=Paper=
{{Paper
|id=None
|storemode=property
|title=Geospatial Intelligence and Data Fusion Techniques for Sustainable
Development Problems
|pdfUrl=https://ceur-ws.org/Vol-1356/paper_48.pdf
|volume=Vol-1356
|dblpUrl=https://dblp.org/rec/conf/icteri/KussulSBSKL15
}}
==Geospatial Intelligence and Data Fusion Techniques for Sustainable
Development Problems==
https://ceur-ws.org/Vol-1356/paper_48.pdf
Geospatial intelligence and data fusion techniques for
sustainable development problems
Nataliia Kussul1,2, Andrii Shelestov1,2,4, Ruslan Basarab1,4, Sergii Skakun1, Olga
Kussul2 and Mykola Lavreniuk1,3
1.
Space Research Institute NAS Ukraine and SSA Ukraine
(nataliia.kussul, serhiy.skakun, andrii.shelestov,
basarabru)@gmail.com, nick_93@ukr.net
2.
National Technical University of Ukraine “Kyiv Polytechnic Institute”, Kyiv, Ukraine
olgakussul@gmail.com
3.
Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
4.
National University of Life and Environmental sciences of Ukraine
Abstract. Knowledge on spatial distribution of land cover and land use is
extremely important for solving applied problems in many domains such as
agriculture/food security, environmental monitoring, and climate change.
Geospatial data including satellite imagery play an important role since it can
provide regular, consistent and objective information. Identifying geospatial
patterns and quantifying changes that occur in space and time require special
techniques to be exploited. These techniques are associated with the area of
geospatial intelligence and deal with multi-source data fusion and exploitation
of advance intelligent methods. This paper presents the use of these techniques
for processing archived and up-to-date satellite imagery for large-scale land
cover and crop classification in Ukraine. The main purpose of this paper is to
not only show potential of geospatial intelligence, but to pay attention of
educators to this extremely important area.
Keywords. Geospatial intelligence, land cover, crop mapping, image
processing, satellite imagery, big data.
Key Terms. HighPerformanceComputing, MachineIntelligence,
InformationTechnology, Intelligence, Data.
1 Introduction
Geospatial information is a very important source of data for distributed systems
development, education, decision making and competitive business. Due to regular
acquisition of satellite data all over the world for the last couple of decades as well as
new communication, navigation and crowdsourcing techniques, it has become
possible to monitor the current state of the large territories development, estimate
trends, analyze available scenarios for future development and manage things to
provide sustainability. The approach is based on modern IT, namely geospatial
intelligence [1] and data fusion [2] techniques. By geospatial intelligence we consider
all aspects of geospatial data processing including intelligent methods and
technologies to fuse/integrate data and products acquired by multiple heterogeneous
sources using machine learning techniques and emerging big data and geo-
information technologies. In this paper we exploit geospatial technique to address two
important applications for Ukraine, in particular land cover/land use mapping and
crop mapping. The purpose is to not only show the potential of geospatial
intelligence, but to pay attention of the educators to this powerful IT and bridge the
gap between market needs for such specialists and professionals.
Ukraine is one of the main crop producers in the world [3], so agricultural
monitoring is a very important challenge for Ukraine. One of the most promising data
sources to solve the underlined tasks at large scale is remote sensing data, namely the
satellite imagery [4-12]. This is mainly due capabilities to timely acquire images and
provide repeatable, continuous measurements for large territories. At present, there
are only coarse-resolution satellite imagery (500 m spatial resolution), that has been
utilized to derive global cropland extend, e.g. GlobCover, MODIS [13]. But, low-
resolution maps always underestimate or overestimate certain land cover or crop type
areas. Also several global land cover maps have been made using higher resolution
data such as from Landsat-series satellites [14-15], but they are not accurate enough at
regional level for Ukraine. Therefore, creation of global products, such as land cover
maps and crop maps, based on high resolution satellite images (at 30 m) is very
important task for sustainable economic development of Ukraine. This paper presents
the results of regional retrospective high resolution land cover mapping and large
scale crop mapping for Ukrainian territory using multi-temporal Landsat-4/5/7/8
images and also some supporting data and knowledge obtained during our own
investigations [7-8]. The main results of the work were obtained within EC-FP7
project “Stimulating Innovation for Global Monitoring of Agriculture and its Impact
on the Environment in support of GEOGLAM” (SIGMA).
