Volume 10, Number 1 (October 2022):3991-4000, doi:10.15243/jdmlm.2022.101.3991 ISSN: 2339-076X (p); 2502-2458 (e), www.jdmlm.ub.ac.id
Open Access 3991 Research Article
Vegetation dynamics of Sangkub watershed in North Sulawesi Province indicated by NDVI of Landsat data
Veybi Djoharam1*, Widiatmaka2, Marimin3, Dyah Retno Panuju2, Suria Darma Tarigan2
1 Natural Resources and Environmental Management, Graduate Study Program, IPB University, Jl. Pajajaran, IPB Baranangsiang Campus, Bogor Regency, Indonesia
2 Department of Soil Science and Land Resource, Faculty of Agriculture, IPB University, Jl. Meranti, IPB Dramaga Campus, Dramaga Sub-district, Bogor Regency, Indonesia
3 Department of Agro-Industrial Technology, Faculty of Agricultural Engineering and Technology, IPB University, Jl. Meranti, IPB Dramaga Campus, Dramaga Sub-district, Bogor Regency, Indonesia
*corresponding author: [email protected]
Abstract Article history:
Received 15 July 2022 Accepted 1 September 2022 Published 1 October 2022
Vegetation can be an important indicator of ecosystem change, the influence of anthropogenic and non-anthropogenic factors. Therefore, it is important to study the dynamics of vegetation changes. One technique that can be used for vegetation analysis is the Normalized Difference Vegetation Index (NDVI). NDVI is an indicator of the active biomass that helps distinguish vegetation from other land cover and can provide information about changes in Spatio-temporal vegetation dynamics, thus allowing for assessment of the ecological conditions. This study aimed to investigate the dynamic of vegetation in the Sangkub watershed area located in North Sulawesi Province. This analysis used geospatial technology with the NDVI method, utilizing Landsat 5 and Landsat 8 satellite imagery data in three periods the year 2000, 2015, and 2020. The results showed that vegetation cover of the Sangkub watershed decreased substantially, whereas the non-vegetated area increased gradually over time.
Keywords:
evapotranspiration GIS
hydrology LULC
watershed management
To cite this article: Djoharam, V., Widiatmaka, Marimin, Panuju, D.R. and Tarigan, S.D. 2022. Vegetation dynamics of Sangkub watershed in North Sulawesi Province indicated by NDVI of Landsat data. Journal of Degraded and Mining Lands Management 10(1):3991-4000, doi:10.15243/jdmlm. 2022.101.3991.
Introduction
Ecosystem function is strongly influenced by vegetation (Dobbs et al., 2017; Zhou et al., 2020).
Vegetation change patterns are very vital and have become one of the world's problems because they affect monitoring and decision-making (Omar and Kawamukai, 2021; Verhoeven and Dedoussi, 2022).
Climate change factors play a major role in this change (Zhang et al., 2020; Liu et al., 2021); furthermore, the intensity and type of human activity become other contributing factors (Kennedy et al., 2019).
Annual flows, baseflow, and sedimentation loads increase due to the reduced canopy and understorey protection when deforestation occurs (Latuf and
Amaral, 2016; Kassa et al., 2019). Deforestation will bring more river streams and basic streams (Cavalcante et al., 2019) by lowering evapotranspiration. Vegetation dynamics affect evapotranspiration values, runoff rates, soil surface conditions, and the trend of discharge changes (Jiao et al., 2017). However, studies on deforestation are still few in the Sangkub watershed area of North Sulawesi Province. Understanding the impact of deforestation on the condition of waters is very important because it relates to the management of the surrounding land and water.
Satellite technology provides information on the earth's surface that is complete enough to be used in conducting environmental evaluations (Estoque and
Open Access 3992 Murayama, 2017; Xu et al., 2018). Through the use of
this technology, scientists can assess ecosystems using quantitative models (Huete, 2016). Analysis of environmental changes, such as plant status on a regional scale, can also be carried out using certain algorithms (Panek and Gozdowski, 2020). Monitoring changes in the environment on a large scale can be done thanks to the development of computer technology (Shi et al., 2020; Wang et al., 2020).
The NASA project has provided three sensors (TM, OLI, and TIRS) that can be used to calculate vegetation cover indicators (NASA, 2022). The most popular vegetation index for vegetation dynamic analysis is the Normalized Difference Vegetation Index (NDVI) (Chu et al., 2019; Panek and Gozdowski, 2020).
The calculation of vegetation index by satellite images is determined from the high value of the reflectance of near-infrared radiation by the sponge parenchyma and the process of absorption of red light by chlorophyll. Reflection of infrared radiation indicates good vegetation, and most of the infrared radiation will be absorbed by low leaf chlorophyll indicating poor vegetation. The maximum absorption of chlorophyll is in the wavelength of 0.2; 0.9; and 0.66 m (Venkateswarlu et al., 2012). A value of 1 is the maximum value of NDVI, and -1 is the minimum value (Aburas et al., 2015). A negative value indicates that clouds and water strongly reflect visible light.
