ArticlePDF Available

Abstract and Figures

Lombok Island is categorized as a small island with an area o f 4738.7 km2 which makes it susceptible to climate change. Climate change is thought to have reduced the ability of forest biophysical and can cause damage to the ecosystem or a shifting of forest ecosystems. The purpose of this study is to identify the ecosystem changes in Lombok Island based on historical climate data and also to identify the vegetation domination based on ground truth result. Research study area covers the entire area of Lombok Island, West Nusa Tenggara. Data analysis was performed using Geographic Information Systems (GIS) to analyze the spatial distribution of ecosystem zones. Spatial data processing was performed by interpolation method to estimate values at unknown locations or adjacent points. Ecosystem types in this study were determined by Holdridge Life Zones which are generated from historical climate data from 1975 to 2012 and described by the time series map of 1975, 1995, and 2012. The result indicates that ecosystem change occurred in Lombok Island which has shown in time series map of ecosystem zone. There were only five life zones in 2012 which were tropical moist forest, tropical dry forest, tropical very dry forest, subtropical wet forest and subtropical moist forest. The tropical dry forest was seen to dominate the area of Lombok Island in 2012. The ground truth result shown that the vegetation domination matches each ecosystem zone in the recent time.
Content may be subject to copyright.
Procedia Environmental Sciences 24 ( 2015 ) 165 173
1878-0296 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Selection and peer-review under responsibility of the LISAT-FSEM Symposium Committee
doi: 10.1016/j.proenv.2015.03.022
Available online at www.sciencedirect.com
ScienceDirect
The 1st International Symposium on LAPAN-IPB Satellite for Food Security and Environmental
Monitoring
Geospatial approach for ecosystem change study of Lombok Island
under the influence of climate change
Saputri Saptaa,*, Bambang Sulistyantarab, Indung Sitti Fatimahb, Akhmad Faqihc
*
aDepartment of Landscape Architecture, Bogor Agricultural University Graduate School, Kampus IPB Darmaga, Bogor 16680,
Indonesia
bDepartment of Landscape Architecture, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 16680, Indonesia cDepartment of
Geophysics and Meteorology, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 16680, Indonesia
Abstract
Lombok Island is categorized as a small island with an area o f 4738.7 km2 which makes it susceptible to climate change.
Climate change is thought to have reduced the ability of forest biophysical and can cause damage to the ecosystem or a shifting
of forest ecosystems. The purpose of this study is to identify the ecosystem changes in Lombok Island based on historical climate
data and also to identify the vegetation domination based on ground truth result. Research study area covers the entire area of
Lombok Island, West Nusa Tenggara. Data analysis was performed using Geographic Information Systems (GIS) to analyze the
spatial distribution of ecosystem zones. Spatial data processing was performed by interpolation method to estimate values at
unknown locations or adjacent points. Ecosystem types in this study were determined by Holdridge Life Zones which are
generated from historical climate data from 1975 to 2012 and described by the time series map of 1975, 1995, and 2012. The
result indicates that ecosystem change occurred in Lombok Island which has shown in time series map of ecosystem zone. There
were only five life zones in 2012 which were tropical moist forest, tropical dry forest, tropical very dry forest, subtropical wet
forest and subtropical moist forest. The tropical dry forest was seen to dominate the area of Lombok Island in 2012. The ground
truth result shown that the vegetation domination matches each ecosystem zone in the recent time.
© 2015 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of the LISAT-FSEM Symposium Committee.
Keywords: climate change; ecosystem change; Geographic Information Systems (GIS); Holdridge Life Zones
* Corresponding author. Tel.: +62 899 954 4559; fax: +62 251 863.
E-mail address: saputri.sapta@gmail.com.
© 2015 The Authors. Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Selection and peer-review under responsibility of the LISAT-FSEM Symposium Committee
166 Saputri Sapta et al. / Procedia Environmental Sciences 24 ( 2015 ) 165 – 173
1. Introduction
1.1. Background
Ecosystems of the Lombok Island are very interesting to study considering the beauty of coastal ecosystems in
the South and mountain ecosystems with the Mount Rinjani dominates the West of Lombok Island. In addition,
Lombok Island is a border region between the flora and fauna of Asia and Australia and form unique ecosystem
diversity. However, Lombok Island is categorized as a small island with an area of 4738.7 km2 which makes it
susceptible to climate change. Climate change is thought to have reduced the ability of forest biophysical and can
cause damage to the ecosystem or a shift of forest ecosystems.
There has been a research on climate change on ecosystem zones in Lombok Island which has shown the shifted
climate types in the period 1961-2008 [1]. However, in that study, the climate data obtained only from
meteorological stations of Selaparang and Kediri, thus the distribution of climate zones and ecosystems is not very
representative of actual conditions. Therefore, further study is needed using a geospatial approach to see the
distribution of climate zones and ecosystems of Lombok Island.
