The dynamics of land use/land cover change modeling and their implication for the flood damage assessment in the Tondano watershed, North Sulawesi, Indonesia

  • Fajar Yulianto
  • Indah Prasasti
  • Junita Monika Pasaribu
  • Hana Listi Fitriana
  • Zylshal
  • Nanik Suryo Haryani
  • Parwati Sofan
Original Article

Abstract

The Markov Chain and Cellular Automata (Markov-CA) approach have been applied to create the dynamics of land use/land cover (LULC) change modeling in the Tondano watershed, North Sulawesi, Indonesia. The multi temporal of remotely sensed data, Landsat 5 TM in 1997, Landsat 7 TM in 2002 and Landsat 8 LDCM in 2015 were used to produce the LULC maps. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Map (GDEM) data were used as input for the flood modeling created by the Monte Carlo algorithm. The LULC maps in 1997 and 2002 were used to create predictions and modeling LULC map with the Markov-CA approach in the next few years (for the year 2015, 2025, 2035 and 2050). Meanwhile, the LULC map in 2015 with an accuracy of 80.11 % based on the calculation of the Kappa index has been used as a reference map to determine the accuracy of the Markov-CA approach to produce a model of the LULC map in 2015. The result of the accuracy by using cross-correlation matrix between the LULC model in 2015 with the LULC reference in 2015 is 75.88 %. The dynamics of LULC changes showed that area-class forest, dry land, paddy fields and shrubbery would be expected to experience an area decreases in the extent from the year 2015 to 2050, with the rate of change in average: 10.52, 13.22, 14.49 and 1.15 ha/year, respectively. Meanwhile, the area-class bare soil, plantation, settlement and water body would be expected to experience an area increases, with the rate of change in average: 6.79, 11.14, 11.49 and 9.7 ha/year, respectively. Furthermore, flood damage assessment can be calculated by estimating LULC area affected by the flood, which is determined based on the overlay between LULC maps from the result of Markov-CA with flood maps from the result of Monte Carlo algorithm. Under current conditions, estimated flood damage exposure to extreme flood events with return periods of 100 years for the water level scenario Hc = 3 m and Hc = 5 m is more than €520 and €958 million, respectively.

Keywords

Remote sensing Flood damage assessment Tondano watershed North Sulawesi Indonesia 

Introduction

Land use/land cover (LULC) can be defined as part of the physical composition and characteristics of the land elements on the earth’s surface. LULC systems have complex dynamics, which consists of natural, social and economic spatially. The availability and distribution of LULC change have significant impacts on climate, environmental issue and natural ecosystem conditions (Cihlar 2000; Wang et al. 2012; Yan et al. 2015). The management and good planning to LULC can provide an important role in the moderation of extreme events, climate change mitigation and land degradation. For example, its role can mitigate flood events, landslide and erosion trough water retention in soils and water uptake by vegetation (Stürck et al. 2015). The LULC changes can be caused by several factors, such as human activities, e.g., urbanization, deforestation, agriculture intensification and others. In addition, natural factors also contributed to the change of LULC (Lambin 1997; Halmy et al. 2015).

The dynamics of LULC change can be monitored by using remotely sensed data. The availability of remotely sensed data have been provided a variety of temporal, spectral and spatial resolution, which can be used to detect changes on the earth’s surface (Rogan and Chen 2004; Wu et al. 2006; Halmy et al. 2015). Furthermore, the utilizations of remotely sensed data were used as input in generating a LULC map. The monitoring of the LULC change dynamics can be done by comparing a LULC map at currently with the conditions in previous years as a multi-temporal (Behera et al. 2012). The use of remotely sensed data for mapping of the dynamics of LULC changes has been used extensively. Example include the use of Landsat MSS, Landsat TM, Landsat ETM+, Indian Remote sensing Satellite LISS-III (IRS), MODIS, NOAA/AVHRR, CBERS, among others (e.g., Sun et al. 2007; Gong et al. 2015; Wehmann and Liu 2015; Shooshtari and Gholamalifard 2015; Nejadi et al. 2012; Pouliot et al. 2014; Chen et al. 2013; Yang et al. 2011).

To determine the condition of the LULC in the next several years can be done with modeling of LULC predictions. The modeling can be done either by the model of Markov-CA (Guan et al. 2011; Yang et al. 2014; Gong et al. 2015). The use of Markov-CA is an interesting approach and it has advantages in the modeling of LULC change, spatially and temporally (Sylvertown et al. 1992; Wang and Zhang 2001; Behera et al. 2012). One of the implications of the use of monitoring the dynamics of LULC change and predictions can be used for the flood damage assessment (e.g., Marfai and King 2007; Ward et al. 2010; 2011; Joling 2013; Yulianto et al. 2015a). The flood damage assessment is required to estimate the impact of LULC in the next several years from the occurrence of floods. The existence of such information can then be used as input for flood risk mapping in a region (Su et al. 2005; Metzler 2011). Several other studies related to the implications of LULC for the flood damage assessment (e.g., Jonge et al. 1996; Herath 2003; Yen and Anh 2010; Jongman et al. 2012; Beckers et al. 2013).

The city of Manado is a downstream area of the Tondano watershed, and Fig. 1 shown the location of the research area. There are four major flood events in the city of Manado based on the historical record, namely: December 3, 2000; February 21, 2006; February 17, 2013 and January 15, 2014 (Dayantolis and Fitri 2014). Flooding that occurred on January 15, 2014 in the city of Manado, North Sulawesi, Indonesia is a flood of greatest impact. Rainfall with an intensity of more than 175 mm has become one of the causes of flooding in the research area. This condition during the flood event on January 15, 2014 can be shown based on the monitoring of rainfall accumulation from QMorph remotely sensed data on January 13–16, 2014 (Fig. 2). The existence of these conditions for 2 days with a relatively long duration of time has triggered flood in the research area.
Fig. 1

a Indonesia map for inset location of Sulawesi island. b Inset location of the research area at North Sulawesi. c Digital elevation model (DEM) in the Tondano watershed, North Sulawesi, Indonesia (Source: ASTER GDEM in the year 2011)

Fig. 2

Rainfall accumulation based on the QMorph remotely sensed data in the research area (Tondano watershed), on January 13–16, 2014. a Rainfall accumulation on January 13, 2014, time: 00–23 UTC. b Rainfall accumulation on January 14, 2014, time: 00–23 UTC. c Rainfall accumulation on January 15, 2014, time: 00–23 UTC. d Rainfall accumulation on January 16, 2014, time: 00–23 UTC (Source: QMorph remotely sensed data, processed by: LAPAN 2014)

The availability of information related to the impact of flood events in the research area is important to be done with the aim to quickly estimate of losses caused by flooding. One of the immediate measures that can be used to calculate the impact of flooding on LULC. In addition, the calculation of the impact of flooding from LULC change needs to be done in order to determine the potential loss to the effects of flooding in the future. That information can be used as input to government and several stakeholders to anticipate and minimize the impact of flood risk and it can also be used as a reference in environmental management policies in the research area. The objectives of this research are: (a) to analyze, modeling and predictions the dynamics of LULC change from multi-temporal remotely sensed data, (b) to implement the results of modeling the dynamics of LULC change and predictions for the flood assessment in the research area.

