Ekundayo E.O. Environmental monitoring

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Monitoring Lake Ecosystems Using Integrated Remote

Sensing / Gis Techniques: An Assessment in the Region of West Macedonia, Greece

201

distributed hydrologic models and for the morphometric evaluation of river network

structure. The analysis of the DEM resulted to the delineation of the hydrographic network

of the area of the transnational Prespa basin. The ASTER DEM has been used to delineate

the changes of the relief of the Vegoritis lake basin.

Geology plays a role in the region as it allows the interconnections of adjacent river basins,

which is the case of Prespa and Ohrid lakes. Ground waters cannot be observed directly by

existing EO satellites, however, location, orientation and length of lineaments can be derived

from EO and can be used as input for studies of fractured aquifers (e.g. location of sites for

water harvesting). Available geologic maps have been scanned, geo referenced, digitized for

the whole region within the context of the GIS system, Figure 3. The original maps have

been of different scales and information content. A great variety of rocks with varying age

and lithology constitute the catchment areas. Available information on location of springs

has been also integrated in the GIS database.

Α1. 1988

Α2. 2000

A1 & A2. Impact of the implementation of Government policies after the 1990’s as it shown

on the multi tem

oral ima

es of 1988 to 2000.

B. Emergent vegetation due to siltation

C. Mining activites. 3D representation of

relief changes due to surface mining as it is

mapped by the ASTER DEM & Landsat

ima

e of 2011.

D. Red areas show burned b

forest fires of 2007 overla

ed on the Corine land cover ma

Fig. 17. Impact of anthropogenic factors to the lakes of the study area.

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Natural and anthropogenic processes take place in the basins of Prespa and Vegoritis lakes

and these have an impact on the water resources of the basins. The catchments of the three

lakes have been described by the GIS based analysis of “Corine Land Cover Classification”

Figure 17-D. MERIS data has been used for Corine land cover map updating because of their

improved temporal resolution. Burnt areas due to the 2007 forest fires are detected and

mapped on the MERIS data.

Surface mining takes place in Vegoritis lake basin with negative impacts of mining on the

water resources, both surface and groundwater, which occur at various stages of the life cycle

of the mines and even after their closure: 1.From the mining process itself, 2. From dewatering

activities which are undertaken to make mining possible. 3. During the flooding of workings

after extraction has ceased 4. By discharge of untreated waters after flooding is complete.

Anthropogenic factors seem to play a key role on the deterioration of the water resources of

the region. Integrated Earth Observation / GIS techniques help to monitor changes in lake

basins and can cover specific water management requirements, Table 2, Figure 17.

Anthropogenic Impact Comments

Transnational

treaties

First aggrement 1959- 2nd 2000 Prespa

Park 2/2/2010, Petersberg Process (1998),

Athens Declaration Process Water

Convention 1992, Karipsiadis2008

Implementation is suffering from

problems like lack of information,

insufficient data.

Infra-

structures

Diverson of Aghios Germanos (1936)

Diversion of Devolli river (mid-70's) It has

deposited about 1.2 million m3 of alluvium

in the shores of Micro Prespa Lake. Sluice

gates controlling flow of waters from

Micro to Macro Prespa lake (2004).

Figure 17_B shows the effect of Devolli

river diversion to Micro Prespa lake.

Mining

The environmental effects of the extraction

stage: Surface disturbance, and the

increased amount of sediments

transported to the lake.

Figure 17 C shows the effect of surface

mining in the Vegoritis lake basin.

Land cover

changes

Multitemporal changes of the surface of

lakes 1972-2009 period.

Land cover changes due to forest fires,

Figure 17 D

Social changes

After the fall of the Eastern Block regimes

the land was redistributed in Albania.

The total 550 agricultural cooperatives were

converted to 467,000 small holder farms.

These land management practices could

have driven or intensified different water

usage across Albania that would have

influenced hydrologic lake water

balances..Figure 17, A1 & A2

Agriculture

Irrigation schemes / pumping stations

were created during the period 1950-1980,

and occur on mainly flat, or gently sloping

and river terrace

Agriculture influence both the quantitative

/ qualitative characteristics of the lakes

Table 2. Selected natural / anthropogenic impacts on the water resources of lakes

Monitoring Lake Ecosystems Using Integrated Remote

Sensing / Gis Techniques: An Assessment in the Region of West Macedonia, Greece

203

An advantage of using remote sensing is that data for large areas within a single image can

be collected quickly and relatively inexpensively, while this can be repeated through

selected time intervals. It is clear that in order to make regional assessments, one must

develop a means to extrapolate from well-studied areas, as the site of our inter-comparison,

to other lakes. Since the strength of satellite imagery for lake monitoring is the regional scale

dimension, more than one location has to be taken for reference in order to learn how to

separate crucial environmental parameters from all kinds of important interfering

phenomena. Deterioration of water quantity and quality parameters is interpreted for Macro

Prespa & Vegoritis lakes, while Ohrid lake remains stable.