This previous study showed that there have been some changes in Lombok Island on its climate which is
characterized by the changing trend of rainfall, temperature and climate types. The impacts of climate change on
forest ecosystems include mangrove forest ecosystem devastation, loss of endemic species, decreased in land cover,
as well as reduced quality and quantity of springs. Therefore, climate change information and the resulting impacts
need to be updated to support the optimization of adaptation and mitigation of climate change so as to reduce the risk
of ecological damages.
1.2. Objective
The purpose of this study is to determine the ecosystem zones spatially and identify the ecosystem change in
historical data in Lombok Island based on the climate historical data. The results of this study are expected to be the
basis of the data that is important to formulate mitigation and adaptation strategies to deal with the clim ate change,
particularly on the Lombok Island landscape.
1.3. Problem Statement
This research was done by analyze the climate data and classify the ecosystem zones. To determine the result of
the research there will be some problems which are stated below:
1. How to classify the ecosystem of an area?
2. How are the changes of the ecosystem zone in the Lombok Island?
3. How was the climate change affect to the ecosystem zone in the Lombok Island?
2. Methodology
2.1. Study Area
Research study area covers the entire area of the island of Lombok, West Nusa Tenggara (Figure 1). Field
observations and ground truthing will be conducted on Lombok Island at some points in the region in accordance
with the results of the determination of the sampling area will be undertaken ahead of the field observations. While
the analysis and synthesis will be performed in Bogor, West Java.
167
Saputri Sapta et al. / Procedia Environmental Sciences 24 ( 2015 ) 165 – 173
Fig. 1. Areas of research, Lombok - NTB (Source: Google Earth)
2.2. Research Framework
The scope of this study is limited to landscape change of Lombok Island using historical climate data from 1975
to 2012. Figure 2 shows that data collecting process covers observation tabular data from climate station and also
base map to be used in data processing and analyzing with GIS approach. GIS approach is used to obtain bio-
temperature and precipitation data which are required for the ecosystem zones classification process. Ecosystem
zones to be classified using Holdridge Life Zones method as it correlates to climate factor which would be explained
in the next section (subchapter 2.3). Ground truthing is conducted to validate the result derived from this study to
verify the current existing condition with the description obtained from analysis result.
Fig. 2. Workflow diagram of the research
2.3. Ecosystem Zones Classification
Ecosystem zones were determined using Holdridge Life Zones classification system to assess the impact of
climate change on ecosystem zones. The Holdridge Life Zones system correlates climatic indices with 37 life zones
ranging from polar desert to wet tropical rainforest. It uses two main variables in determining classification, average
biotemperature and average annual precipitation [2].
The average rainfall annually (mm) is used for precipitation data . Biotemperature (BT) is a unit of measurement
of energy used in the life zone chart where this unit shows the average value of the air temperature in Celsius is used
for growing crops. This temperature range is between 0°C as the minimum point and 30°C as the maximum point
(0°C <T <30°C). The ratio of annual potential evapotranspiration as the third variable is a function of biotemperature
and precipitation. Therefore, this is not required as input for the life-zone classification scheme [3].
Holdridge Life Zones diagram (Figure 3) is a graphical classification of zone ecosystems on earth that shows the
relationship of the mountains and lowland vegetation based on latitude, elevation, precipitation and air temperature
[2]. Classification zones are a rough model that can predict potential forest types that can grow optimally in regions
with certain climatic conditions. This diagram is formed using two identical axes for average annual precipitation to
168 Saputri Sapta et al. / Procedia Environmental Sciences 24 ( 2015 ) 165 – 173
make up two sides of an equilateral triangle. The third side of the triangle is a logarithmic axis for potential
evapotranspiration (PET) ratio measured in millimeters per year (mm/yr). Axes for mean annual biotemperature are
set to the base of the triangle [4].
Since the number of surface observation station for temperature database availability is inadequate, temperature
data should be derived from DEM (satellite image) and will be references to existing data point from surface
observation of Meteorology Climatology and Geophysics Agency (BMKG). Meanwhile, the precipitation data
collected from 33 rainfall observation station of Sub Directorate of Hydrology and Water Quality.
Fig. 3. Holdridge Life Zones classification system [2]
3. Results and Discussion
3.1. Data Analysis
Data analysis was performed using Geographic Information Systems (GIS) to analyze the spatial distribution of
climate zones and ecosystems to facilitate interpretation from these data. Spatial data processing was done by
interpolation to estimate values at unknown locations or adjacent points [5]. Ecosystem zones are determined using
Holdridge Life Zones method with historical climate data from 1975 to 2010. Climate data used in this study
consists of biotemperature and rainfall data. Temperature data derived from DEM data analysis to create a
biotemperature map, while areal rainfall map obtained from the analysis of Thiessen Polygon.