Study area

The Tondano watershed in North Sulawesi, Indonesia, is located at the coordinates of about at 1°27′18.72″ to 1°6′8.28″N and 125°1′38.64″ to 124°45′57.24″E, and It is an include some district or city. For the upstream areas, the coverage area is an includes most of the Minahasa district, Minahasa Utara district and Tomohon city. Meanwhile, for the downstream areas, the coverage area is an includes most of the Manado city. The Tondano watershed area is about 54,108 Ha, which is divided into four (4) sub-watershed, namely: Tikala, Klabat, Tondano and Noongan (The Management Center of Tondano Watershed—Ministry of Forestry 2014). The number of people living in the Tondano watershed area for the year 2014 was more than 697,893 people and distributed in several districts. Table 1 shown the number of the population living in the Tondano watershed for the year 2014 (Indonesian of Central Agency Statistics (BPS) 2014a, b, c, d).
Table 1

The number of the population living at the Tondano watershed in the year 2014

Districts

Number of population in 2014

Manado

 

 Mapanget

51,259

 Paal Dua

42,798

 Singkil

47,963

 Tikala

29,136

 Tuminting

51,039

 Wanea

22,768

 Wenang

33,048

Minahasa

 

 Langowan Timur

12,475

 Langowan Barat

15,308

 Langowan Selatan

7539

 Langowan Utara

8158

 Tompaso

16,117

 Kawangkoan

26,578

 Sonder

18,064

 Tombulu

16,196

 Tondano Barat

19,513

 Tondano Selatan

21,815

 Remboken

11,175

 Kakas

21,473

 Eris

9819

 Kombi

9874

 Tondano Timur

14,130

 Tondano Utara

12,525

Minahasa Utara

 

 Kauditan

24,263

 Airmadidi

28,153

 Kalawat

29,743

 Dimembe

23,568

 Talawaan

19,932

Tomohon

 

 Tomohon Selatan

21,682

 Tomohon Tengah

21,329

 Tomohon Timur

10,453

Total

697,893

Source: Indonesian of Central Agency Statistics (BPS) 2014a, b, c, d

The geological conditions in North Sulawesi is dominated by limestone sedimentary basin as a unit Raratotok. Other rock types are breccias and sandstones, which consists of rough breccia-conglomerate, interspersed with smooth up rough sandstones, silt stone and clay stone, as well as pyroxene andesite breccia. The group of tuff Tondano has a Pliocene age and consists of fragments of andesitic volcanic rocks that contain pumice, tuff and ignimbrite breccia and andesite lava. In the Quaternary rocks are a group of young volcanic rocks, consisting of andesite-basalt lava, bombs, lapilli and ash. Meanwhile, the youngest rock group has a composition of limestone coral reefs, lakes and rivers and sediment deposition alluvium (Sompotan 2012). Figure 3 shown the geological conditions of the research area, which is part of Sulawesi island, Indonesia.
Fig. 3

Geological map of Sulawesi, Indonesia (Source: Hall and Wilson 2000; Sompotan 2012)

Method

Data availability

The availabilities of satellite image data were used in this research consists of Landsat 5 TM, Landsat 7 TM, Landsat 8 LDCM and ASTER GDEM, which have a spatial resolution of 30 m. Multi-temporal Landsat image data were used as input for LULC mapping, modeling and prediction of the dynamic LULC change. Meanwhile, the availability of ASTER GDEM data was used as input for flood modeling in the research area. Table 2 shown the availability of satellite image data were used in this research.
Table 2

The availability of satellite image data were used in this research

Date type

Spatial resolution (m)

Path and row

Acquisition date

Source

Landsat 5 TM

30

112, 059

June 04, 1997

USGS

Landsat 7 TM

30

112, 059

May 25, 2002

USGS

Landsat 7 TM

30

112, 059

July 28, 2002

USGS

Landsat 8 LDCM

30

112, 059

January 29, 2015

LAPAN

Landsat 8 LDCM

30

112, 059

April 03, 2015

LAPAN

Landsat 8 LDCM

30

112, 059

May 21, 2015

LAPAN

ASTER GDEM

30

N01, E124

October 17, 2011

Japan space systems

Data processing and analysis

Pre-processing satellite images

The pre-processing stages in this research were conducted by converting the value of Digital Number (DNs) into the reflectance values of Top of Atmosphere. In the standard product Landsat 5 TM and Landsat 7 TM imagery have DNs format 8-bit unsigned integer. Meanwhile, the standard product of Landsat 8 LDCM imagery has DNs format 16-bit unsigned integer. The pre-processing stage is done with the purpose to obtain the standard reflectance values, because there are differences in the DNs format of the image data. Thus, the results of the pre-processing data can be used for processing and analysis in the later stages. Landsat data processing in this research was done using the Semi-Automatic Classification Plugin contained on Open Source software Quantum GIS (QGIS) 2.12.2 Lyon and it was released on December 18, 2015 (http://www.qgis.org/en/site/forusers/download.html). According to Luca et al. (2013), this plugin is a free plugin for QGIS and it can be used for the semi-automatic supervised classification designed to expedite the processing of multispectral or hyperspectral remote sensing images, which provides a set of tools for pre-processing and post-processing.

In the pre-processing stage was required to change the range of values DNs in the image into reflectance values or radians using radiometric coefficients from the metadata file (MTL_file). The conversion process of DNs values into the reflectance values consists of two stages. The first stage is to convert the DNs values into radian values, and the second stage is to convert the radian values into the reflectance values. Formula to convert DNs values into radian values can be presented in Eq. (1). Meanwhile, the formula to convert the radian value into the reflectance values can be presented in Eq. (2) (Chavez 1988; NASA 2011; Luca et al. 2013; USGS 2013a, b).
$$L\lambda = \left( {\frac{{\left( {LMAX_{\lambda } - LMIN_{\lambda } } \right)}}{{\left( {Q_{Calmax} - Q_{Calmin} } \right)}}} \right) \times \left( {Q_{Cal} - Q_{Calmin} } \right) + LMIN_{\lambda }$$
(1)
where \(L\lambda\) is the spectral radiance at the sensor’s aperture. \(Q_{Cal}\) is the quantized calibrated pixel value. \(LMIN_{\lambda }\) is the spectral radiance that is scaled to \(Q_{Calmin}\). \(LMAX_{\lambda }\) is the spectral radiance that is scaled to \(Q_{Calmax}\). \(Q_{Calmin}\) is the minimum quantized calibrated pixel value. \(Q_{Calmax}\) is the maximum quantized calibrated pixel value.
$$\rho = \left[ {\pi \times (L_{\lambda } - L_{\rho } } \right) \times d^{2} ]/ (ESUN_{\lambda } \times { \cos }\theta_{s} )$$
(2)
where \(\rho\) is the land surface reflectance for Landsat images. \(L_{\lambda }\) is the at satellite radiance. \(L_{\rho }\) is the path radiance. \(d\) is the earth to sun distance in astronomical units. \(ESUN_{\lambda }\) is the mean solar Exo-atmospheric irradiances. \(\theta_{s}\) is the solar zenith angle.