6. Discussion

Monitoring of the lake ecosystems is of paramount importance for the overall development

of a region. Remote sensing provides valuable information concerning different

hydrological parameters of interest to a lake assessment project. Monitoring is supported

due to the multi-temporal character of the data. Temporal changes for the last 30 years can

be analyzed with the use of satellite imagery. Processing techniques that have been applied

include integrated image processing / GIS vector data techniques. Satellite data generate

GIS database information required for hydrological studies and the application of models.

Neural network algorithms are quite effective for the satellite data classification. Generated

database can be used to assess changes that are taking place in the lakes and its surrounding

environment. The areal extent of the lakes has been mapped accurately in all cases. Using

the adopted methodology various parameters concerning the lakes and their basins can be

extracted related to the description of catchments, surface area, water-level, hydrogeology

and water quality characteristics of the lakes.

Water quality parameters of the lakes can be retrieved from remote sensing. Peristrophic

movements (gyres) can be clearly identified in the time series images, both in the optical and

thermal bands of the Landsat satellite system for the Macro Prespa lake. Understanding the

naturally occurring mixing processes in the lake aids in determining the ultimate fate of

pollutants, and supports the application of good management strategies and practice.

The high spatial resolution of the satellite images allow the surface currents and general

circulation in lakes to be accurately identified using the multi-temporal imagery. This

can assist in monitoring the clarity and general water quality of lakes. ENVISAT MERIS

satellite data have been used for the assessment of spatio-temporal variability of selected

water quality parameters like dispersion of suspended solids and chlorophyll concentration.

Deterioration of water quantity and quality parameters is interpreted for both Macro Prespa

and Vegoritis lakes. It is indicated that satellite monitoring is a viable alternative for spatio-

temporal monitoring purposes of lake ecosystems. However, technology alone is insufficient

to resolve conflicts among competing water uses. A more useful approach is to have specialists

to support decision makers by making available to them the use of data and techniques.

7. References

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by the International Association for Great Lakes Research, 2006.

Landscape Environmental Monitoring:

Sample Based Versus Complete Mapping

Approaches in Aerial Photographs

Habib Ramezani

, Johan Svensson

and Per-Anders Esseen

Department of Forest Resource Management,

Swedish University of Agriculture Science, Umeå,

Department of Ecology and Environmental Science, Umeå University, Umeå,

Sweden

1. Introduction

Unknown land use premises are to be expected due to changing conditions, e.g. shifting

land use priorities, climate change, globalizing natural resource markets or new products in

the natural resource sector. As a result the need is obvious for accurate, relevant and

applicable landscape data to be used in cause–and–effect analysis concerning changes in

environmental conditions (Ståhl et al., 2011).

The current land use strongly influence landscape structure (composition and configuration)

and contribute to biodiversity loss (Hanski, 2005; Fischer and Lindenmayer, 2007). In order

to consider current status and also to monitor trends within a landscape there is a need for

reliable and continuous information as a basis for policy– and strategic – as well as

operational decision making (Bunce et al., 2008). For this purpose, many countries have now

established or are in the process of establishing monitoring programs that provide

information on large spatial scale (e.g., regional and national levels), for instance, the

National Inventory of Landscapes in Sweden (NILS) (Ståhl et al., 2011), the Norwegian 3Q

(NIJOS, 2001), and similar programs in other countries, e.g., in Hungary (Takács and

Molnár, 2009). A major concern in landscape monitoring at national scale is the large

complexity and amount of data, and the consequently the labor need in data acquisition,

database management as well as data analysis and interpretation.

Description and assessment of landscape conditions and changes require relevant, accurate

and applicable landscape metrics, which are defined based on measurable attributes of

landscape elements such as patches or boundaries. The suite of metrics must cover both the

composition and configuration of the landscape to have potential to detect changes within a

given landscape or when comparing different landscapes.

Calculation of landscape metrics is commonly conducted on completely mapped areas

based on remotely sensed data. FRAGSTATS (McGarigal and Marks, 1995) is a frequently

used software for this purpose. In mapping, homogenous areas are first delineated as

polygons. Aerial photo interpretation is usually performed using a manual approach while

some automated and computer–assisted approaches have recently become available (e.g.,

Blaschke, 2004). Important attributes in manual interpretation include tone, pattern, size and

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shape (Morgan et al., 2010). The experience of the interpreters is critical and the results from

manual interpretation are thus often more accurate than those from automated approaches.

