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References:
1. Principles and Methods in Landscape Ecology 
Landscape Ecology  Almo
Farina
2. Landscape ecology principles in Landscape Architecture and Land use Planning 
Land use Planning  Wenche
E. Dramstad
E. Dramstad, James D. Olson, , James D. Olson, Richard T.T. Forman
3. International Journals
PPT would be uploaded to the BLOG
Dr. Kaswanto
Selasa 12 Mei 2015
www.kaswanto.staff.ipb.ac.id
MESO SCALE
Outline: 1.Introduction
2.Fractal Dimension
3.Geographic Information Systems (GIS)
4.Remote Sensing (RS)
5.Case Studies
It is important to immediately clarify that the study of the landscape requires metrics but also additional tools like Databases, Spatial Statistics, Geographic Information Systems, Remote Sensing Techniques and Global Positioning Systems, that are used in many other circumstances.  These methodologies are applied in geology, geography, navigation agronomy climatic economics and social navigation, agronomy, climatic economics and social sciences, forecasting, etc.
 At least 4 methodological approaches to study landscape metrics: 1) numerical analysis, 2) spatial analysis, 3) multiscalar analysis and 4) spatial modeling analysis.

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Landscape analysis can be performed on at least at four levels of spatial resolution: individual, patch, mosaic
ti l l ti i di id l t h i and landscape
d l d
(Figure 8.2).
The measurement of distances can be done according a selection of possibilities: 1. from each patch to all the adjacent neighbors of each patch. 2. from a patch to all others of the same group, 3. from each patch to the p
single nearest patch of a different group, 4. from a patch of a specific group to another patch of a specific group (Figure . 8.5 and Table 8.6).
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Figure 8‐13. Example of different complexity of a vegetation border expressed by the fractal dimension D, note that the increase of edges is equivalent to the increase of fractal dimension (from Hees 1994, with permission).
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pp
p
The GIS appears indispensable for most landscape investigations like:
Land use change
Vegetation patterning
Animal distribution across the landscape
Linking remote sensing with topography
Modeling processes across the landscape
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Figure 8‐29. Spectral and spatial resolution for the commonest civilian satellites: AVHRR satellites: AVHRR, MSS, TM and SPOT, and the electromagnetic spectral response curve for green vegetation (Iverson et al. 1989).
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



Bird community ecology
Rectify aerial photographs
Low‐altitude oblique photographs
mapping vegetation patches on the ground with an accuracy of 5m after differential correction.
www.gpsireland.ie/
http://archaeology.about.com/
www.engadget.com
http://electronics.howstuffworks.com
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1. AEZ
AEZ
2. UHI
3. LUCC
4. Carbon Stock
5. Water Quality
CASE STUDY 1: AEZ ‐ DAS CIANJUR
Distribusi Klas
Elevasi (atas kiri), Klas Kemiringan
Lereng (atas kanan)
Existing Tataguna
Lahan (tengah kiri), Jenis Tanah (tengah‐kanan) Bahaya Erosi
(bawah kiri), dan
usulan tata guna
lahan ekologis
(bawah kanan) BACK
DAS Cianjur – Sub‐DAS Citarum ( Saroinsong, Arifin, Gandasasmita & Takeuchi, 2003)
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(Wang et al, 2012) (Wang et al, 2012) BACK
CILIWUNG WATERSHED
(PPLH IPB 2004)
40
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Land Use Change on Spatial Pattern
CILIWUNG WATERSHED
(PPLH IPB 2004)
41
42
1. Type of change of land use pattern (1989 vs. 2009)
LANDSAT Satellite Images (1989, 2001 and 2009)
Land Use and Cover Classification
1. Type of land use pattern change
•
•
•
•
•
•
•
Appearance
Disappearance
Expansion
Annexation
Reduction
Division
Remain
2. Forest Annual Rate Change
Calculated with the formula proposed Puyravaud (2003):
%⁄
100
2
2
1
1
3. The Driving Factors of Change
•
•
•
•
•
•
•
Altitude
Slope
Population density
Distance to major road
Distance to river
Distance to urban area
Soil drainage
Source: Someya et al. (2009)
Impact of land use changes on spatial pattern of landscape
43
44
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Results
Changes in the areas of the various types of forests pattern
2. The Annual Rate of Change
The annual rate of change for forest was calculated with the formula proposed
by Puyravaud (2003):
1
A2 and A1 = the forest cover areas at the end and the beginning, respectively, of
the period being evaluated.
t1 and t2 = the numbers of years spanning on that period.