Zeros indicate bare rock with the same or less nir and
RED, while the vegetation cover shows a positive value (Ju et al., 2021).
The land cover change analysis carried out covers the Sangkub watershed of North Sulawesi Province.
This is motivated by flood events that often occur in the Sangkub Watershed area every rainy season.
Analysis of satellite images to measure patterns of changes over time in the Normalized Difference Vegetation Index (NDVI) processed using Geographic Information Systems technology. This study aimed to analyze the dynamics of vegetation change in the Sangkub watershed area of North Sulawesi Province;
over the period 2000, 2015, and 2020 using satellite image data from Landsat 5 and Landsat 8.
Materials and Methods Study area
The research location is the Sangkub watershed in North Sulawesi Province (Figure 1) by coordinate reference system (CRS) located in UTM Zone 51N (WGS84 / UTM Zone 51N / EPSG: 23871) with image acquisition based on Worldwide Reference System (WRS) located on Path-Row 112-060. According to the map of Tondano Watershed Management Center and Protected Forest (2020), Sangkub Watershed has an area of 125,104.9 ha. Sangkub River water flow is generally used to irrigate rice fields covering an area of 3,601 ha (Ineke et al., 2017).
Figure 1. Sangkub watershed research site.
Dataset
The main datasets used in the study included three Landsat images derived from Landsat 5 and Landsat 8.
Landsat 8 image data of 2020 with product code ID
LC08_L1TP_112060_20200228_20200822_02_T1 and Landsat 8 image data in 2015 product code ID LC08_L2SP_112060_20150910_20200908_02_T1.
Landsat image data 5 of 2000 product code ID LT05_L1TP_112060_20000527_20200907_02_T1.
Open Access 3993 NDVI
The NDVI formula was used to estimate plant density as in Equation 1 (Morawitz et al., 2006). The production of land cover maps from the NDVI index was performed using Geographic Information Systems (GIS) in Figure 1.
NDVI =NIR − R
NIR + R … … … (1) The NDVI is calculated from the ratio of the difference between the near-infrared band and the red band equal to the sum of the two spectral reflectances (Omar and Kawamukai, 2021). The concept of the NDVI technique is based on the principle that healthy plants have low reflectivity to the visible rays of the electromagnetic spectrum due to chlorophyll. The vegetation density criteria were evaluated using the Normal Density Value Index (NDVI) values from Table 1. The NDVI map reclassification was performed through the value of the NDVI range from -1 to 1, referring to the vegetation ratio based on the reflection spectrum (Bharathkumar and Aslam, 2015).
Vegetation cover has a positive effect on transpiration if it is greater than 60% (Wang et al., 2018), so on this basis, the reclassification in this study is divided into
two classes, i.e. the first has an NDVI value in the range of -1 to 0.25 representing unused land and the second vegetative soil is an NDVI value of 0.25 to 1.
Table 1. Vegetation density criteria.
Class Normalized Difference Vegetation Index (NDVI)
Description
1 -1 to -0.03 Land does not vegetate 2 -0.03 to 0.15 Greenness is very low
3 0.15 to 0.25 Low greenery
4 0.26 to 0.35 Medium greenery 5 0.36 to 1.00 High greenness Source: Regulation of the Minister of Forestry of the Republic of Indonesia (2012).
Research flowchart
The study used Landsat satellite imagery data from 2000, 2015, and 2010. Landsat 5 (TM) and Landsat 8 (OLI-TIRS) images with 30 m resolution, each obtained from the Earth Explorer site (http://earthexplorer.usgs.gov/) of the Geological Survey. Landsat images have been indexed and geo- edited from their source (Aburas et al., 2015).
Figure 2. The operational process to generate an NDVI value map.
Red tape and NIR were combined to produce an NDVI map. Landsat 8 red ribbons are in band 4 and NIR in band 5, while in Landsat 5 red ribbons are in band 3 and NIR is in band 4.
Results and Discussion
The ecological quality of the watershed environment is strongly influenced by the dynamics of land cover and landscape structure variables. Watershed health in the function of improving local ecological resilience to climate change (Ervinia et al., 2019) as well as providing essential services for human and ecological functions (Hamel et al., 2018) overall affected by
forest conditions (Mueller et al., 2019) it is strongly related to the dynamics of vegetation.
Severe damage to vegetation often occurs in mining areas. Open pit mining activities have a major impact on forest resources and ecosystem biodiversity (Agbeshie and Abugre, 2021), causing considerable ecological changes and extreme environmental damage (Wang et al., 2020; Kondratenko et al., 2022).
