Insights for Manage Geospatial Big Data in Ecosystem Monitoring

using Processing Chains and High Performance Computing

Fabián Santos

and Gunther Menz

Center for Remote Sensing of Land Surfaces, University of Bonn, Walter Flex Straße 3, Bonn, Germany

Remote Sensing Research Group, Department of Geography, University of Bonn,

Meckenheimer Allee 166, 53115 Bonn, Germany

1 RESEARCH PROBLEM

Big data (BD) is nowadays a research frontier and a

strategic technology trend, which is still emerging as

a new scientific paradigm in many fields (Chen and

Zang, 2014). It is commonly conceptualized by the

3V´s model of (Laney, 2009), who defines three

dimensions in BD known as: volume (data size),

variety (data types) and velocity (production rate);

that could be challenging to analyze, especially in

large quantities. For these reasons, processing and

analysis of BD requires new approaches, which are

not suitable for conventional software and hardware.

According to (Percival, 2009) Geospatial data

(GD) has always been BD but not as it is today, due

the accelerated increase and accessibility of

geographical technologies (as for example: state-of-

art earth observation satellites, mobile devices,

ocean-exploring robots, unmanned aerial vehicles,

etc.). Moreover, the distribution policies in favor of

free and open access to archives are giving way to

automated mass processing of large collections of

Geospatial data (Hansen and Loveland, 2012).

Thereby, this type of data can be considered (under

certain conditions), as a synonym of BD which

requires not only powerful processors, software,

algorithms and skilled data researchers (European

Commission, 2014) but also a set of conditions that

are not fully met to make possible and accessible the

data-intensive scientific discovery.

For these reasons, this research aims to explore

the link between Geospatial data and BD, especially

in the design and programming of processing chains,

which usually do not show explicit considerations to

manage the BD dimensions, moreover applied to

ecosystem monitoring research.

2 OUTLINE OF OBJECTIVES

Due that the BD and Geospatial data involves many

combinations; only two cases will be analyzed on

this research. The first case involves the analysis of a

large set of satellite images, so the volume

dimension of BD will be the main challenge. The

second case implies a unique large database of

different Geospatial data sources and types, thus the

variety dimension of BD will be the main problem.

For these reasons, this research is divided in two

empirical objectives and one theoretical, whose

purposes will be the following:

 Analyze the restoration process of disturbed

tropical forests in Ecuador. For this reason, a

processing chain for prepare a large collection of

Landsat images and a time series analysis will be

developed, applying the high performance

computing approach.

 Identify the environmental drivers that influence

the restoration process of disturbed tropical

forests in Ecuador. To this purpose, an

exploratory statistical analysis and pattern

extraction from a database composed by different

sources and types of Geospatial data will be

review. This will require the development of

another processing chain using the high

performance computing approach for harmonize

and extract the patterns inside the data.

 Describe the linkages of BD in Geospatial data

and the benefits of the high performance

computing approach in processing chains. Due

this reason, the processing chains already

developed will be debugged and optimized in

order to guarantee their reproducibility and

distribution as open source software. Moreover, a

detailed description of their design and

processing efficiency will allow the conclusion

of the technical aspects of this objective.

3 STATE OF THE ART

In the early seventies, the term “information

Santos F. and Menz G..

Insights for Manage Geospatial Big Data in Ecosystem Monitoring using Processing Chains and High Performance Computing.

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

overload” was mention by (Toffler, 1970) to explain

the difficulties associated with decision making due

the presence of excessive information. After that, a

concern for the management and interpretation of

large volumes of data became more relevant;

however, without a proper solution.

When computer systems were developed enough

for recognize or predict patterns on data (Denning,

1990), the scientific community was able to describe

with more details the properties of the BD. The first

known scientists who conceptualized the term were

(Cox and Ellsworth, 1997) and they described it as

the large data sets which exceed the capacities of

main memory, local disk, and even remote disk.