20
15
DA
EX
AN
RD
DI
,
1989
2009
DA
EX
AN
RD
DI
Variables
Altitude
Slope
Population density
Distance to major road
Distance to river
Distance to urban area
Soil drainage
Agriculture land
F
0.0012
0.0302
‐ 0.0003
0.0261
0.0043
‐ 0.0584
0.0745
2009
10
DA
EX
AN
RD
DI
RE
Type of Change
Cibuni Watershed 100
Area (Thousand ha)
Area (Thousand ha)
Area (Thousand ha)
Area (Thousand ha)
Grass land
1989
20
AP
100
50
2001
80
60
40
20
0
1989
2001
2009
1989
2001
Year
2009
2009
Year
Year
F
Forest
t
Forest
30
RE
0
Year
50
40
Cimandiri Watershed 20
1989
RE
The Land Use Changes in the Southern Areas 40
2009
DI
AP: Appearance, DA: Disappearance, EP: Expansion, AN: Annexation, RD: Reduction, DI: Division, RM: Remain 0
2001
RD
60
Type of Change
45
AN
0
AP
150
1989
EX
Cibuni Watershed
Thousands
βn
2,
60
100
0
DA
Type of Change
70
60
50
40
30
20
10
0
Ciliwung Watershed 20
2009
AP
RE
Area of Change (ha)
β2
1, Cisadane Watershed 40
1989
2
Type of Change
The Land Use Changes in the Northern Areas 60
6
Cimandiri Watershed
Pi = the probability of a grid cell for the occurrence of land use type.
X’s = the driving factors.
Βi = the coefficient of each driving factor in the logistic model.
80
8
0
AP
Thousands
β1
12
10
4
0
Area of Change (ha)
β0
2009
5
3. The Driving Factors of Change
1
1989
10
Thousa
ands
2
1
Ciliwung Watershed
255
Area of Change (
(ha)
100
2
Thousa
ands
Area of Change (
(ha)
%⁄
Cisadane Watershed
30
Built‐up area
Northern
G
A
0.0054 ‐ 0.0089
0.0323 ‐ 0.0002
‐ 0.0001 ‐ 0.0007
0.0098
0.0021
0.0001
0.0001
‐ 0.0691
0.0021
0.0689 ‐ 0.0891
B
‐ 0.0074
0.0098
‐ 0.0002
‐
‐
‐ 0.0012
0.0025
Forest
Grass land
Variables
Altitude
Slope
Population density
Distance to major road
Distance to river
Distance to urban area
Soil drainage
Agriculture land
F
0.0032
0.0502
‐ 0.0653
0.0037
0.0083
‐ 0.9834
0.0519
G
Grass land
l d
A i lt
Agriculture land
l d
Built‐up area
Southern
G
A
0.0010 ‐ 0.0198
0.0356 ‐ 0.0112
‐ 0.0001 ‐ 0.0001
0.0043
0.0163
0.0163
0.0653
‐ 0.0451
0.0001
0.0889 ‐ 0.0341
Variables
B
‐ 0.0074
0.0058
‐ 0.0001
‐
‐
‐ 0.0002
0.0001
47
Altitude
Slope
Population density
Distance to major road
Distance to river
Distance to urban area
Soil drainage
F
0.0010
‐ 0.0282
‐ 0.0001
0.0056
0.0098
‐ 0.0025
0.2378
B ilt
Built‐up area
Southern
G
A
0.0098
0.0001
0.0093 ‐ 0.0072
‐ 0.0001 ‐ 0.0001
0.0043
0.0025
0.0001
0.0002
‐ 0.0001
0.0001
0.7629 ‐ 0.0091
B
‐ 0.0274
0.0738
0.0051
‐
‐
‐ 0.0009
‐ 0.0001
F
Forest
t
G
Grass land
l d
Variables
Altitude
Slope
Population density
Distance to major road
Distance to river
Distance to urban area
Soil drainage
A i lt
Agriculture land
l d
F
0.0012
0.0302
‐ 0.0003
0.0098
0.0024
‐ 0.0064
0.0519
B ilt
Built‐up area
Southern
G
A
0.0014
0.0001
0.0413 ‐ 0.0112
‐ 0.0001 ‐ 0.0001
0.0014
0.0021
0.0084
0.0174
0.0001
0.0001
0.8689 ‐ 0.0641
B
0.0024
0.0138
0.0091
‐
‐
‐ 0.0292
0.0009
BACK
48
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Carbon Stock Estimation
Carbon Stock Estimation
CASE STUDY 4: Carbon Stock Estimation
Cisadane
Ciliwung
Cimandiri
Cibuni
Carbon at Agro‐forestry Landscapes
• Biomass
• Necromass
• Soil Organic Matter Above Ground
• Trees Biomass
• Understorey plants
• Necromass
N
• Litter
Below Ground
• Soil Organic Matter
50
50
Carbon Stock Estimation
THE SCALING UP
Methods
LANDSAT image
1
• Tree scale
• Crops scale
C ~ Plant
2
• Plot scale
• Quadrant scale
C ~ Plot 3
• Landscape scale
• Land use scale
C ~ Land use
4