Holdridge [2] proposed a life zone classification to predict the potential vegetation of a region for values of
climatic indices. Life zones are delimited by bio-temperature, precipitation, and potential evaporation ratio. In
particular, the system is based on two factors: mean annual biotemperature and mean annual precipitation [6]. Based
upon study of several ecosystems, Holdridge assumes that the potential evaporation ratio (PET) is proportional to
biotemperature with a proportionality constant of 58.93. PET is therefore not an independent variable but simply
derived from the two primary variables of precipitation and biotemperature [4].
169
Saputri Sapta et al. / Procedia Environmental Sciences 24 ( 2015 ) 165 – 173
Spatial data processing on this study, as described on figure 4, consists of climate data analysis to create the
climatic map which then can be used to perform the ecosystem zone analysis based on Holdridge Life Zones
classification system. Result was validated with ground truthing to verify the recent existing ecosystem.
Fig. 4. Spatial data processing scheme
3.1.1. Base Map
Data used for spatial processing analysis on this study include the administration map, SRTM data, and climate
observation station map of Lombok Island. Administration map is used to provide the area identification, and also to
clip the other spatial maps according to the boundary of Lombok Island. SRTM data used are in ARC GRID, ARC
ASCII and Geotiff format with 6000 x 6000 pixels, in decimal degrees and datum WGS84. Climate observation
stations are shown on the map according to its coordinate which then can be included the historical climate data
information attribute for each station point.
3.1.2. Biotemperature
Biotemperature defined as the mean of unit-period temperatures with substitution of zero for all temperature
values below 0°C and above 30°C. On this study, air temperature was estimated by using Braak equation. The
higher the elevation, the lower is the air temperature [7]:
T = 26,3 °C - (0,01 x elevation in meter asl x 0,6 C)
The average air temperature at zero elevation (coast) ranges from 25 to 27°C [8].
DEM analysis was used to create a biotemperature map based on the temperature data from observation stations
combined with elevation data of the area. Digital Elevation Models (DEM) are 3-dimensional representations of the
170 Saputri Sapta et al. / Procedia Environmental Sciences 24 ( 2015 ) 165 – 173
topography of landscapes and can be used to generate information relevant to hydrological, geomorphological,
biological and other environmental applications [9] [10]. Topography often overlooked because of its influence on
climate. DEM data was derived from the USGS/NASA SRTM data [11], while the historical temperature data
derived from four climatology and meteorology stations of Meteorology Climatology and Geophysics Agency
(BMKG). As the climate data from those stations were inadequate to describing the whole temperature condition,
therefore this study fetched the data from other area by using Braak formula.
Evaluating the fetched data to the existing one were done by linear regression method, the main purpose of this
step is to enhance the scope of data Regression analysis is a statistical method that investigates the relationship
between a response variable Y and a set of other variables named as independent variables or predictors X [12]..
Linear regression is conducted by comparing between surface observations data with data derived from Braak
equation. Regression equation result is used to correct biotemperature data which has been generated from DEM
data. The map below (Figure 5) is the example of biotemperature map which uses the surface observation data of
2000 since its data availability is the most sufficient for the area coverage.
Fig. 5. Biotemperature map of Lombok Island in 2000
3.1.3. Precipitation
Data limitation both from the surface observation station and satellite imagery enforces this study to use Thiessen
Polygon method although the accuracy of this method has not been adequate. However, Thiessen Polygon are best
used at an early stage in a spatial study to sort out visually confusing site distributions into patterns that are str ong
enough to warrant further study.
Rain gauges generally measure rainfall at individual points but can be compared directly with runoff from that
area to obtain the average depth of rainfall occurring over an area which is called as the areal rainfall. The Thiessen
polygon method accounts for the variability in spatial distribution of gauges and the consequent variable area which
teach gauge represents. The area of a polygon for an individual station as a proportion of the total basin area
represents the Thiessen weight for that station [13]. The Thiessen polygon method is objective and readily
computerized which makes it as a widely-used method.
Interpolation refers to the process of estimating the unknown data values for specific locations using the known
data values for other points. Thiessen polygons are constructed by around each sample point. All points within a
polygon are assumed to have the same data value as the sample point around which polygon is constructed [14].
Thiessen polygon analysis for this study was generated on GIS using rainfall observation point of study area which
Legend
Biotemperature range (in °C)
171
Saputri Sapta et al. / Procedia Environmental Sciences 24 ( 2015 ) 165 – 173
then created the areal rainfall map of Lombok Island (Figure 6). This example of rainfall map used the surface
observation data of 2000 since it has the most complete number of surface observation stations which provide the
rainfall data to show the distribution of the total observation stations located in study area.