However, the manual approach may be time-consuming (Corona et al., 2004), subjective

(interpreter-dependent) and considerable variation may occur between photo interpreters.

The automated approach is sometimes unreliable, for instance, when land cover classes that

are similar in terms of spectral reflectance should be separated (Wulder et al., 2008). In

addition, overall time, including delineation and corrections may be large if an

inappropriate automated approach is chosen.

Sample based approach is an interesting alternative to extract landscape data compared to

complete mapping (Kleinn and Traub, 2003). The argument is that a sample survey takes

less time; that it is possible to achieve more accurate result in a well-designed and well-

executed sample survey; and that data can be acquired and analyzed more efficiently (Raj,

1968; Cochran, 1977). The efficiency and speed in delivering results is of particular interest

in landscape–scale monitoring programs where stakeholders commonly are closely involved

and expect outputs within reasonable time. Figure 1 shows examples of complete mapping

and sample based approaches (point and line intersect sampling methods) over 1 km × 1 km

aerial photo from NILS.

Fig. 1. Examples of complete mapping and sample based approaches to extract landscape

metrics in 1 km × 1 km aerial photo. A) Complete mapping, B) systematic point sampling

with fixed buffer (40 m), C) point pairs sampling, and D) systematic line intersect sampling.

Since aerial photos are important source of data for many ongoing environmental

monitoring programs such as NILS (Ståhl et al., 2011), there is an urgent need to investigate

the possibilities and limitations of both mapping and sample based approaches for

estimating landscape metrics. The overall objective of this chapter is to compare the

Landscape Environmental Monitoring:

Sample Based Versus Complete Mapping Approaches in Aerial Photographs

207

advantages and limitations of complete mapping versus sample based approaches for

estimating landscape metrics Shannon’s diversity, total edge length and contagion from

aerial photos. The specific objectives are: (1) to compare point and line intersect sampling for

selected metrics in terms of the level of detail and accuracy of data extracted, and the time

needed (cost) to extract the data, (2) to compare sample based and complete mapping

approaches in terms of time needed, and (3) to investigate statistical properties (bias and

RMSE) of estimators of selected metrics using Monte-Carlo sampling simulation.

2. Material and methods

2.1 Study area

The data was collected from aerial photographs and land cover maps from the NILS

program (Ståhl et al., 2011), which covers the whole of Sweden. NILS was developed to

monitor conditions and trends in land cover classes, land use and biodiversity at multiple

spatial scales (point, patch, landscape) as basic input to national and international

environmental frameworks and reporting schemes. NILS was launched in 2003 and has

developed a monitoring infrastructure that is applicable for many different purposes. The

basic outline is to combine 3-D interpretation of CIR (Color Infra Red) aerial photos with

field inventory on in total of 631 permanent sample plots (5 km × 5 km) across all terrestrial

habitats and the land base of Sweden (see Fig. 2).

Fig. 2. Illustration of systematic distribution of 631 NILS 1 km × 1 km sample plot across

Sweden with ten strata. The density of plots varies among the strata (Ståhl et al., 2011).

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The present study is based on a detailed aerial photo interpretation of a central 1 km × 1 km

square in the sample plot. Landscape data was extracted from 50 randomly selected NILS

1 km × 1 km sample plots distributed throughout Sweden. The aerial photo interpretation is

carried out on aerial photos with a scale of 1:30 000. The aerial photographs in which

interpretations were made had a ground resolution of 0.4 m. Polygon delineation is made

using the interpretation program Summit Evolution from DAT/EM and ArcGIS from ESRI.

According to the NILS’ protocol, homogenous area delineated into polygons which are

described with regard to land use, land cover class, as well as features related to trees,

bushes, ground vegetation, and soils (Jansson et al., 2011; Ståhl et al., 2011).

2.2 Landscape metrics

Landscape metrics are defined based on measurable patch (landscape element) attributes

where these attributes first should be estimated. In this study, point (dot grid) and line

intersect sampling (LIS) methods were separately applied in (vector-based) land cover map

from aerial photos for estimating three landscape metrics: Shannon’s diversity, total edge

length and contagion. Riitters et al. (1995) demonstrated that these metrics are among the

most relevant metrics in landscape pattern analysis. Definition and estimators of the

selected metrics are briefly described below.