The classification process for Land use/land cover mapping

At this stage, the multi-temporal of Landsat imagery were used as input for processing and classification of LULC mapping. The processing is done by the individual training areas. The maximum likelihood (ML) was used as a classification algorithm on the semi-automatic classification plugin, which is part of the supervised classifier categories.

According to Richards and Jia (2006), Huang et al. (2009), and Luca et al. (2013), the ML algorithm is used the Gaussian threshold stored in each class signature to assign every pixel class, which assumed that the probability distribution for the classes of the models form of multivariate normal. ML algorithm can be presented in Eqs. (3) and (4).
$$G_{i} ({\text{x}}) = {\text{ln p}}(\omega_{i} ) - \frac{1}{2} {\text{ln }}\left| {\mathop \sum \limits_{i} } \right| - \frac{1}{2} \left( {{\text{x }} - m_{i} } \right)^{\text{t}} \mathop \sum \limits_{i}^{ - 1} (x - m_{i} )$$
(3)
Therefore:
$${\text{x}} \in \omega_{i} {\text{i }}\quad {\text{if }}g_{i} ({\text{x}}) > g_{j} ({\text{x}}) \quad {\text{for all j}} \ne {\text{I}}$$
(4)
where \(G_{i} \left( {\text{x}} \right)\) is the discriminant function in ML algorithm. \(\omega_{i}\) is the class, where i = 1,…, M and M is the total number of classes. \(x\) is the vector pixel in n-dimensional, where n is the number of bands. \({\text{p}}(\omega_{i} )\) is the probability that the correct class, in \(\omega_{i}\) for a pixel at position x. \(|\sum_{i} |\) is the determinant of the covariance matrix of the data in class \(\omega_{i}\). \(\mathop \sum \nolimits i\) is the inverse of the covariance matrix. \(m_{i}\) is the mean vector.

Modeling and prediction of the dynamic land use/land cover change

In this research, modeling and prediction of the dynamic LULC change has been carried out by using a Markov-CA. LULC maps in 1997 and 2002, which is the results of multi-temporal classification of Landsat imagery, were used as input data for the modeling and prediction LULC in the next years. Meanwhile, LULC map in 2015 that is the classification result of Landsat imagery in 2015 was used as a reference map to describe the condition LULC today. The reference of LULC map was used to perform the calculation of the accuracy assessment LULC in 2015 with Markov-CA approach.

Markov-CA approach is a combination model of Markov Chain and Cellular Automata approach, which can be used to predict and simulate LULC change in several next year (Behera et al. 2012; Yang et al. 2014). The Markov Chain model is a stochastic process model that can describe the probability of a change from one object to another. The model is one of the recommended methods for modeling LULC based on the use of the time evolutionary trend. It means that the time (t − 1) to time (t) can be used as input probability LULC change, which is further used for LULC prediction in the next year (t + 1) (Thomas and Laurence 2006; Behera et al. 2012). Cellular Automata is a part of the essential elements of geo-spatial, which focus on the variations in the dynamics of change and it is able to simulate the characteristics of the spatial–temporal complex system and it cannot be represented by a specific equation (Mousivand et al. 2007; Arsanjani et al. 2013; Yang et al. 2014). In this research, the modeling LULC have been processed by the tools of Markov-CA module contained in the IDRISI Andes software, which is developed by Clark Labs at Clark University.

Implication for the flood damage assessment

Their implication for flood damage assessment in the research area has been carried out by calculating the impact of flooding inundation of the LULC, which can scripted and predictable for the next few years. In this research, flood inundation scenarios have been created by the Monte Carlo algorithm approach. Flood inundation modeling with the Monte Carlo algorithm approach does not require complex parameters and does not require long processing times, as it has been applied to some previous studies (e.g., Smemoe et al. 2007; Kalyanapu 2011; Yulianto et al. 2015b). The use of Monte Carlo algorithms previously were conducted by Felpeto et al. (2007) and Felpeto (2009) to simulate an inundation on the lava flows. The algorithm model is a probabilistic model, with the assumption that the topographic conditions have a dominant factor for determining the material flows.

In this research, topographic data can be represented by ASTER GDEM, with a spatial resolution of 30 m. In addition, several other parameters were used to run the Monte Carlo algorithm, e.g., maximum flow length, location to start iteration, height correction for water level scenario and the number of iterations. Table 3 shown the parameter data were used for simulation of the flood probability using a Monte Carlo algorithm in the upper boundary Tondano river. The probability of flood inundation using a Monte Carlo algorithm can be presented in Eqs. (5)–(7) (Felpeto 2009; Yulianto et al. 2015b).
$$P_{i} = \frac{{\Delta h_{i} }}{{\mathop \sum \nolimits_{j = 1}^{8} \Delta h_{j} }}$$
(5)
$$\Delta h_{i} = h_{0} + h_{c} - h_{i} \quad if\left( {h_{0} + h_{c} - h_{i} } \right) > 0$$
(6)
$$\Delta h_{i} = 0 \quad if\left( {h_{0} + h_{c} - h_{i} } \right) \le 0$$
(7)
where \(P_{i}\) is the probability of material flows. \(h_{i}\) is the topography represented by the value of the height \((h)\) of a cell in the DEM data located in the cells \(i = 0\) and \(i = 1, 2, 3, \ldots , 8).\) \(\Delta h\) is the height difference between the cell with a cell-neighboring. \(h_{c}\) is the height correction.
Table 3

The parameter data were used for simulation of the probability of flood inundation using a Monte Carlo algorithm in the upper boundary Tondano river

Return periods (years)

Volume (m3)

Maximum flow length (m)

Start iteration position (X; Y)

Height correction for water level (Hc)

The number of iterations

3 m

5 m

2

91,411,390

7435

711593; 163768

3 m, 5 m

33,800

20,300

5

107,900,660

7435

711593; 163768

3 m, 5 m

40,000

24,000

10

120,547,770

7435

711593; 163768

3 m, 5 m

44,800

26,800

20

133,995,330

7435

711593; 163768

3 m, 5 m

49,600

33,700

50

153,526,310

7435

711593; 163768

3 m, 5 m

57,000

38,200

100

169,695,400

7435

711593; 163768

3 m, 5 m

62,900

42,500

Source: summarized and modified data from Nanlohy et al. 2008

The numbers of iteration were determined by the approximation formula. The formula was developed by Zhu (2010) and it has been applied by Seniarwan and Gandasasmita (2013) and Yulianto et al. (2015b) to simulate the inundation models. This formula can be presented in Eqs. (8) and (9).
$$f\left( H \right) = Q - V = Q - \mathop \sum \limits_{i = 1}^{m} A x (Hi - Ei)$$
(8)
$$V = \mathop \sum \limits_{i = 1}^{m} A x hc$$
(9)
where \(f\left( H \right)\) is the function of the equation to analyze the flood inundations height \(\left( H \right)\), which is based on the ratio between the volume grooves topography \(\left( V \right)\) and the volume of the flood source \(\left( Q \right)\). \(H_{i}\) is the accumulation of flood inundation height between the \(h_{c}\) and the DEM elevation \(E_{i}\) in unit pixels \(i\) and \(i = \left( {1, 2, 3, \ldots , n } \right)\). \(m\) is the number of units pixels or pixel iterations required. \(A\) is the area of the unit pixels.