Consequently, the term was mainly associated with

the size or volume of the data but (Laney, 2001)

proposed two additional properties for describe BD

calling them: variety, for refer to the diversity of

data types and; velocity, for indicate the production

rate of data. This concept approach is called the

three V´s of BD and nowadays inspires most of the

BD management strategies. However, other authors

as (Assunção et al., 2013) suggest additional V´s

properties and considerations for BD management

calling them: veracity, value, visualization and

vulnerability.

Regarding to Geospatial data as BD, its use

constitutes a research frontier which is making

conventional processing and spatial data analysis

methods no longer viable. The increasingly data

collection and complexity of sensors aboard the

earth observation satellites and other technology

devices based in Geospatial data production is

nowadays demanding new platforms for processing,

which are now accessible through cloud computing

services (Sultan, 2010). However, the scientific

literature related to this field is not so numerous than

the research done over individual or small

collections of satellite images using conventional

computing methods. A decline on this tendency over

time is concluded by (Hansen et al., 2012), who

affirms that methods in the future will evolve and

adapt to greater data volumes and processing

capabilities; and (Gray, 2009) who anticipated a

revolution of scientific exploration based on data-

intensive and high-performance computing

resources.

The release by NASA and the USGS of a new

Landsat Data Distribution Policy (National

Geospatial Advisory Committee, 2012) which

enables the free download of the whole available

data collection constitutes an example of a data-

intensive source which demands new approaches for

extract meaningful information. In this sense,

(Potapov et al., 2012) demonstrated the feasibility to

work with large Landsat collections developing a

methodology which enable the quantification of

forest cover loss through the analysis of a set of

8,881 images and a decision tree change detection

model. Moreover, (Flood et al., 2013) proposed an

operational scheme for process a standardized

surface reflectance product for 45,000 Landsat

TM/ETM+ and 2,500 SPOT HRG scenes,

developing an innovative procedure for correct the

atmosphere, bidirectional reflectance and

topographic variability between scenes. However, in

both cases, is unknown the computing strategies

adopted for manage and process such large

collection of images.

Nonetheless, other authors describe with detail

the use of High Performance Computing and

Geospatial data. For instance, (Wang et al., 2011)

develop a prototype of a scientific Cloud computing

project applied in remote sensing, which describes

the requirements and organization of the resources

needed; (Almeer, 2012; Beyene, 2011) investigated

the MapReduce programming paradigm for process

large collection of images; and (Christophe et al.,

2010) describes some benefits of Graphical

processing units (GPU) respect to Multicore Central

Processing Units (CPU) on the processing time of

different algorithms types, commonly used in remote

sensing.

From all the references consulted, these two

approaches were mainly found, in other words,

separating the design of the remote sensing

processing chains from the BD management

strategies. For this reason, this research aims to

couple them on two specific cases of large

Geospatial data collections applied on Ecosystem

monitoring, which involve the design of processing

chains and BD management strategies.

4 METHODOLOGY

As is mention in the section 2, the empirical research

will be applied in two cases, therefore each research

objective has their own specific data sources,

analysis methods, validation procedures and study

areas (except for the third one which is mainly

theoretical). The materials and methods are

summarized in the next paragraphs (subsections 4.1

to 4.4):

 Data sources: multispectral and radar remote

sensing products, aerial photography, ancillary

cartography, climatic databases, GPS inventories,

field recognition and surveys.

CLOSER2015-DoctoralConsortium

 Data analysis: literature research, parallel

computing paradigm, image processing, machine

learning, time series analysis and exploratory

statistics

 Validation procedures: multi-scale accuracy

assessment

 Study areas: selected sites of different tropical

forest in Ecuador

The study areas considered for this research

represent sites which the available Geospatial data

achieves the minimum information requirements,

furthermore with a good register of field data and

fidelity needed for the validation procedures.

Finally, on this review only the first objective

materials and methods are described in detail due the

advances achieved until now.