2 Objective of the study and data description
The paper covers two different studies: retrospective land cover mapping and crop
mapping. These two problems are solved using the same geospatial intelligence
approach that encompasses the use of advanced machine learning techniques. In
particular, we use a combination of unsupervised and supervised neural networks to
first restore missing values in multi-temporal images, and then to provide a supervised
classification with an ensemble of multilayer perceptrons (MLPs). One of the
advantages of this approach is possibility for automatic processing taking into account
of large amount of satellite imagery that need to be processed.
At the first study, we used atmospherically corrected Landsat-4/5/7 products to
produce land cover maps for land cover change detection. This was performed for all
territory of Ukraine and required processing of about 500 Landsat scenes to cover it
completely for three decades: 1990s, 2000s and 2010s. Also, we manually formed
training and test sets for supervised classification using the photo interpretation
method. Train and test sets were created with uniform spatial distribution over the
territory of interest and proportional representation of all land cover classes, namely
artificial surface, cropland, grassland, forest, bare land and water.
Fig. 1. Location of Ukraine and JECAM test site in Ukraine (Kyiv oblast, marked with bold
boundaries.
The second study is the pilot project on large scale crop mapping for JECAM test
site [16] in Ukraine for 2013 (Fig. 1). The Joint Experiment for Crop Assessment and
Monitoring (JECAM) is an initiative of GEO Agriculture Monitoring Community of
Practice with the intent to enhance international collaboration around agricultural
monitoring towards the development of a “system of systems” to address issues
associated with food security and a sustainable and profitable agricultural sector
worldwide (http://www.jecam.org). The JECAM test site in Ukraine was established
in 2011 and covers administrative region of Kyiv oblast with the geographic area of
28,100 km2 with almost 1.0 M ha of cropland. For large scale crop mapping over the
study region we used two data sources – remote sensing images acquired by
Operational Land Imager (OLI) sensor aboard Landsat-8 satellite and data acquired at
ground surveys. We used Fmask algorithm for clouds detection and masking [17].
Ground surveys were conducted in June 2013 to collect the knowledge about crop
types and land cover types (Fig. 2) over the interested area. In this study we used
European LUCAS nomenclature as a basis for land cover / land use types.
3 Method and results
The main scientific challenges for geospatial intelligence problem solving are
geospatial data fusion and correct interpretation of geospatial information. To address
them for big data satellite monitoring problems we propose the novel approach, based
on combination of three machine learning paradigms for geospatial information
analysis: big data segmentation, neural network classification and data fusion. Data
fusion is performed at the pixel and at the decision making levels. During
preprocessing stage, Landsat-4/5/7 and Landsat 8 scenes were merged to multi-
channel format for each path, row and date. First, we restore cloudy pixels from time-
series of images using self-organizing Kohonen maps [18] and after provide
classification based on the time-series of restored images available for the certain year
and required area. Classification was done by using an ensemble of neural networks
(MLPs). The method of pixel and decision making level data fusion is proposed in
[16].
Table 1. Accuracy comparison of Land Cover30-2010 and GlobeLand30-2010
Product Land Cover30-2010 GlobeLand30-2010
Class UA, % PA, % UA, % PA, %
Artificial 100 87.8 79.5 3.4
Cropland 93.5 96.2 99.4 85.3
Forest 95.4 96.2 89.9 95.9
Grassland 81.4 71.2 34.4 60.5
Bare Land 91.7 96.4 0.4 57.1
Water 99.5 99.6 96.6 99.9
Overall accuracy, % 94.7 89.7
Fig. 2. The land cover map of Ukraine for 2010 year (and also land cover maps of Kyiv oblast
for 2010, 2000 and 1990 years).