The flood damage assessment can be created using overlaying the maps of flood inundation model under scenario return periods: 2, 5, 10, 20, 50 and 100 years with the maps of LULC modeling under scenario for the 2015, 2025, 2035 and 2050 years. Based on the results of the overlay can be estimated LULC affected by floods for several next year. The value of assets has been calculated with reference to the research conducted by Marfai and King (2007) and Ward et al. (2011) and we have the same assumptions for asset values in the research area with the Semarang and Jakarta, Indonesia.

The value of assets for settlement was estimated to be ca. €1.2 million per hectare, for agriculture (e.g., paddy field, dry land and plantation) was estimated to be ca. €80,000 per hectare, and also for open area (e.g., bare soil and shrubbery) was estimated to be ca. €1700 per hectare. The absolute value of exposure damage estimated in this research are considered as an indication. Exposure damage was estimated based on the actual market value per hectare in each class LULC. However, these estimates give a good impression of exposure damage on LULC changes due to flooding.

Results

Land use/land cover mapping in the research area

In this research, the dynamics LULC change can be described using LULC maps in the year 1997, 2002 and 2015, which is the result of the maximum likelihood classification of Landsat imagery. Spatially, the dynamics LULC change from the year 1997 to 2015 can be presented in Figs. 4 and 5a. Meanwhile, the estimate area for LULC changes in the year 1997 to 2015 can be presented in Tables 4 and 5. It can be shown that for LULC class on forest and paddy fields have experienced a decrease in the extent of 1997–2015, with an average change of area: 121.82 and 108.13 ha/year, respectively. Meanwhile, LULC class on dry land, bare soil, plantation, settlement, shrubbery and water body have experienced an increase with an average change of area: 67.04, 19.93, 61.38, 63.67, 10.28 and 7.65 ha/year, respectively.
Fig. 4

The results of LULC map using maximum likelihood supervised classification on Landsat imagery. a LULC in the year 1997. b LULC in the year 2002

Fig. 5

Comparison between LULC classification was used as a reference map, with the LULC class was generated from the modeling Markov-CA approach. a LULC map in the year 2015 (reference map). b LULC map in the year 2015 (model map)

Table 4

The estimate area of LULC changes for the year 1997–2015 in the research area

LULC Class

Year

1997

Area (ha)a

2002

Area (ha)a

2015

Area (ha)a

Forest

16,498.44 (29.20)

15,617.37 (27.64)

14,305.65 (25.32)

Dry land

9390.15 (16.62)

10,181.44 (18.02)

10,596.88 (18.76)

Bare soil

587.61 (1.04)

810.01 (1.43)

946.36 (1.67)

Plantation

12,487.05 (22.10)

13,309.27 (23.56)

13,591.90 (24.06)

Settlement

2811.42 (4.98)

3270.47 (5.79)

3957.39 (7.00)

Paddy fields

6660.54 (11.79)

5095.80 (9.02)

4714.21 (8.34)

Shrubbery

3651.84 (6.46)

3721.96 (6.59)

3836.89 (6.69)

Water body

4413.96 (7.81)

4494.69 (7.96)

4551.74 (8.06)

Total

56,501.01 (100)

56,501.01 (100)

56,501.01 (100)

aPercentage area

Table 5

The estimate of the average rate area of LULC changes for the year 1997–2015 in the research area

LULC class

Change area (ha)

Average area (ha/year)

1997–2002

2002–2015

1991–2015

Forest

−881.07

−1311.73

−121.82

Dry land

+791.29

+415.44

+67.04

Bare soil

+222.40

+136.35

+19.93

Plantation

+822.22

+282.63

+61.38

Settlement

+459.05

+686.93

+63.67

Paddy fields

−1564.74

−381.60

−108.13

Shrubbery

+70.12

+114.93

+10.28

Water body

+80.73

+57.05

+7.65

‘−’ decrease; ‘+’ increase

Modeling and prediction of the dynamic land use and land cover change

Modeling and prediction of the dynamic LULC change have been created with Markov-CA approach. LULC maps in the year 1997 and 2002 were used as input data for LULC mapping in the next few years. Meanwhile, LULC map in the year 2015 was used as a reference map to determine the accuracy assessment of LULC model in the year 2015 from Markov-CA approach. The accuracy of the LULC reference in 2015 has been determined based on 548 sample location in the research area. At the sample location, LULC map in 2015 can be corrected by using high-resolution imagery from Google Earth. The results of calculation were performed by using the Kappa index obtained accuracy of 80.11 %, with the real similarity object of 83.57 % and inequality objects of 16.43 %. Table 6 shown the accuracy of the Kappa index on the LULC map in 2015, which is the result of the maximum likelihood supervised classification. Furthermore, the accuracy assessment of the LULC map from Markov-CA approach can be calculated using cross-confusion matrix between LULC reference in 2015 to LULC model in 2015 and it can be shown that the accuracy LULC model of 75.88 %. Figure 5 shown the comparison between LULC classification was used as a reference map, with the LULC class was generated from the modeling Markov-CA approach. Meanwhile, the result of the LULC prediction in 2025, 2035 and 2050 can be shown in Fig. 6.
Table 6

The accuracy of the Kappa index for the LULC map in 2015, which is the result of the maximum likelihood supervised classification and corrected by using high-resolution imagery from Google Earth

LULC Classes

Reference

Total

Forest

Dry land

Bare soil

Plantation

Settlement

Paddy fields

Shrubbery

Water body

Forest

151

3

16

170

Dry land

70

2

11

1

84

Bare soil

1

0

6

1

2

1

2

1

14

Plantation

3

1

1

55

1

4

1

66

Settlement

1

49

50

Paddy fields

8

2

7

1

62

4

1

85

Shrubbery

4

1

3

2

4

26

40

Water body

39

39

Total

159

83

12

84

53

82

34

41

548

Fig. 6

LULC maps from the modeling using Markov-CA approach. a LULC map prediction for the year 2025. b LULC map prediction for the year 2035. c LULC map prediction for the year 2050

Based on the results of modeling and prediction LULC using Markov-CA approach, the estimate area for LULC changes in the year 2015–2050 can be presented in Tables 7 and 8. It can be shown that for LULC class on the forest, dry land, paddy fields and shrubbery have predicted a decrease in the extent of 2015–2050, with an average change of area: 10.52, 13.22, 14.49 and 1.15 ha/year, respectively. Meanwhile, LULC class on bare soil, plantation, settlement and water body have predicted an increase with an average change of area: 6.79, 11.14, 11.49 and 9.7 ha/year, respectively.
Table 7

The estimate area of LULC changes for the year 2015–2050 in the research area, which is the result of Markov-CA approach

LULC class

Year

2015

Area (ha)a

2025

Area (ha)a

2035

Area (ha)a

2050

Area (ha)a

Forest

14150.82 (25.05)