Fig. 6. Areal rainfall map of Lombok Island in 2000
3.1.4. Ecosystem Zone Classification
Combination between biotemperature and precipitation index value create 37 ecosystem zones which called as
life-zones. Biotemperature index and precipitation index are determined based on value category of each life-zone
which is referred to Holdridge Life Zones classification scheme. Although regional section of humidity and
altitudinal belts cutting across regions have some natural relations, the latitudinal biotemperature regions are seem to
be natural geographical units [2]. The preliminary result of ecosystem zone map which displays the life zone
classification over an area is showed through three years (1975, 1995, and 2012). This spatial information shows
that Lombok Island has 10 types of life zones, such as subtropical thorn steppe/woodland, subtropical dry forest,
subtropical moist forest, subtropical wet forest, subtropical rain forest, tropical thorn wood, tropical very dry forest,
tropical dry forest, tropical moist forest, and tropical wet forest (Figure 7).
In 1975, there are seven life zones such as tropical wet forest, tropical moist forest, tropical dry forest, tropical
very dry forest, subtropical rain forest, subtropical wet forest, and subtropical moist forest. In 1995, it changed to
eight life zones which are tropical moist forest, tropical dry forest, tropical very dry forest, tropical thorn woodland,
subtropical wet forest, subtropical moist forest, subtropical dry forest, and subtropical thorn steppe/woodland. The
tropical wet forest and subtropical rain forest life zones were no longer exist in 1995. There were only five life zones
in 2012 which are tropical moist forest, tropical dry forest, tropical very dry forest, subtropical wet forest and
subtropical moist forest. The tropical dry forest was seen to dominate the area of Lombok Island in 2012. The
ground truth check result verified that the vegetation domination in Lombok area are dominated by grasses,
groundcovers, shrubs, scrubs, and short trees which could indicate a dry forest. There are plenty of grasslands and
shrublands found within this area. Nevertheless, some rain forest ecosystems are found in the center of the island.
The significant changes from 1975 to 2012 caused by the changes of the precipitation and biotemperature values.
But, there were also heterogeneity of the climate data attributes from each observation station which make the
Legend
172 Saputri Sapta et al. / Procedia Environmental Sciences 24 ( 2015 ) 165 – 173
visible changes become biased. It is necessary to improve the data availability from each observation station by
conducting a further data inventory.
Fig. 7. Ecosystem zones map of Lombok Island in 1975 (a), 1995 (b), and 2012 (c) based on Holdridge Life Zones classification.
3.2. Discussion
Former research of ecosystem change study in Lombok Island by Rieke Nandini and Budi Narendra [1] has
shown the shifted climate types in the period 1961-2008. The climate data of the study obtained only from
meteorological stations of Selaparang and Kediri, which makes distribution of climate zones and ecosystems is less
representative to describe actual conditions.
Yates et al. [3] compared the Holdridge Life Zones model as a climate-vegetation model to three mechanistic
simulation models (BIOME2, Dynamic Global Phytogeography Model (DOLY), and Mapped Atmosphere-Plant-
Soil System (MAPSS)) for the conterminous United States under contemporary climate and a set of future climates
prescribed by three Global Circulation Model experiments. It showed that under circumstances of limited data
availability, computation resources, and access to mechanistic models and model expertise, simple correlation
models such as Holdridge may be the only method that can be applied.
Lasco et al. [15] figured out that temperature increase had minimal effect on life zones in the Philippines, because
all parts of the country already fall within the tropical belt under the Holdridge system (>24°C). However, specific
forest type could change dramatically with increases in precipitation levels.
4. Conclusions
The preliminary result shows that the ecosystem zone of Lombok Island has been varied from 1975 to 2012
according to its climatic data which are subjects to climate change. The range of ecosystem zone of Lombok Island
from 1975-2012 includes tropical wet forest, tropical moist forest, tropical dry forest, tropical very dry fore st,
173
Saputri Sapta et al. / Procedia Environmental Sciences 24 ( 2015 ) 165 – 173
tropical thorn woodland, subtropical rain forest, subtropical wet forest, subtropical moist forest, subtropical dry
forest, and subtropical thorn steppe/woodland. The tropical dry forest is seen to dominate the area of Lombok Island
in 2012.
It is recommended for the future studies will look at how climate change affect the ecosystem in the future using
climate scenarios and also to show trends of climate change that has occurred from precipitation and biotemperature
values.
References
1.
R. Nandini and B. Narendra , "Kajian Perubahan Curah Hujan, Suhu, dan Tipe Iklim pada Zona Ekosistem di Pulau Lombok," Jurnal
Analisis Kebijakan Kehutanan, 2011; 3(8): 228-244,.
2.
L. Holdridge, Life Zone Ecology, San Jose, Costa Rica: Tropical Science Center; 1967.
3.
D. Yates, T. Kittel and Cannon RF, "Comparing the correlative Holdridge model to mechanistic biogeographical models for assessing
vegetation distribution response to climatic change. Climatic Change 2000;44:59-87.,"
4.
C. Thomas, "Global Warming and World Ecosystem Distribution: Toward Quantifying Ecosystem Change," Massachusetts Institute of
Technology, Massachusetts; 1993.
5.