• Watershed Scale
• Regional Scale
C ~ Watershed
LULC classification
Sampling plot in each land use Plants Biomass
•Trees
T
•Root
•Understorey
Necromass
•Wooden
W d
•Non‐wooden
Soil Organic Matter
• Depth 0 ‐5, 5‐15, &15‐30 cm
D th &
Calculating C stock in each land use
51
Short Explanation
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Sampling Plot Procedure
Land Utilization Types
Main Land Use Type
Forest
Grassland/bareland
Big Plot 20 x 100 m
Small Plot 5 x 40 m
Agriculture land
Sub Plot 2 * (0.5 x 0.5 m)
Land use
0.5 m
5
Note:
N
t
Tree with dbh > 30 cm
Tree with dbh < 30 cm
Understorey and Litter
No Land Utilization Type
Code Description
1 Primary Forest
PF
Natural forest
2 Secondary Forest
SF
Replanted forest
Dominated by Hevea brasiliensis plants
3 Rubber Forest
RF
4 Albizia Forest
AF
Dominated by Paraserianthes falcataria plants
5 Imperata
IC
Bareland covered by Imperata cylindrica
6 Cassava
CS
Dominated by Manihot esculenta cultivation
7 Grassland 1
GS1 Grassland with herbaceous plants
8 Grassland 2
GS2 Grassland with mixed grass species
9 Tea plantation
TP
Tea (Camellia sinensis) cultivation
10 Cacao plantation
CP
Cacao (Theobroma cacao) cultivation
11 Vegetable Dryfield
VD
Highland vegetables, dryfield
12 Strachy Crops dryfield SD Mixed strachy crops, dryfield
13 Agroforestry 1
AF1 Agroforestry system dominated by coconut and/or bamboo species
0.5 m
14 Agroforestry 2
1 land utilization type 3 sampling plots.
1 watershed 16 type of land utilizations.
1 watershed  48 plots, in total 192 plots were measured.
0.5 m
0.5 m
AF2 Agroforestry system dominated by mahogany and/or fruit trees species
15 Paddyfield 1
PF1
Paddy field local rice cultivar
16 Paddyfield 2
PF2
Paddy field with cultivar R‐64
25
500
20
400
15
300
200
10
100
5
0
Cimandiri Watershed
The SAs
Amount o
of C Stock (Mg/ha)
30
600
Area (ha) Thousands
Cisadane Watershed
Amountt of C Stock (Mg/ha )
Results
The NAs
54
0
PF
SF RF AF
IC
60
600
50
500
40
400
30
300
200
20
100
10
0
CS GS1 GS2 PT1 PT2 VD SD AF1 AF2 PD1 PD2
0
PF
Land Use Types
Area (ha) Thousands
A
53
SF RF AF
IC
CS GS1 GS2 PT1 PT2 VD SD AF1 AF2 PF1 PF2
500
20
400
15
300
200
10
100
5
0
Cibuni Watershed
Amount of C Stock (
Mg/ha)
30
25
Area (ha) Thousands
Amount of C Stock (Mg/ha)
Ciliwung Watershed
600
0
PF
SF RF AF
IC
Understorey
Necromass
Litter
Soil 0‐5 cm
Soil 5‐15 cm
50
500
40
400
30
300
200
20
100
10
0
CS GS1 GS2 PT1 PT2 VD SD AF1 AF2 PF1 PF2
0
PF
Land Use Types
Trees
60
600
Area (ha) TThousands
Land Use Types
SF RF AF
IC
BACK
CS GS1 GS2 PT1 PT2 VD SD AF1 AF2 PF1 PF2
Land Use Types
Soil 5‐15 cm
Area
55
Trees
Understorey
Necromass
Litter
Soil 0‐5 cm
Soil 5‐15 cm
Soil 5‐15 cm
Area
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Water Resources Management
Water Quality Samples Locations
CASE STUDY 5: Water Quality
Water Samples:
•
•
•
•
 Based on result from preliminary research, the water quality was measured through 11 parameters. 4 watersheds
6 villages in each watershed
4 locations in each village
g
3 repetitions in a location
Down Stream
300 m asl
Total:
Middle Stream
4 x 6 x 4 x 3 = 288 samples
700 m asl
 Those are (1) Dissolved Oxygen: DO, (2) Biological Oxygen
Demand: BOD, (3) Chemical Oxygen Demand: COD, (4)
Ammonium: NH4, (5) Nitrate: NO3, (6) Nitrite: NO2, (7)
Phosphate: PO4, (8) Acidity: pH, (9) Alkalinity: OH‐, (10) Bacteria
Escherichia coli, and (11) General Bacteria ‐ others than E. coli.