14012.89 (24.80)

13884.33 (24.57)

13782.46 (24.39)

Dry land

9567.41 (16.93)

9527.44 (16.86)

9432.80 (16.69)

9104.78 (16.11)

Bare soil

763.82 (1.35)

787.82 (1.39)

930.16 (1.65)

1025.41 (1.81)

Plantation

11461.22 (20.28)

11561.19 (20.46)

11667.34 (20.65)

11851.05 (20.97)

Settlement

5476.19 (9.69)

5546.91 (9.82)

5558.26 (9.84)

5878.28 (10.40)

Paddy fields

7138.69 (12.63)

6968.71 (12.33)

6842.73 (12.11)

6631.38 (11.74)

Shrubbery

3643.86 (6.45)

3615.60 (6.40)

3603.49 (6.38)

3603.28 (6.38)

Water body

4275.00 (7.57)

4504.47 (7.97)

4582.12 (8.11)

4624.16 (8.18)

Total

56,501.01 (100)

56,501.01 (100)

56,501.01 (100)

56,501.01 (100)

aPercentage area

Table 8

The estimate of the average rate area of LULC changes for the year 2015–2050 in the research area, which is the result of Markov-CA approach

LULC class

Change area (ha)

Average area (ha/year)

2015–2025

2025–2035

2035–2050

2015–2050

Forest

−137.93

−128.56

−101.86

−10.52

Dry land

−39.97

−94.64

−328.02

−13.22

Bare soil

+24.00

+166.34

+95.25

+6.79

Plantation

+99.96

+106.15

+183.71

+11.14

Settlement

+70.71

+11.35

+320.02

+11.49

Paddy fields

−169.98

−125.98

−211.34

−14.49

Shrubbery

−28.26

−12.32

−0.21

−1.15

Water body

+229.47

+77.65

+42.04

+9.97

‘−’ decrease; ‘+’ increase

Implication for flood damage analysis

In this research, flood modeling has been performed by the Monte Carlo algorithm approach. Flood modeling scenario was created based on the annual flood for periods, namely: 2, 5, 10, 20, 50 and 100 years. The water level in the modeling has been created by scenarios Hc = 3 m and Hc = 5 m. Selection of the height flood inundation scenarios has been conducted based on observations of the real flood conditions in the field, which had occurred on January 15, 2014. The results showed that the height of the flood at the time of the incident almost reached the roof of the house settlements, which has a height of 3 m. One of these conditions can be shown in Fig. 7, which is heavily populated locations in Malendeng village. In addition, the height of the flood inundations has also reached about 5 m at the time of the flood event on January 15, 2014. These conditions can be shown in Fig. 8, which is one of the nearest settlement to the banks of the Tondano river. The results of the flood modeling with the return periods and the height flood inundation scenarios can be shown in Figs. 9 and 10.
Fig. 7

The height of flood inundation of 3 m at the time of the flood event on January 15, 2014. a During flood in Tikala river, Malendeng village. b Post flood in Tikala river, Malendeng village (photos from The Management Center of Tondano Watershed—Ministry of Forestry 2014)

Fig. 8

The height of flood inundation of 5 m at the time of the flood event on January 15, 2014. a During flood in Tondano river, Kairagi village. b Post flood in Tondano river, Kairagi village (photos from The Management Center of Tondano Watershed—Ministry of Forestry 2014)

Fig. 9

The results of flood inundation modeling using the Monte Carlo algorithm with the height flood scenario Hc = 3 m. a Flood scenario return period 2 years. b Flood scenario return period 5 years. c Flood scenario return period 10 years. d Flood scenario return period 20 years. e Flood scenario return period 50 year. f Flood scenario return period 100 years

Fig. 10

The results of flood inundation modeling using the Monte Carlo algorithm with the height flood scenario Hc = 5 m. a Flood scenario return period 2 years. b Flood scenario return period 5 years. c Flood scenario return period 10 years. d Flood scenario return period 20 years. e Flood scenario return period 50 years. f Flood scenario return period 100 years

The implementation of flood inundation modeling can be used to determine the flood damage assessment on LULC affected by flooding in the research area. This result can be predicted on the impact of floods in each LULC class for year 2015, 2025, 2035 and 2050. Thus, the impact of floods in the future can be predicted. The results of overlay between the flood inundation maps with the LULC maps have produced LULC information affected by floods, which can be presented in Fig. 11. Meanwhile, the results of the damage exposure estimation at each flood scenario that is calculated based on the market value per hectare in each LULC class can be presented in Tables 9, 10, 11, 12, 13 and 14. It can be shown that the total of flood damage exposure for the water level scenario Hc = 3 m with the return periods 2, 5, 10, 20, 50 and 100 years are more than €424, €438, €499, €509, €511 and €520 million, respectively. Meanwhile, for the water level scenario Hc = 5 m with the return periods 2, 5, 10, 20, 50 and 100 years are more than €915, €922, €924, €947, €957 and €958 million, respectively.
Fig. 11

The results of overlay between the flood inundation maps with the LULC maps have produced LULC information affected by floods in the research area. a Flood scenario return period 2 years. b Flood scenario return period 5 years. c Flood scenario return period 10 years. d Flood scenario return period 20 years. e Flood scenario return period 50 years. f Flood scenario return period 100 years

Table 9

The damage exposure estimation of the flood scenario return period 2 years and LULC model prediction in 2015, 2025, 2035 and 2050 (value in 1000 EUR)

LULC

Return period 2 years, Hc = 3 m

Return period 2 years, Hc = 5 m

2015

2025

2035

2050

2015

2025

2035

2050

Dry land

232

224

264

192

640

600

808

400

Bare soil

61

54

65

37

137

120

138

98

Plantation

2992

3472

3464

2784

6648

7528

7680

5896

Settlement

412,680

414,600

417,960

449,760

883,320

886,080

891,360

956,880

Paddy fields

8784

8520

7656

7720

24,240

23,896

22,000

22,080

Shrubbery

31

33

34

30

106

109

117

110

Total

424,780

426,903

429,443

460,523

915,091

918,334

922,103

985,464

Table 10

The damage exposure estimation of the flood scenario return period 5 years and LULC model prediction in 2015, 2025, 2035 and 2050 (value in 1000 EUR)

LULC

Return period 5 years, Hc = 3 m

Return period 5 years, Hc = 5 m

2015

2025

2035

2050

2015

2025

2035

2050

Dry land

240

224

256

200

768

696

952

440

Bare soil

58

51

62

35

139

118

139

114

Plantation

3040

3520

3528

2816

6904

7936

8112

6136

Settlement

426,360

427,680

431,760

462,960

889,560

891,000

893,400

955,440

Paddy fields

8704

8432

7632

7640

25,384

25,096

23,280

23,312

Shrubbery

26

29

28

26

112

117

125

116

Total

438,428

439,936

443,266

473,677

922,867

924,963

926,008

985,558

Table 11

The damage exposure estimation of the flood scenario return period 10 years and LULC model prediction in 2015, 2025, 2035 and 2050 (value in 1000 EUR)