A. As-Syakur, "Evaluasi zona agroklimat dari klasifikasi Schmidt-Ferguson menggunakan aplikasi Sistem Informasi Geografi (SIG)," Jurnal
Pijar MIPA, 2009; 3(1):17-22,.
6.
T. C. N. K. ML Parry, The Impact of Climatic Variations on Agriculture, Volume 2. Assessments in Semi-Arid Areas, Kluwer: Dordrecht;
1988.
7.
C. Braak, The Climate of The Netherlands Indies. Proc. Royal Mogn. Meteor. Observ. Batavia; 1928.
8.
S. Ritung, W. F. Agus and H. Hidayat, Land Suitability Evaluation with a Case Map of Aceh Barat district, Bogor, Indonesia: Indonesian Soil
Research Institute and World Agroforestry Centre; 2007.
9.
S. Jenson and J. Domingue, "Extracting topographic structure from digital elevation data for geographic information system analysis,"
Photogrammetric Engineering and Remote Sensing, 1988;54:1593-1600,.
10.
M. Hutchinson and J. Gallant, "Representation of terrain," in Geographical Information Systems: Principles, Technical Issues, Management
Issues and Applications. Second Edition, New York, Wiley, 1999:105-124.
11.
A. Jarvis, H. Reuter, A. Nelson and E. Guevara, Hole-filled SRTM data Version 4, available from CGIAR-CSI SRTM 90m Database
(http://srtm.csi.cgiar.org), International Centre for Tropical Agriculture (CIAT); 2008.
12.
A. A. Smadi and N. H. Abu-Afouna, "On Least Squares Estimation in a Simple Linear Regression Model with Periodically Correlated Errors:
A Cautionary Note," Austrian Journal of Statistics,. 2012;41( 3): 211-226,.
13.
Hydrology Project Training Module, How to compile rainfall data, New Delhi: Hydrology Project Technical Assistance; 2002.
14.
P. A. Burrough and R. A. McDonnell, Principles of Geographical Information Systems, New York: Oxford University Press; 1998.
15.
R. Lasco, S. Roy, P. Sanchez and K. Garcia, Modeling of Climate Change on Transition of Forest types in the Philippines using the
Holdridge Life Zones and GCM Projections for Southeast Asia., in press; 2007.
16.
W. Taesombat and N. Sriwongsitanon, "Areal rainfall estimation using spatial interpolation," ScienceAsia, 2009; 35: 268-275,.
17.
N. Estwick, "GIS as a Tool for Rainfall Analysis," in Esri UC, San Diego; 2011.
18.
M. Hall, Archaeology Africa, Oxford: James Currey Publishers; 1996.
... Lombok Island, Indonesia, is a habitat for tropical biodiversity. It is categorized as a small island with an area of 4738.7 km 2 (Sapta et al. 2015). The biogeography of Lombok Island belongs to the Wallacea region located between the Sunda and Sahul Shelves, with the Wallace and Lydekker lines as boundaries. ...
... Thus, flora and fauna in this region are a transition between Southeast Asia and Australia. Sapta et al. (2015) suggested that Lombok Island is a border region between flora and fauna of Asia and Australia and form unique ecosystem diversity. As a tropical Island in the Wallacea region, Lombok has a wide variety of endemic flora and fauna with several types of habitats in tropical rainforests and orchards. ...
Article
Full-text available
Hudiwaku S, Himawan T, Rizali A. 2021. Diversity and species composition of fruit flies (Diptera: Tephritidae) in Lombok Island, Indonesia. Biodiversitas 22: 4608-4616. Fruit flies (Diptera: Tephritidae) are pests of several horticultural crops that can reduce the quality and quantity of fruit production. Information on fruit flies in Lombok Island, Indonesia, is still limited. However, it is predicted to have a high diversity of fruit flies because this island belongs to the Wallacea region. The aims of this research was to study the diversity and species composition of fruit flies in different habitat types in Lombok Island. The research was carried out on two habitat types, i.e., tropical rainforest and orchard with each habitat type consisted of three different sites that located spread across Lombok Island as replication. The research was carried out on two habitat types, i.e., tropical rainforest and orchard, with each habitat type consisted of three different sites spread across Lombok Island as replication. A sampling of fruit flies was conducted using parapheromone traps from March to June 2020. Twenty-two species and 210,267 individual fruit flies were collected from all locations during the study period. The most dominant species were Bactrocera carambolae, Bactrocera limbifera, Zeugodacus caudatus, and Bactrocera dorsalis. Based on the ANOVA, habitat types significantly affected the abundance but not the species richness of fruit flies. The visualization results obtained from the NMDS ordination indicated a difference in the species composition of fruit flies between the two habitats. In conclusion, habitat types are an essential factor in shaping the community of fruit flies in Lombok Island.