2.2.1 Shannon’s diversity index (H)

This metric refers to both the number of land cover classes and their proportions in a

landscape. The index value ranges between 0 and 1. A high value shows that land cover

classes present have roughly equal proportion whereas a low value indicates that the

landscape is dominated by one land cover class. The index,

, is defined as

ln( )









(1)

where

is the area proportion of the

th land cover class and s is the total number of land

cover classes considered (assumed to be known). For

0, ln( )

jjj

ppp





is set to zero. The

estimator

of H is obtained by letting the estimator

for land cover class

in Eq. 2 (for

point sampling) and in Eq. 3 (for line intersect sampling) take the place of

in formula (1).

With point sampling,

is estimated without bias by





(2)

where

takes the value 1 if the

th sampling point falls in certain class and 0 otherwise

and

n is the sample size (total number of points).

With the line intersect sampling (LIS) method (Gregoire and Valentine, 2008),

can

unbiasedly be estimated by







(3)

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Sample Based Versus Complete Mapping Approaches in Aerial Photographs

209

where

l is the intersection length of the

th land cover class with sampling line

, L is the

total length of all line transects, and

is the total area.

2.2.2 Total edge length (E)

This metric refers to the amount of edge within landscape. An edge is defined as the border

between two different land cover classes. Edge length is a robust metric and can be used as a

measure of landscape fragmenattion (Saura and Martinez-Millan, 2001). In a highly

fragmented landscape there are more edges and response to those depends on the species

under consideration (Ries et al., 2004). The length is relevant for both biodiversity

monitoring and sustainable forest magament.

Ramezani et al. (2010) demonstrated that total edge length in the landscape can be estimated

using point sampling in aerial photographs without direct length measurement. In such

procedure, estimation of the length is based on area proportion of a buffer around patch

borders. In Fig. 3 is shown a rectangular buffer around patch border for simulation

application. The proportion of sampling points within the buffer can be employed for

estimating the buffer area and, hence, the edge length. In practice, however, if a photo

interpreter observed a point within distance d from a potential edge, this would be recorded.

Figure 2 shows a circular buffer (with fixed radius 40 m) around sampling points on non-

delineated aerial photograph for estimating edge length in practice.

According to Ramezani et al. (2010), the buffer area

B inside the landscape with area A, can

be estimated without bias, for a given land cover class by

BpA

(4)

where

is the estimator (1) of the buffer area proportion. The length

E of the edge of the

land cover class

is then estimated by



(5)

where d is buffer width (m) in one side.

Fig. 3. Illustration of rectangular buffer with fixed width created in both sides of patch

border for estimating edge length for simulation application (from Ramezani et al., 2010)

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In the LIS method, the estimation of total edge length is based on the method of Matérn

(1964). The edge length can unbiasedly be estimated by simply counting the number of

intersections between patch border and the line transects. According to Matérn (1964), the

total edge length estimator

(m ha

-1

), using multiple sampling lines of equals length, is

given by

10000









(6)

where

m is the total number of intersections, n is the sample size (number of lines) and

is the length of the sampling line (m).

2.2.3 Contagion (C)

Contagion metric was first proposed by O’Neill et al. (1988) as a measure of clumping of

patches. Values for contagion range from 0 to 1. A high contagion value indicates a landscape

with few large patches whereas a low value indicates a fragmented landscape with many

small patches. Contagion metric is highly related to metrics of diversity and dominance and

can also provide information on landscape fragmentation. This metric is originally defined and

calculated on raster based map (O’Neill et al., 1988; Li and Reynolds, 1993).

Recently, however, a new (vector-based) contagion metric has been developed by Ramezani

and Holm (2011a), which is adapted for point sampling. The new version is distance–

dependent and allows estimating contagion metric using point sampling (point pairs).

According to Ramezani and Holm (2011a), for a given distance

d the (unconditional)

contagion estimator is defined as

ˆˆ

()ln( ())

() 1

2ln( )

ij ij









(7)

where the

()

(unconditional probability) is estimated by the relative frequency of points

in land cover classes

and

. The estimator

()

is then inserted into the Eq. 7 to obtain

estimator of

()Cd

the unconditional contagion function and s is the number of observed

land cover classes in sampling.

A vector based contagion metric has been developed by Wickham et al (1996), which is

defined based on the proportion of edge length between land cover classes

and

to total

edge length within landscape. This definition (i.e., Eq. 8) is more adapted to the LIS method.

According to Wickham et al (1996), contagion estimator can be written

ˆˆ

ln( )

ln(0.5( ))

ii j











(8)

Similar to point based contagion (Eq. 7), component

should be estimated and then

inserted into Eq. 8. The estimator

(

ˆˆ



) is the proportion of the estimator of edge

length between land cover classes

and

(

) to the estimator of total edge length (

)