LULC

Return period 10 years, Hc = 3 m

Return period 10 years, Hc = 5 m

2015

2025

2035

2050

2015

2025

2035

2050

Dry land

296

264

320

200

648

624

856

344

Bare soil

70

62

73

43

133

116

134

103

Plantation

3112

3616

3584

2912

6400

7328

7544

5688

Settlement

485,880

488,520

492,360

525,000

892,920

893,400

897,120

960,720

Paddy fields

10,008

9688

8816

8936

24,672

24,392

22,536

22,624

Shrubbery

32

34

34

32

100

104

113

104

Total

499,398

502,184

505,187

537,123

924,873

925,964

928,303

989,583

Table 12

The damage exposure estimation of the flood scenario return period 20 years and LULC model prediction in 2015, 2025, 2035 and 2050 (value in 1,000 EUR)

LULC

Return period 20 years, Hc = 3 m

Return period 20 years, Hc = 5 m

2015

2025

2035

2050

2015

2025

2035

2050

Dry land

296

264

336

208

704

648

880

416

Bare soil

71

62

74

45

146

128

146

111

Plantation

3216

3712

3704

3000

6480

7400

7640

5712

Settlement

496,320

498,120

501,600

538,680

914,400

916,560

921,480

987,240

Paddy fields

9616

9408

8456

8336

25,424

25,024

23,080

23,192

Shrubbery

33

35

36

32

106

112

120

111

Total

509,551

511,601

514,206

550,301

947,260

949,872

953,345

1,016,783

Table 13

The damage exposure estimation of the flood scenario return period 50 years and LULC model prediction in 2015, 2025, 2035 and 2050 (value in 1000 EUR)

LULC

Return period 50 years, Hc = 3 m

Return period 50 years, Hc = 5 m

2015

2025

2035

2050

2015

2025

2035

2050

Dry land

328

304

360

208

712

696

928

416

Bare soil

69

61

73

43

138

120

140

110

Plantation

2992

3480

3480

2792

7024

8032

8176

6200

Settlement

498,600

499,560

504,120

538,680

923,520

925,680

927,720

992,760

Paddy fields

9880

9680

8728

8768

26,368

26,016

24,080

24,272

Shrubbery

28

30

30

27

116

118

130

120

Total

511,897

513,115

516,790

550,518

957,878

960,662

961,174

1,023,878

Table 14

The damage exposure estimation of the flood scenario return period 100 years and LULC model prediction in 2015, 2025, 2035 and 2050 (value in 1000 EUR)

LULC

Return period 100 years, Hc = 3 m

Return period 100 years, Hc = 5 m

2015

2025

2035

2050

2015

2025

2035

2050

Dry land

296

288

376

208

720

672

912

424

Bare soil

70

61

71

45

147

129

147

111

Plantation

3144

3608

3592

2896

6880

7872

7984

6032

Settlement

506,040

506,880

512,760

547,080

923,880

925,920

929,280

997,440

Paddy fields

10,472

10,320

9240

9184

26,512

26,160

24,192

24,416

Shrubbery

33

35

37

32

117

120

132

121

Total

520,054

521,191

526,075

559,446

958,256

960,873

962,647

1,028,544

Discussion

This research has produced information related to the dynamics of LULC change modeling and their implication for the flood damage assessment. In this section, we will discuss related limitations, uncertainty and future direction for the use of methods that have been applied in the research area.

The results of LULC information that has been generated in this research, there are limitations. These limitations are related to the use of Markov-CA approach which does not include the parameters and factors that could affect or inhibit the occurrence of the LULC change in the research area. Thus, the resulting of LULC map updates in the next few years are assumed to be linear and the results are a scenario with a normally indication, without any triggering factor and inhibit the occurrence of the LULC change. This can be shown on difference in the average area change between the results of LULC classification in 1997–2015 with the results of LULC modeling in 2015–2050. Where, the average area change of the LULC classification in 1997–2015 is tending to relatively large. Meanwhile, the average area change of the LULC modeling in 2015–2050 is tending to relatively small. As an example for class of forest and paddy fields, the LULC classification in 1997–2015 had a rate of change: 121.82 and 108.13 ha/year, respectively. Meanwhile, the LULC modeling in 2015–2050 had a rate of change: 10.52, 14.49 ha/year, respectively. It can be used as input for future research in the research area to add the factors and parameters that can trigger factor and inhibit the occurrence of the LULC change. Thus, the information on the prediction and modeling LULC maps that will be able to provide good accuracy and precision (note: the result of the accuracy by using cross-correlation matrix between the LULC models in 2015 with the LULC reference in 2015 is 75.88 %), as has been done by several previous studies (e.g., Guan et al. 2011; Halmy et al. 2015)

The limitations of the flood inundation models were used in this research, namely: (a) modeling was performed on the segment of the Tondano river with a range point of downstream boundary to the point of the upper boundary Tondano river, (b) the Tikala river has been modeled as lateral inflow into Tondano river, (c) the existence of drainage, tributary and bridges in the segment of the Tondano river were ignored. These limitations have the same with the research created by Nanlohy et al. (2008). Map of flood inundation models that have been created by using the Monte Carlo approach presents a static and not dynamic modeling. In addition, the limitations of the resulting model can only present the distribution of flood inundation that is shown with a probability value at each scenario in the running. Where, at a value of 0 indicates a low probability and value of 1 indicates a high probability. Thus, the depth of flood information is not available in this model. However, the modeling can provide relatively quick benefits of generating flood inundation models. It is suitable for rapid mapping required during emergency response. The results of the flood inundation modeling have not been validated in this research. Thus, the field of measurement and observation for model validation is required and recommended in the future research.

Implementation of the uses of LULC and flood inundation modeling was estimated flood damage assessment in the research area. The process is done in a simple, namely: overlay between the flood inundation model with the LULC model that has been calculated for the next few years. Thus, the information of LULC affected by flooding can be determined in the research area. Estimates of flood damage exposure for the next few years has been calculated based on the asset value per hectare under current conditions in each LULC class. The results obtained by the flood scenario in extreme conditions for the return period of 100 year, the total of flood damage exposure can be estimated more than €520 million for the water level scenario Hc = 3 m and €958 million for the water level scenario Hc = 5 m. The market value of each class of LULC has not been done in detail. Thus, the limitations can be shown by the incorporation of market value in agricultural class (e.g., paddy fields, plantations and dry land) and the open area class (e.g., bare soil and shrubbery). It is necessary and recommended in the future research to focus in calculating the asset value for each LULC class in detail.

Conclusion

Markov-CA and Monte Carlo algorithm approaches have been successfully used in this research to create LULC and flood modelling, which can then be implemented to estimate the flood damage assessment for the next few years. The utilization of multi-temporal remotely sensed Landsat and ASTER GDEM have supported the acquisition of input data for modeling created by Markov-CA and Monte Carlo algorithm approaches. The results of LULC and flood modeling in this research could be used to support the mapping at a scale of 1:25,000–1:50,000. In the future research, it is advisable and recommended to use multi-temporal remotely sensed data with high resolution, such as Ikonos, Quickbird, Worldview, Pleiades and others. Thus, the need modeling for detailed mapping or less than scale 1:10,000 can be fulfilled. In addition, the use of stereo imagery from SPOT 6 and 7 with a spatial resolution of 6 m can be used for detailing topography information or DEM in the research area. The requirement of DEM data with a spatial resolution of less than 10 m is needed in flood modeling. Thus, the accuracy of the results of flood modeling can be improved and increased. The results of LULC modeling and their implication not only used for estimating the flood damage assessment, but also the results can be used to predict the flow discharge of flooding due to LULC changes for the next few years. Thus, in the future research can be suggested and recommended to predict the flow discharge of flooding due to LULC changes, and the results can be used as one of the stages in the flood mitigation effort in the research area.