... The combination of bio-temperature and precipitation values creates 37 ecosystem zones called life zones. Bio temperature index and precipitation index were determined based on the value category of each life zone, which was referred to as the Holdridge Life Zones classification scheme (Sapta et al., 2015). The preliminary ecosystem zone map shows the life zone classification in the study area for 2010 and 2020. ...
Article
Full-text available
Reliable estimates of how human activities may affect wildlife populations are critical for making scientifically sound resource management decisions. A significant issue in estimating the consequences of management, development, or conservation measures is the need to account for a variety of biotic and abiotic factors, such as land use and climate change, that interact over time altering wildlife habitats and populations. The snow leopard Panthera uncia (Schreber, 1775), as a vulnerable species, is extremely sensitive to indirect impacts of climate change. Given that it is highly difficult undertaking conservation measures on the entire range of snow leopards, identifying hotspots for conservation is necessary. This study was conducted in Bagrot and Haramosh valleys, in the Trans-Himalayan region, to evaluate the impacts of climate and human pressure on snow leopard habitat. Hybrid classification of Landsat satellite data for 2010 and 2020 was performed to elucidate land use changes that suggested a decrease in permanent snow by 10 % and 3 % in Haramosh and Bagrot while an increase in settlements cover by 16 % and 23 %, respectively. Life zone comparison for 2010 and 2020 using the Holdridge life zone (HLZ) classification system disclosed a change from three life zones to five life zones in Haramosh, and four life zones to five life zones in Bagrot, caused by a temperature increase of 2 • C to 3 • C, indicating that the area is becoming more and more suitable for settlements and less favorable for snow leopards. This study underlines again that mountainous regions are more vulnerable to the impacts of climate change. Warming weather is making survival more difficult for snow leopards. Although they are resilient to the direct effects of climate change, indirect impacts like avalanches , flash floods, urbanization, and human-wildlife conflict make them more vulnerable and threaten their survival. Thus, we recommend establishing further protected areas, better controlling illegal wildlife trade, and conducting genetic studies to understand impacts on snow leopards and rangeland management, livelihood improvement, and human-wildlife conflict reductions.
... The ecosystems of Lombok Island exhibit dominance by patchy and fragmented savanna and grassland ecosystems, alongside lowland tropical rain forests, upland tropical forests, and sub-alpine vegetation, owing to the dry temperature and arid ecosystems. Additionally, the island features numerous meadows and shrublands (Sapta et al. 2015). ...
Article
Full-text available
Modeling climate change impacts under future CCM3 scenario on sorghum (Sorghum bicolor) as an drought resilient crop in tropical arid Lombok Island, Indonesia. Intl J Trop Drylands 8: 35-43. The arid ecosystems and drought conditions exacerbated by climate change and rising CO2 levels necessitate the identification of alternative drought-tolerant crops. Sorghum bicolor L. has emerged as a promising option due to its resilience to drought. However, there is dearth of information regarding its future potential distribution, particularly in arid regions like Lombok Island, Indonesia, where sorghum is being considered as a viable alternative to ensure food security. This study employs Maximum Entropy (MaxEnt) modeling, incorporating environmental and bioclimatic variables, along with the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) scenario reflecting doubled CO2 levels, to model the future potential distribution of S. bicolor. The model projects a total suitable habitat area of 1,875 km 2 , constituting 39.56% of Lombok Island's land area. Notably, very high-suitability areas of 175 km 2 , and high-suitability areas of 200 km 2 encompass 3.69% and 4.22% of the island's territory, respectively, predominantly concentrated in the southern region of the island and characterized by low precipitation and high temperatures, particularly at altitudes ranging from 0 to 1,000 meters. The model's performance, evaluated using the Area Under the Curve (AUC), yields a score of 0.725, indicating a good level of accuracy. Key factors influencing sorghum distribution include annual precipitation (68.69%), isothermality (9.56%), temperature seasonality (9.56%), precipitation seasonality (8.69%), and annual mean temperature (3.47%). The CCM3 model forecasts an expansion of sorghum distribution toward the north, occupying approximately 6.25% of Lombok's total area. These findings highlight sorghum's adaptability and resilience to future climate changes, positioning it as a valuable resource for sustainable agriculture in arid environments.
... Although this technique performs well mainly at the equator and its surroundings (Isaac and Bourque 2001;Lin 2003;Sapta et al. 2015), as we can see, it has also been tried to apply at mid-latitudes. This is due to the fact that several validation experiments have shown that under certain conditions, a HLZ map reflects with sufficient accuracy the potential vegetation of a region. ...