To estimate the accuracy of land cover classification for Ukrainian territory, we
used two approaches: accuracy assessment on independent test (testing) set and
comparison of the class areas in land cover with official statistics. The overall
classification accuracy achieved in this study was approximately 95%. Accuracies for
each individual class were more than 70%. The lowest classification accuracy was for
grassland, because it is difficult to separate grassland from some of spring crops. We
also compared (Table 1) our result, taken for Ukraine with global land cover map
GlobeLand30-2010 at 30 m resolution. The overall classification accuracy of our land
cover map was 5% higher than GlobeLand30-2010. Also accuracy of grassland from
our maps was +10% (producer accuracy, PA) and +45% (user accuracy, UA) [19]
better than GlobeLand30-2010. Our final land cover map is shown at Fig. 2.
Table 2. Classification results
No Class PA, % UA, %
1 Artificial 100.0 97.9
2 Winter wheat 95.7 91.8
3 Winter rapeseed 93.5 99.4
4 Spring crops 40.6 34.6
5 Maize 90.5 86.8
6 Sugar beet 94.9 89.6
7 Sunflower 84.1 85.4
8 Soybeans 69.7 77.1
9 Other cereals 70.9 78.0
10 Forest 96.9 92.9
11 Grassland 91.0 89.0
12 Bare land 86.7 99.0
13 Water 100.0 98.1
3.1 Large scale crop mapping
The use of multi-temporal Landsat-8 imagery and an ensemble of MLP classifiers
allowed us to achieve overall accuracy of slightly over 85% (Table 2) which is
considered as target accuracy for agriculture applications.
Target accuracy of 85% was also achieved for winter wheat, winter rapeseed,
maize and sugar beet. For the spring crops, sunflower and soybeans the accuracy is
less, than 85%. Soybeans is the least discriminated summer crop with main confusion
with maize. In particular, almost 61% of commission error and 71% of omission error
was due to confusion with maize. All non-agriculture classes including forest and
grassland yielded PA and UA of more than 85%. The final classification map is
shown in Fig. 3.
Comparison of official statistics and crop area estimates derived from Landsat-8
imagery for Kyiv region described at the Table 3.
Fig. 3. Final crop map obtained by classifying multi-temporal Landsat-8 imagery.
Table 3. Comparison of official statistics and crop areas derived from Landsat-8 imagery
Class no. Class Crop area: Crop area: Relative
official Landsat-8 error, %
statistics, x derived, x 1000,
1000, ha ha
2 Winter wheat 187.3 184.5 -1.5
3 Winter rapeseed 46.7 59.9 28.3
5 Maize 291.7 342.4 17.4
6 Sugar beet 15.5 11.2 -27.9
7 Sunflower 108.2 117.6 8.7
8 Soybeans 145.9 168.5 15.5
4 Application in education process
As well as geospatial intelligence is one of the emerging areas of data science, we
actively use it in education process. Developed approach to land cover and crop
mapping is actively used for education purposes. We incorporate these topics
(geospatial intelligence methods and developed software) into a master and PhD
program of “Ecological and economic monitoring” specialization at the National
University of Life and Environmental Sciences of Ukraine with the main focus on big
geospatial data processing and satellite data analysis.
Also we are actively trying to implement project based education, involving
students into scientific projects . Some methods of data fusion are included into
laboratory works on intelligent computations. Master and PhD student fulfill their
qualification diplomas within international projects. According to our experience
more attention should be paid on geospatial data processing and intelligent
computations within Bachelor programs on Computer Science in Life Science
universities.
5 Conclusions
This paper presents a novel approach for satellite monitoring based on big
geospatial data analysis. The main idea of the proposed geospatial intelligence
approach is the use of supervised neural networks in order to classify multi-temporal
optical satellite images with the presence of missing data. A supervised classification
was performed with the use of ensemble of MLP classifiers to create such global
products as retrospective land cover and crop maps for the whole territory of Ukraine.
Proposed approach allowed us to achieve the overall classification accuracy of 95%
for three different time periods (1990, 2000 and 2010) and improve quality of maps
comparing to other land cover maps available for Ukraine at 30 m spatial resolution,
namely GlobeLand30-2010. The same approach was successfully applied for the
JECAM test site in Ukraine for large area crop mapping.
Now geospatial intelligence is a hot topic in big data analysis, but we observe the
lack of experts in the area. Therefore, we would like to pay attention of the IT
educators to the gap and build a roadmap to fill it.
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