Notes

Acknowledgments

This paper is a part of the research activities entitled “The utilization of remotely sensed data to support analysis of floods in Indonesia”. This research was funded by the budget of DIPA LAPAN activities in 2015, Remote Sensing Application Center, Indonesian National Institute of Aeronautics and Space (LAPAN). Thanks go to Dr. M. Rokhis Khomarudin (Director of Remote Sensing Application Center, LAPAN) who has supported the implementation of this research. Drs. Taufik Maulana, MBA., Dr. Wikanti Asriningrum and colleagues at the Remote Sensing Application Center, LAPAN for support, discuss and give suggestions in this research. Landsat 5 MSS and Landsat 7 TM images were provided by the U.S. Geological Survey (USGS). Landsat 8 LDCM images were provided by LAPAN. ASTER GDEM data were provided by the Japan Space Systems (Japan-US ASTER Science Team).

References

  1. Arsanjani JJ, Helbich M, Kainz W, Boloorani AD (2013) Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int J Appl Earth Obs Geoinf 21:265–275CrossRefGoogle Scholar
  2. Beckers A, Dewals B, Erpicum S, Dujardin S, Detrembleur S, Teller J, Pirotton M, Archambeau P (2013) Contribution of land use changes to future flood damage along the river Meuse in the Walloon region. Nat Hazards Earth Syst Sci 13:2301–2318CrossRefGoogle Scholar
  3. Behera MD, Borate SN, Panda SN, Behera PR, Roy PS (2012) Modelling and analyzing the watershed dynamics using Cellular Automata (CA)-Markov model—a geo-information based approach. J Earth Syst Sci 121:1011–1024CrossRefGoogle Scholar
  4. Chavez PS Jr (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24:459–479CrossRefGoogle Scholar
  5. Chen X, Yu SX, Zhang YP (2013) Evaluation of spatio temporal dynamics of simulated land use/cover in China using a probabilistic Cellular Automata-Markov model. Pedosphere 23(2):243–255CrossRefGoogle Scholar
  6. Cihlar J (2000) Land cover mapping of large areas from satellites: status and research priorities. Int J Remote Sens 21:1093–1114CrossRefGoogle Scholar
  7. Dayantolis W, Fitri HT (2014) Overview climatological flooding on January 15, 2014 in Manado. Category Archives: Manado. https://bencanasulut.wordpress.com/category/manado. Accessed 08 Jan 2015 (in Indonesian)
  8. Felpeto A (2009) VORIS a GIS-based tool for volcanic hazard assessment. User’s Guide Version 2.0.1Google Scholar
  9. Felpeto A, Matri J, Ortiz R (2007) Automatic GIS-based system for volcanic hazard assessment. J Volcanol Geotherm Res 166:106–116CrossRefGoogle Scholar
  10. Gong W, Yuan L, Fanc W, Stott P (2015) Analysis and simulation of land use spatial pattern in Harbin prefecture based on trajectories and cellular automata—Markov modelling. Int J Appl Earth Obs Geoinf 34:207–216CrossRefGoogle Scholar
  11. Guan D, Li HF, Inohae T, Su W, Nagaie T, Hakao K (2011) Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol Model 222:3761–3772CrossRefGoogle Scholar
  12. Hall R, Wilson MEJ (2000) Neogene sutures in eastern Indonesia. J Asian Earth Sci 18(6):781–808 CrossRefGoogle Scholar
  13. Halmy MWA, Gessler PE, Hicke JA, Salem BB (2015) Land use/land cover change detection and prediction in the northwestern coastal desert of Egypt using Markov-CA. Appl Geogr 63:101–112CrossRefGoogle Scholar
  14. Herath (2003) Flood damage estimation of an urban catchment using remote sensing and GIS. International Training Program on Total Disaster Risk ManagementGoogle Scholar
  15. Huang SL, Wang SH, Budd WW (2009) Sprawl in Taipei’s peri-urban zone: responses to spatial planning and implications for adapting global environmental change. Landsc Urban Plan 90(1–2):20–32CrossRefGoogle Scholar
  16. Indonesian of Central Agency Statistics (BPS) (2014) Statistics of Minahasa DistrictGoogle Scholar
  17. Indonesian of Central Agency Statistics (BPS) (2014) Statistics of Minahasa Utara DistrictGoogle Scholar
  18. Indonesian of Central Agency Statistics (BPS) (2014) Statistics of Tomohon DistrictGoogle Scholar
  19. Indonesian of Central Agency Statistics (BPS) (2014) Statistics of Manado DistrictGoogle Scholar
  20. Joling RJ (2013) Adding more detail to potential flood damage assessment: An object based approach. Thesis BSc Aarde and Economie. Vrije Universiteit Amsterdam Faculteit Aard-en LevenswetenschappenGoogle Scholar
  21. Jonge T, Kok M, Hogeweg M (1996) Modelling floods and damage assessment using GIS. HydroGIS 96: application of geographic information systems in hydrology and water resources management. In: Proceedings of the Vienna Conference. IAHS Publ 235Google Scholar
  22. Jongman B, Kreibich H, Apel H, Barredo JI, Bates PD, Feyen L, Gericke A, Neal J, Aerts JCJH, Ward PJ (2012) Comparative flood damage model assessment: towards a European approach. Nat Hazards Earth Syst Sci 12:3733–3752CrossRefGoogle Scholar
  23. Kalyanapu AJ (2011) Monte Carlo based flood risk analysis using a graphics processing unit-enhanced two-dimensional flood model. Dissertation. Department of Civil and Environmental Engineering. The University of UtahGoogle Scholar
  24. Lambin EF (1997) Modelling and monitoring land-cover change processes in tropical regions. Prog Phys Geogr 21:375–393CrossRefGoogle Scholar
  25. Luca C, Michele M, Silvia M (2013) Investigating the relationship between land cover and vulnerability to climate change in Dares Salaam. Working Paper, Rome: Sapienza UniversityGoogle Scholar
  26. Marfai MA, King L (2007) Tidal inundation mapping under enhanced land subsidence in Semarang, Central Java Indonesia. Nat Hazards 44:93–109CrossRefGoogle Scholar
  27. Metzler SM (2011) Land use interpretation in flood damage estimation. Master’s Theses and Graduate Research, San Jose State University. Paper 4103Google Scholar
  28. Mousivand AJ, Sarab AA, Shayan S (2007) A new approach of predicting land use and land cover changes by satellite imagery and Markov chain model (case study: Tehran). MSc Thesis. Tarbiat Modares University, Tehran, IranGoogle Scholar
  29. Nanlohy BJB, Jayadi R, Istiarto (2008) The study of flood control alternatives Tondano river in Manado city. Forum civil engineering XVIII:756–767 (in Indonesian) Google Scholar