Upper Stream
Village Samples
57
58
Water Resources Management
Water Resources Management
11 Parameters:
•
•
•
•
•
•
•
•
•
•
•
WQI formula proposed by Rodriguez de Bascaroan
(Pesce & Wunderlin, 2000)
DO
COD
BOD
Nitrite Nitrate
Ammonium
Phosphate
Alkalinity Acidity
Escherichia coli
General Bacteria
Normalization Factor (Ci)
Parameters
Pi
100
90
80
70
60
50
40
30
20
10
0
DO
4.0
>7.5
>7
>6.5
>6
>5
>4
>3.5
>3
>2
1
<1
COD
3.0
<5
<10
<20
<30
<40
<50
<60
<80
<100
≤150
>150
BOD
3.0
<0.5
<2
<3
<4
<5
<6
<8
<10
<12
≤15
>15
NO2‐N
2.0
<0.005
<0.008
<0.01
<0.04
<0.075
<0.1
<0.15
<0.2
<0.25
≤0.5
>0.5
NO3‐N
2.1
<0.5
<2
<4
<6
<8
<10
<15
<20
<40
≤70
>70
NH4‐N
3.0
30
<0.01
<0.05
0.1
<0.2
<0.3
<0.4
<0.5
<0.75
<1
≤1.25
>1.25
PO4
1.1
<0.025
<0.05
<0.1
<0.2
<0.3
<0.5
<0.75
<1
<1.5
≤2
>2
Alkalinity
1.7
<20
<40
<60
<80
<100
<120
<140
<160
<180
≤200
>200
pH
1.9
7
6.9‐7.5
6.7‐7.8
6.5‐8.3
6.2‐8.7
5.8‐9.0
5.5‐9.5
5.0‐10.0
4.5‐10.5
4.0‐11.5
<4.0;>11.5
Escherichia coli
3.0
<50
<500
<1000
<2000
<3000
<4000
<5000
<7000
<10000
≤14000
>14000
Fecal Coliform
3.6
<50
<500
<1000
<2000
<3000
<4000
<5000
<7000
<10000
≤14000
>14000
*All values are in mg/l, except for pH (pH unit) and bacteria (MPN/100ml).
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Water Resources Management
Water Resources Management
WQ Sample Location
Results
Springs
a
a
a
Excellent
Good
a
b
c
Ponds
d
Medium
Paddy Fields
Bad
Rivers
Very Bad
1 Village
Among four locations, the highest to
the lowest WQI values are springs,
ponds, paddy fields and rivers,
respectively.
Classification of WQI in stream level and water sample location. All WQI values are
situated at “good” and “medium” levels. The different letter show the mean
difference is significant at the 0.05 level.
BACK
61
QUIZ
Jawablah pertanyaan di bawah ini
1. Pilihlah salah satu case studies case studies yang yang sudah
sudah dijelaskan di atas,
atas,
2. Sebutkan lokasi yang ingin
yang ingin Anda analisis untuk case studies di
case studies di
atas,,
atas
3. Jelaskan alasannya dalam perspektif Ekologi Lanskap, Lanskap, mengapa
mengapa
Anda
d ingin melakukan
l k k analisis
l tersebut
b di
d lokasi
l k yang Anda
yang Anda
d
tentukan
PENGANTAR EKOLOGI LANSKAP 2015
PENGANTAR EKOLOGI LANSKAP Tulis jawaban Anda pada blog http://kaswanto.staff.ipb.ac.id/ paling lambat
paling lambat hari Selasa tanggal 19 Mei 2015 pukul
19 Mei 2015 pukul 07.00 WIB
07.00 WIB..
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