-spatial information related to floods;
• Analyzing spatial data and determining risk areas and appropriate evacuation/emergency areas;
• Querying potential mitigation measures; and
• Producing a comprehensive map to aid planning and decision-making especially in the local government level.

Recommendations for further study include the consideration of other natural and social factors in determining the flood-risk index and extending the flood-risk analysis to zonal or parcel level.

VII. BIBLIOGRAPHY

Adeaga, O. (2008). Flood Hazard Mapping and Risk Management in Part of Lagos N.E. 10th International Conference for Spatial Data Infrastructure St. Augustine, Trinidad. Retrieved from:
www.gsdi.org/gsdiconf/gsdi10/papers/TS13.3paper.pdf

Alkema D. and M.Z. Rahman (2006). Digital Surface Model Construction and Flood hazard Simulation for Development Plans in Naga City, Philippines. Retrieved from: www.gisdevelopment.net/application/natural_hazards/floods/mm037_1.htm

Bapalu, G.V. and R. Sinha (2005). GIS in Flood Hazard Mapping: a case study of Kosi River Basin, India. Retrieved from www.gisdevelopment.net/application/natural_hazards/floods/floods001.pf.htm.

Fano, J. (2010). Establishment of Flood Risk Index by Province Based on Natural and Social Factors. (Master’s Thesis). International Center for Water Hazard and Risk Management, Tsukuba, Japan.

Baoyin, H. and Hailin, Z. (2009). GIS-based Risk Evaluation for Regional Disaster. Journal of Environmental Technology and Engineering, Vol. 2 (3): 87-91 Retrieved from:

http://www.icss-edu.tw/Journals/jete/v2/no3/87.pdf

Guarin, G. P. et. al. (2004). Community-Based Flood Risk Assessment Using GIS for the Town of San Sebastian, Guatemala. University of Twente, Faculty of Geo-information Science and Earth Observation Electronic Library. Retrieved from:

http://www.itc.nl/library/Papers_2005/p_jrnl/vanwesten_com.pdf

National Statistics Coordination Board (2010). Municipality of Cainta Demography Retrieved from:

http://www.nscb.gov.ph/activestats/psgc/municipality.asp?muncode=045805000&regcode=04&provcode=58

Municipality of Cainta, Rizal (2009). Annual Report CY 2009. Municipal Government of Cainta, Rizal.

Municipality of Cainta, Rizal (2009). Contingency Plan on Natural Disasters. Municipal Government of Cainta, Rizal.

Municipality of Cainta, Rizal (2009). Socio-Economic Profile CY 2009. Municipal Government of Cainta, Rizal.

Philippine Atmospheric Geophysical and Astronomical Services Administration (2011).

http://pagasa.dost.gov.ph

- mjv 26/03/2011
GmE 203 Final Project

strife.defeat.pain

first evening of march. it’s raining

and i’m once again pouring my heart out. for so much pain i can’t contain. so much pain i almost cannot bear.
for the longest time we are being tried. for the longest time we are asked to wait. and up to now we are still waiting.
and i understand that it is not in our time but in His time. but just the same it still hurts.

i’m hurting now not because I don’t believe. nor because I am losing hope. hope is all we have. faith is all that keeps us alive.

i’m hurting because i can’t do anything to keep him from hurting too. i’m bleeding as bad as he is. but i’m willing to bleed twice as much if it will make him feel at ease.

it makes me question myself, how in the world has it come to this. it makes me wonder, how long still?

and why? is it too bad to ask why?

Sonnet XVII by Pablo Neruda

First read this in my English 11 class back in 2003. Quoting a friend, this is one of those things you wish you were able to write on your own…

I don’t love you as if you were the salt-rose, topaz
or arrow of carnations that propagate fire:
I love you as certain dark things are loved,
secretly, between the shadow and the soul.

I love you as the plant that doesn’t bloom and carries
hidden within itself the light of those flowers,
and thanks to your love, darkly in my body
lives the dense fragrance that rises from the earth.