4.1 Geospatial Data

For categorize the Geospatial data of the first

objective, two types are listed with their respective

sources:

 Primary sources (all in raster format):

1) A set of Landsat 4, 5, 7 and 8 images (±350

data sets collected) acquired for the

multispectral sensors TM, ETM+ and OLI-

TIRS (all with 7 spectral bands in the optical,

infrared and thermal regions) over 3 scenes

(each scene covers 33,300 km2); processed

until the level L1T (which means a geometric

correction but not a radiometric correction).

This information covers a period of ± 30

years with time intervals of 0.5 to 2 years;

and with a spatial resolution of 30 meters.

2) A set of digital elevation models obtained

from the Shuttle Radar Topography Mission

for the respective Landsat scenes, with a

correction of the data voids (however with a

fair quality). The spatial resolution of this

data is 30 meters

3) A set of very high resolution images from the

RapidEye satellites (5 meters of spatial

resolution with a geometric correction) and

from historical archives of aerial photography

(this information is under request).

 Secondary sources (raster and vector format):

4) Ancillary cartography, which describes the

ecosystems and the different forest types in

Ecuador; and other layers for describe the

human and biophysical features of landscapes

(roads, rivers, administrative boundaries,

cities, soil types, climates, etc.)

5) Climate databases from the WorldClim and

metereological stations.

6) GPS inventories of plant species and forest

carbon stock measures on the field.

4.1.1 Open Issues

Due that the processing capabilities are restricted to

a multicore computer; the raster data used for the

analysis is reduced to a set of small study areas

inside the image scenes. However, our interest is

extend the processing capabilities using a cloud

computing service for modify and upgrade the

processing chain developed with the complete area

of the images.

4.2 High Performance Computing

According to (Christophe et al., 2011) high

performance computing is a natural solution to

provide the computational power, which have

several approaches like cluster, grid or cloud

computing. Moreover, this approach not only refers

to a connected groups of computers locally or

geographically distributed; it refers as well to the

parallel computing paradigm which is the

simultaneous assign of tasks when is possible to

divide a big processing problem into smaller ones.

This can be done through the central processing

units (CPU) or the graphical processing units (GPU)

which nowadays computers have as hardware

resources.

The design of the processing chain for the

Landsat images applies the parallel processing

paradigm through the use of the cores in a multicore

computer and the division of the collection of

images acquired. For this purpose, an automatic

detection of the cores in the computer makes

possible to obtain the factor needed for subdivide the

complete list of images. This is done in the R

language through the package “foreach” (Weston,

2015) which allows the management of the cores in

a loop programming structure. The next R script

shows this design:

#list the data repository in the

variable “load.data” which is the

set of images

load.data <- list.files(load.data,

full.names= T)

#detect the number of cores

available in the computer

ncores <- detectCores(all.tests =

TRUE, logical = TRUE)

InsightsforManageGeospatialBigDatainEcosystemMonitoringusingProcessingChainsandHighPerformance

Computing

#subdivide the “load.data” list in

groups according to the cores

load.data <-

split(load.data,as.numeric(gl(length

(load.data),ncores,length(load.data)

)))

#for each element “j” in the

“load.data” subdivided list, take

only a set (which #represent a set

of 4 image directories when a

computer have 4 processing cores)

for (j in 1:length(load.data)){

data.set <- load.data[[j]]

#register cores for apply in

parallel

clusters <-makeCluster(ncores)

registerDoParallel(clusters)

#apply the parallel loop for

each element “i” inside the

variable #“data.set”. Due that

each core need to load an

environment, the command

# “package=raster” specify that

is needed this package to

execute the script

foreach(i=1:length(data.set),.packag

es="raster") %dopar% {

#here comes the script which

indicates the different

operations that should #be done

to each image folder, which is

indexed by the “i” element

inside #the variable “data.set”

…

#for close the parallel loop for

the “i” element

}

#for stop the cores and prepare

them for the next element of the

loop

stopCluster(cl)

#finally, for close the loop of the

subdivided list “load.data” another

curly #brackets is needed

}

This structure allowed the distribution and

application of complex algorithms over set of

images, instead individual images, decreasing the

processing time and according to (Zecena et al.,

2012) a more efficient energy consumption and

algorithm processing.