Article
Full-text available
The Holdridge life zone (HLZ) method is applied to map potential vegetation types in Turkey. The HLZ map is compared to a map of actual vegetation in order to assess the degradation status of vegetation in Turkey. Data required to identify HLZ classes are provided by the General Directorate of Meteorology, while the current vegetation status is estimated with data provided by the General Directorate of Forestry. After weather data are cleaned and missing values are replaced, the HLZ type is estimated for each station, and then thematic maps are created using the ArcGIS software. The study reveals that there are 12 HLZ types in Turkey. The three dominant types are as follows: cool temperate steppe, warm temperate dry forest, and cool temperate moist forest. In regions where physical geographical controls change in short distances, the biodiversity is greater, and linked to this, the HLZ diversity also appears to be greater. Comparing the identified life zones to the actual vegetation, in some areas, remarkable mismatches can be found. Although, in some regions, the life zone type is consistent with the land cover type, in some narrow areas, the potential vegetation does not reflect features of the current vegetation cover. Considering limitations and capabilities of the assessment approach used in this study, we think that the incompatibility between actual and modelled vegetation types in the eastern region of Turkey is caused by the intensive landscape use. The goal of this research is to support future bioclimatic studies and land use management strategies.
... Based on published scientific works of 1975-2017 period, we identified 28 studies applying Braak's equation, ranging from local scale to regional scale (i.e. Southeast Asia), with various purposes, i.e. for estimating cloud height based on temperature difference between cloud and land surface [12]; for bioclimatic analysis in developing ecological guidelines for land development and management of humid tropical forest environments [13]; for assessing biodiversity on species distribution and abundance of montane to sub-alpine zones [14]; for drought analysis [15]; for ecosystem change study [16]; for epidemiology analysis of dengue fever [17]; for landscape planning [18]; for rice crop modelling [19]; and for land suitability analyses for agricultural cropping systems, forestry/tree-based planting systems, and animal husbandry (e.g [20] and 19 similar studies by others). ...
... En los últimos tiempos, los avances científicos y computacionales han permitido un desarrollo más detallado y rigurosos de modelos de distribución dedicados a este tipo de análisis [8], [9]. A pesar de estos avances el modelo de zonas de vida de Holdridge continúa a ser ampliamente utilizado en estudios de cambios climáticos debido a su practicidad y accesibilidad universal [8], [9], [10], [11], [12], [13], [14], [15]. ...
Conference Paper
Full-text available
Öz: İçinde bulunan çevrenin özellikleri iklim-vejetasyon sınıflandırma yöntemleri sayesinde daha kolay bir şekilde tasvir edilebilir. Bu çalışmanın amacı Türkiye'deki yaşam-alanlarını belirlemek ve bu alanların arazideki gerçek bitki-örtüsü ile karşılaştırmaktır. Bu amaçla bir tür iklim ve vejetasyon sınıflandırma yöntemi olan Holdridge yöntemi uygulandı. Çalışmada, Meteoroloji Genel Müdürlüğü tarafından sağlanan 1970 ile 2016 yılları arasındaki aylık ortalama sıcaklık ve yağış verileri kullanıldı. Eksik veriler ise Kriking yönteminin Fortran95 temelli bir yazılımı geliştirilerek tamamlandı. Ancak, ilgili meteoroloji istasyondaki eksik verilerin oranı %7'sinden fazla ise değerlendirme dışı tutuldu. Ek olarak, verilere homojenlik testi uygulandı ve %95 güven seviyesinde testi başaran veri dikkate alındı. Yöntemde kullanılan veriler ise yağış, bio-sıcaklık ve potansiyel buharlaşma oranıdır. Elde edilen yaşam-alan verilerin haritaları, ArcGIS 10.2 Coğrafi Bilgi Sistemleri (CBS) içindeki Thiessen poligonlar methodu uygulanarak üretildi. Elde edilen ana sonuçlara göre, Türkiye'de 12 farklı yaşam zonu mevcuttur. Bu yaşam-alanları sıklık sırasına göre, "Serin ılıman step", "Sıcak ılıman kuru orman" ve "Serin ılıman nemli orman" şeklinde sıralanır. Bu yaşam-alanların toplam içindeki oranı %77 civarındadır. Çalışmanın bir başka sonucu ise, yükselti ve eğimin fazla olduğu alanlarda ve yoğun bitki örtüsü türünün gözlemlendiği nemli kıyı bölgelerinde birden fazla yaşam-zonuna ait özelliklerin görülmesidir. Diğer taraftan, Türkiye'deki iklim ve bitki-örtüsü etkileşim ilişkileri dikkate alındığında, Holdridge yöntemi ile bulunan yaşam-alanlarından bazıları gözlenen bitki-örtüsü özelliklerini yansıtmamaktadır. Bu durum, ancak yanlış arazi-kullanımı politikaları ve kuvvetlenen iklim değişikliği ile açıklanabilir. Dolayısıyla, çalışmanın ileride arazi-kullanım planlarında karar vericilere destekler sunacağını öneriyoruz. Abstract: The surrounding features can be more easily depicted by means of climate-vegetation classification methods. The aim of this study is to determine the life-zones in Turkey and to compare them with the actual vegetation-cover in the related areas. For this purpose, a kind of climate and vegetation classification method called Holdridge Life-Zone (HLZ) method was applied. In the application, monthly average temperature and precipitation values from 1970 to 2016 provided by