I love you without knowing how, or when, or from where,
I love you simply, without problems or pride:
I love you in this way because I don’t know any other way of loving

but this, in which there is no I or you,
so intimate that your hand upon my chest is my hand,
so intimate that when I fall asleep it is your eyes that close.

shit!

i am so freakin mad!

furious, frustrated, angry, livid, irate, infuriated, fuming, annoyed, wrathful, outraged, ireful, choleric in all hateful sense of it.

i was dissed, judged, insulted, slapped, betrayed, belittled, blamed, almost killed.

what hurt most is that i love them still.

fuck love!

RS in Assessing the Impacts of Mining Operations to the Surrounding Environment

GmE 202 Final Project Proposal

Introduction

Mining can eventually turn into an environmental risk. This subject has attracted the attention of many researchers for its great environmental impacts and the need to come up with mitigation measures to address the same. Mining could possibly have permanent or temporary affects on all the components of surrounding environment. The impacts of mining could vary in severity depending on mining state, the methods used, and the geological conditions. It may cause massive damage to landscapes and biological habitats. The mining operations could result in the reduction of forest cover, erosion of soil in a greater scale, pollution of air, water and land and reduction in biodiversity (Sarma, 2005).

Mining operations usually cover large parcels of land and consequently affects extensive areas. This causes difficulties in mapping mining activities and evaluating the associated environmental impacts. Adding up to these difficulties are the cost and time needed to come-up with reliable and up-to-date information for monitoring and regulating the changes in the environment (Chitade & Katyar, 2010). Managing the impacts of mine operations to its surrounding environment “requires comprehensive assessment of the changes in environmental variables over both time and space”. This shall provide for the regulation of activities that has been found to be potentially damaging to the environment, and restoring the land that has already been degraded (Limpitlaw, 2006).

Monitoring and evaluation of mining operations and their impacts require observations of the area over a period of time in order to distinguish the natural changes apart from those brought about by human activities. Bounded by spatial and temporal domains, these tasks are better implemented through Remote Sensing (RS) application and the utilization of RS data. RS data are said to be “synoptic, repetitive, and multi-temporal in nature” thus filling the gaps in using the conventional methods for monitoring, evaluation and impact assessment (Chitade & Katyar, 2010).

Statement of the Problem and Research Questions

The research will address how RS can be utilized in the regulation of mining operations, as a result of a general assessment of its impact to the surrounding environment in a given area. The following research questions shall serve as guide in formulating solutions or responses to the problem identified:

1. What are the site variables or indicators to be examined and the assessment criteria to be applied in order to characterize the impacts of mining operations?
2. What satellite imagery can be utilized and what information can it provide in relation to the indicators and assessment criteria identified?
3. How can the rate of change in the surrounding environment over a period of time be qualified and quantified from RS data?
4. Similarly, how can the adverse and auspicious effects of mining operations be determined from RS data?
5. How can the findings, observations and analysis results be integrated and presented in a map with the RS data as base?
6. What measures or regulations, founded from RS applications, can be implemented for better management and administration of mining operations?

Objective of the Study

The main objective of the study is to devise (or adopt and modify) a methodology for the characterization and mapping of the impacts of mining operations to the surrounding environment in order to aid assessment and consequently facilitate monitoring and regulation of said activity. This particularly intends to describe, map, qualify, and quantify to some extent both the adverse and auspicious effects of mining operations in a specific area. It further aims to present a general view of the assessment as applied in a specific area of study.

Research Methodology

RS data particularly satellite imageries covering the selected study area, acquired at certain time interval, shall be obtained from reliable source/s. These imageries shall be subject to corrections, calibration, enhancement, and classification among others, applying the general RS methodologies and utilizing the available image processing software. Secondary data in forms of maps, records, and other information shall likewise be gathered for association with and integration to the RS data.

Site variables or indicators and assessment criteria shall be identified based on ideal and existing conditions. These shall serve as basis for the characterization of the impacts of mining operations. Other variations, findings and observations shall be derived from the processed data. These shall in turn be assessed with respect to the indicators and criteria identified.

The imageries classification and other information extracted from the data sources shall be spatially integrated and presented in map forms using available mapping or GIS software. Final assessment shall be made upon completion of data processing, analysis and integration.

Figure 01 – Process Flow

Table 01 – Gantt Chart.

—end—-

mjv/18/01/11