4.2.1 Open Issues

This approach leads to a further experimentation in a

cluster, grid or cloud computing environment as is

mentioned before, however is needed a feedback for

validate the data distribution between the cores when

they are parallelized, as well the management of

cores in a cloud computing service.

4.3 Image Processing

The image processing approach of this research

follows five modules, each one composed of

different sets of processing tasks which accomplish

specific objectives. This is showed in the figure 1,

which is a flowchart that summarizes the steps of the

processing chain developed, however not yet

finished.

The design of this approach is inspired by the

work of (Flood et al., 2013; Hansen et al., 2007;

Potapov et al., 2012) who developed processing

chains for large collections of Landsat images and;

agreed in almost all cases with the order of the next

required steps: 1) georectification/resampling; 2)

conversion to the top of atmosphere (TOA)

reflectance/atmospheric correction; 3)

cloud/shadow/water masking; 4) standardization of

the reflectance; 5) topographic and bi-directional

reflectance normalization; 6) radiometric validation;

7) index generation/image classification/change

detection/time series analysis; and 8) accuracy

assessment.

4.3.1 Open Issues

Some steps of the processing chain are missing due:

a fail in the processing algorithms used or an

absence of the algorithm in the R language packages

repository. This involves new challenges and in

some cases a change in the language used (as for

example the coregister script which had to be

programmed in Python). Due that our aim is produce

and distributes a pure R application for process large

collections of Landsat images, this can interrupt the

sequence of steps involved. Therefore, new

algorithms, libraries, software and package

alternatives are being searched, but with the only

requirement that they should be open source.

Respect to the accuracy assessment, the results of

the classifications of the images, will allow the

measurement of the precision through the use of

very high resolution satellite images and aerial

photography. This approach called multi-scale

accuracy has been proven for validate MODIS

CLOSER2015-DoctoralConsortium

Figure 1: Flowchart of the processing chain under development for large collection of Landsat images.

(Morisette et al., 2012) and Landsat products

(Goward et al., 2003); however with long time series

a further literature research is needed for adopt a

robust method.

4.4 Tropical Forests in Ecuador

Ecuador, despite its small size (only 283,560 km2),

is one of the most diverse countries in the world

(Sierra et al., 2002) and is counted 91 terrestrial

ecosystems in its continental extension and 9 forest

InsightsforManageGeospatialBigDatainEcosystemMonitoringusingProcessingChainsandHighPerformance

Computing

types (MAE, 2013). However, with a deforestation

rate of 0.68 – 1.7 % annual, which equals to

61,764.50 hectares per year (MAE, 2012) put it on

the list of the countries with highest deforestation

rate in the world (Tryse, 2008). Due of this, is

needed better ecosystem monitoring methodologies,

which can manage the lack of high quality remote

sensing data and corresponding ground data sets

(Avitabile et al. 2012).

4.4.1 Open Issues

Respect to the study areas of tropical forest in

Ecuador, three study areas along an altitude gradient

in the Amazon region are being considered for the

first objective; principally for their accessibility and

data availability. Moreover, due that they are

integrated in a watershed; their results can be useful

for the second objective.

5 EXPECTED OUTCOME

At the end of the first part of this research, our

expectations are: 1) generate a first version of a open

source processing chain designed to prepare time

series analysis with large collections of Landsat

images; 2) evaluate the regeneration time of different

forest types in Ecuador; 3) contribute with some

ideas about the links between Geospatial data and

BD; and 4) demonstrate some benefits of the high

performance computing approach in remote sensing

and processing chains.

6 STAGE OF THE RESEARCH

This research started one year ago and its proposal

was accepted six months ago. Since then, the

processing chain has been under active development

and in six additional months will be totally finished.

In the other hand, the redaction of the scientific

paper about this chapter started time before and will

be ready, as well too, in six months. After that

period, a field work of six months in Ecuador will be

done for collect the necessary information of the

second research objective and corroborate the results

of the first research objective.

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InsightsforManageGeospatialBigDatainEcosystemMonitoringusingProcessingChainsandHighPerformance

Computing