the General Directorate of Turkish Meteorology Service were used. In the case of complete the missing data kriging method, a Fortran95 based source code was developed. However, if the proportion of missing data in the related meteorological station is more than 7%, it is removed. In addition, the homogeneity test was performed on the data, and the set that achieved the test at the 95% confidence level were considered. The data used in the method are precipitation, bio-temperature and potential evaporation ratio. The maps of the acquired HLZ were generated by applying the Thiessen polygons method in the ArcGIS 10.2 Geographic Information Systems (GIS). According to the main results, 12 different life zones are obtained in Turkey. These are: "Cool temperate steppe", "Warm temperate dry forest" and "Cool temperate moist forest" according to their frequency. The proportion of these life zones within the total is about 77%. Another consequence of the work is that there are more than one life-zone features in the areas with * İletişim yazarı: Mehmet Kadri Tekin,
Article
Full-text available
Dampak perubahan iklim dapat dirasakan secara global. Pulau Lombok merupakan salah satu pulau kecil yang mempunyai tingkat kerentanan terhadap perubahan iklim lebih besar dibandingkan pulau-pulau besar. Perubahan iklim dapat menyebabkan terganggunya ekosistem yang ada sehingga perlu adanya strategi mitigasi dan adaptasi terhadap perubahan iklim. Data dan informasi perubahan iklim merupakan data dasar yang penting untuk merumuskan strategi mitigasi dan adaptasi terhadap perubahan iklim. Tujuan penelitian ini adalah mendapatkan data dan informasi perubahan curah hujan, suhu dan tipe iklim serta menelusuri dampaknya terhadap ekosistem hutan di Pulau Lombok. Metode yang digunakan adalah analisis perubahan iklim (besaran, perubahan, distribusi spasial dan kecenderungan), dan analisis dampak perubahan iklim terhadap ekosistem hutan melalui analisis kesenjangan antara data terkini dan data histori. Hasil penelitian menunjukkan bahwa di Pulau Lombok telah terjadi perubahan iklim yang ditandai oleh perubahan kecenderungan curah hujan, suhu dan tipe iklim. Dampak perubahan iklim pada ekosistem hutan antara lain rusaknya ekosistem hutan mangrove, hilangnya jenis-jenis endemik, penurunan tutupan lahan, serta berkurangnya kualitas dan kuantitas mata air.
Article
Full-text available
Accuracy in runoff and flood estimation is important for mitigating water related problems. The accuracy depends on the methods used for areal rainfall approximation. The thin plate spline (TPS) technique was introduced in this study for daily areal rainfall approximation in the Upper Ping river basin and was compared with the areal rainfall estimated from two conventional techniques, the isohyetal and Thiessen polygon techniques. Two data sets of maximum rainfall registered in August 2001 and September 2003 at 68 non-automatic rainfall stations located in the basin and nearby areas were used in the analysis. The TPS technique was carried out in conjunction with two separate sources of digital elevation model (DEM), namely, GLOBE-DEM and SRTM-DEM, which were downloaded from the NOAA and NASA websites and have horizontal resolutions of 1 km and 90 m, respectively. The TPS technique proved to provide more accurate results of rainfall estimation than the other two techniques. The coarser DEM resolution (GLOBE-DEM) performed marginally better in rainfall estimation than the finer DEM resolution (SRTM-DEM).
Book
Three important studies were initiated in the 19708 to investigate the relation­ ship between climatic variations and agriculture: by the National Defense University (1980) on Crop Yields and Climate Change to the Year eooo, by the U.s. Department of Transportation (1975) on Impacts 0/ Climatic Change on the Biosphere and by the U.s. Department of Energy (1980) on Environmental and Societal Consequences 0/ a Possible CO -Induced Climatic Change (the ClAP 2 study). These were pioneering projects in a young field. Their emphasis was on measuring likely impacts of climatic variations rather than on evaluating possible responses, and they focused on first-order impacts (e.g., on crop yields) rather than on higher-order effects on society. A logical next step was to look at higher-order effects and potential responses, as part of a more integrated approach to impact assessment. This was undertaken by the World Climate Impact Program (WCIF), which is directed by the United Nations Environment Program (UNEP). The WCIF is one of four aspects of the World Ciimate Program, which was initiated in 1979.
Article
Software tools have been developed at the U.S. Geological Survey's EROS Data Center to extract topographic structure and to delineate watersheds and overland flow paths from digital elevation models. The tools are special purpose FORTRAN programs interfaced with general-purpose raster and vector spatial analysis and relational data base management packages. The first phase of analysis is a conditioning phase that generates three data sets: the original DEM with depressions filled, a data set indicating the flow direction for each cell, and a flow accumulation data set in which each cell receives a value equal to the total number of cells that drain to it. The original DEM and these three derivative data sets can then be processed in a variety of ways.