  30. NASA (2011) Landsat 7 science data users handbook. Maryland: Landsat Project Science Office at NASA’s Goddard Space Flight Center in GreenbeltGoogle Scholar
  31. Nejadi A, Jafari HR, Makhdoum MF, Mahmoudi M (2012) Modeling plausible impacts of land use change on wildlife habitats, application and validation: lisar protected area, Iran. Int J Environ Res 6(4):883–892Google Scholar
  32. Pouliot D, Latifovic R, Zabcic N, Guindon L, Olthof I (2014) Development and assessment of a 250 m spatial resolution MODIS annual land cover time series (2000–2011) for the forest region of Canada derived from change-based updating. Remote Sens Environ 140:731–743CrossRefGoogle Scholar
  33. Richards JA, Jia X (2006) Remote sensing digital image analysis: an introduction. Springer, BerlinGoogle Scholar
  34. Rogan J, Chen D (2004) Remote sensing technology for mapping and monitoring land-cover and land-use change. Prog Plan 61:301–325CrossRefGoogle Scholar
  35. Seniarwan Baskoro DPT, Gandasasmita K (2013) Spatial modelling of flood inundation: case study Mangottong river area, Sinjay Regency, South Sulawesi Province. Sci J Globe 15(1):62–67 (in Indonesian) Google Scholar
  36. Shooshtari SJ, Gholamalifard M (2015) Scenario-based land cover change modeling and its implications for landscape pattern analysis in the Neka Watershed, Iran. Remote Sens Appl Soc Environ 1:1–19Google Scholar
  37. Smemoe CM, Nelson EJ, Zundel AK, Miller W (2007) Demonstrating floodplain uncertainty using flood probability maps. J Am Water Resour Assoc 43(2):359–371CrossRefGoogle Scholar
  38. Sompotan AF (2012) Gological stucture of Sulawesi Struktur Geologi Sulawesi. Earth Sciences Library. Bandung Institute of Technology (ITB) (in Indonesian) Google Scholar
  39. Stürck J, Schulp CJE, Verburg PH (2015) Spatio-temporal dynamics of regulating ecosystem services in Europe e The role of past and future land use change. Appl Geogr 63:121–135CrossRefGoogle Scholar
  40. Su MD, Kang JL, Chang LF, Chen AS (2005) A grid-based gis approach to regional flood damage assessment. J Mar Sci Technol 13(3):184–192Google Scholar
  41. Sun H, Forsythe W, Waters N (2007) Modeling urban land use change and urban sprawl: Calgary, Alberta, Canada. Netw Spat Econ 7(4):353–376CrossRefGoogle Scholar
  42. Sylvertown J, Hotlier S, Johnson J, Dale P (1992) Cellular automaton models of inter specific competition for space the effect of pattern on process. J Ecol 80:527–534CrossRefGoogle Scholar
  43. The Management Center of Tondano Watershed—Ministry of Forestry (2014) Characteristics of Tondano watershed. Final report (in Indonesian) Google Scholar
  44. Thomas H, Laurence HM (2006) Modeling and projecting land-use and land-cover changes with a cellular automaton in considering landscape trajectories: An improvement for simulation of plausible future states. EARSeLeProc 5:63–76Google Scholar
  45. USGS (2013) Landsat missions: frequently asked questions about the Landsat missions US Geological Survey. Last modified: 5/30/123. Landsat.usgs.govGoogle Scholar
  46. USGS (2013) Using the US Geological survey Landsat 8 product. Last modified: 5/30/123. Landsat7.usgs.govGoogle Scholar
  47. Wang X, Zhang C (2001) A dynamic modelling approach to simulating socioeconomic effects on landscape changes. Ecol Model 140:141–162CrossRefGoogle Scholar
  48. Wang SQ, Zheng XQ, Zang XB (2012) Accuracy assessments of land use change simulation based on Markov-cellular automata model. Proc Environ Sci 13:1238–1245CrossRefGoogle Scholar
  49. Ward PJ, Marfai MA, Yulianto F, Hizbaron DR, Aerts JCJH (2010) Coastal inundation and damage exposure estimation: a case study for Jakarta. Nat Hazards 56:899–916CrossRefGoogle Scholar
  50. Ward PJ, Moel H, Aerts JCJH (2011) How are flood risk estimates affected by the choice of return-periods? Nat Hazards Earth Syst Sci 11:3181–3195CrossRefGoogle Scholar
  51. Wehmann A, Liu D (2015) A spatial–temporal contextual Markovian kernel method for multi-temporal land cover mapping. ISPRS J Photogramm Remote Sens 107:77–89CrossRefGoogle Scholar
  52. Wu Q, Li H, Wang R, Paulussen J, He Y, Wang M et al (2006) Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landsc Urban Plan 78:322–333CrossRefGoogle Scholar
  53. Yan WY, Shaker A, El-Ashmawy N (2015) Urban land cover classification using airborne LiDAR data: a review. Remote Sens Environ 158:295–310CrossRefGoogle Scholar
  54. Yang H, Du L, Guo H, Zhang J (2011) Tai’an land use analysis and prediction based on RS and Markov model. Proc Environ Sci 10:2625–2630CrossRefGoogle Scholar
  55. Yang X, Zheng XC, Chen R (2014) A land use change model: integrating landscape pattern indexes and Markov-CA. Ecol Model 283:1–7CrossRefGoogle Scholar
  56. Yen NTM, Anh TV (2010) Application of remote sensing data for mapping of damage assessment of flood to the land cover, an experiment in Phu Yen province, Vietnam. In: International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied SciencesGoogle Scholar
  57. Yulianto F, Sofan P, Zubaidah A, Sukowati KAD, Pasaribu JM, Khomarudin MR (2015a) Detecting areas affected by flood using multi-temporal ALOS PALSAR remotely sensed data in Karawang, West Java, Indonesia. Nat Hazards 77:959–985CrossRefGoogle Scholar
  58. Yulianto F, Tjahjono B, Anwar S (2015b) The applications of Monte Carlo algorithm and energy con model to produce the probability of block-and-ash flows of the 2010 eruption of Merapi volcano in Central Java, Indonesia. Arab J Geosci 8:4717–4739CrossRefGoogle Scholar
  59. Zhu J (2010) GIS based urban flood inundation modeling. Second WRI Glob Congr Intell Syst 2:140–143Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Fajar Yulianto
    • 1
  • Indah Prasasti
    • 1
  • Junita Monika Pasaribu
    • 2
  • Hana Listi Fitriana
    • 1
  • Zylshal
    • 1
  • Nanik Suryo Haryani
    • 1
  • Parwati Sofan
    • 1
  1. 1.Remote Sensing Application CenterIndonesian National Institute of Aeronautics and Space (LAPAN)JakartaIndonesia
  2. 2.Environmental Agency of Riau Islands Province (BLH)TanjungpinangIndonesia

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