The cut-to-fill activity causes the soil profile to reverse, and mining material extraction on site also produces clay topsoil that prevents vegetation growth (Awotwi et al., 2021). Vegetation destruction also occurs due to heavy machinery operations causing severe soil compaction and high rainfall intensity, resulting in loss of soil structure and soil aggregates Start
Landsat 5 TM of 2000 Sangkub Watershed Image
Landsat 8 OLI-TIRS of 2015 and 2020 Sangkub Watershed Image
Atmospheric and radiometric
correction
NIR Band Red Band
Generating NDVI maps of 2000, 2015, and 2020 Assessment
with Google Earth Image Final output generating
NDVI differences map
Open Access 3994 (Zhang et al., 2015). This is why the mining process is
closely related to soil and vegetation factors (Pingping et al., 2016), so that after mining, the main concern is to restore the previous land cover, which is generally natural vegetation cover (Vidal-macua et al., 2020).
Therefore, there is an urgent need for appropriate land restoration programs or green mining to reduce the adverse environmental impacts that come from mining which has resulted in land cover change and loss of vegetation cover (Worlanyo and Jiangfeng, 2021).
Changes in vegetation have led to significant changes in the storage-discharge relationship of the catchment area, accounting for about 83-128% of the change in groundwater discharge in the catchment area (Cheng et al., 2017). A 1% decrease in forest cover could increase the river's annual flow by 1.9%.
Upstream, a 1% decrease in forest cover could increase annual river discharge and annual baseline runoff by 2.5% to 5.4% and 2.6% to 6.7%. A 1% decrease in forest cover could increase annual suspended sediments by 8.7% (Zhao et al., 2022), and vegetation could reduce sediment concentrations and loads, including reducing runoff (Huang et al., 2019).
Conversely, increased vegetation cover will significantly reduce surface flow (Yin et al., 2017).
Unlicensed gold mining activities and small- scale gold mining in the Pra Ghana watershed, from the results of the study by Awotwi et al. (2021), were reported to have resulted in changes in the hydrological regime, increasing runoff by 51.5% and 43.9%, and reduced baseflow by 24.4% and 22.9%. It is possible that an increase in runoff during the rainy season can cause flooding, while a decrease in base flow may hurt agricultural activities (Awotwi et al., 2021).
Increase in agricultural land and increase in urban settlement by 59.8% and 28.7% each at the cost of forests and grasslands, the hydrological response described as an increase in surface runoff and evapotranspiration from 9.2% and 1.7%, respectively.
The amount of the rainy season increased by 5.6%, and the dry season decreased by 12.7% (Belihu et al., 2020). In addition, the reduction of forest land reduces the et value, so there is more water, such as surface runoff and total water production. Sediment transport on slopes can generally be limited by vegetation (Fang et al., 2016); at the hillside or umbrella scale, vegetative cover directly cuts off rainwater, and the energy of the raindrops hitting the ground can be reduced, thereby protecting the soil from the effects of raindrops and thereby reducing sediment concentrations, reduce sludge generation, so that the presence of sedimentation in the channel can be minimized (Zhu et al., 2015). Regarding sediment production from mining activities, it was reported that unlicensed gold mining activities produced sediment of 161.9 t/year, followed by small-scale 151.1 t/year and large-scale 67.5 t/year (Awotwi et al., 2021).
Understanding the interactions between plant
dynamics and hydrological processes is complex.
Vegetation can directly affect hydrology through root uptake and transpiration and can indirectly affect various hydrological processes through vertical canopy structures and transpiration. The redistribution of water and energy is a horizontal effect (Jiao et al., 2017; Zhang et al., 2017).
Vegetation dynamics that cause land degradation are influenced by various factors, some of which are forest fires, climate factors, topography, soil, human activities such as mining, plantations and agriculture, and socio-economic factors (Jiao et al., 2017; Wang et al., 2020; Petrosillo et al., 2021; Yadav et al., 2021;
Awotwi et al., 2021). However, changes in cover and land use are more influenced by human activities to meet economic needs (Zope et al., 2016). Changes in agricultural and plantation landscapes from natural vegetated landscapes occur simultaneously with the development of human activities. The dynamics of LULCs, especially the expansion of arable land and the reduction of forest land, have become a common phenomenon (Anley et al., 2022) so that in terms of sustainable development goals, land-use change is taking a step forward into the high-risk period (Steffen et al., 2015) in addition to being one of the main drivers of the development of the hydrological characteristics of the river basin (Shi et al., 2014).
Changes in natural forest vegetation in the Sangkub watershed are generally not caused by mining activities but have turned into agriculture and plantations. Sangkub Watershed is a watershed located in North Sulawesi Province and has an area of 125,104.9 ha (Tondano Watershed Management Center and Protected Forest, 2020). Sangkub Watershed water flow is generally used to irrigate rice fields covering an area of 3,601 ha. (Ineke et al., 2017).
Sangkub District is a buffer district for agricultural products and plantations for North Bolaang Mongondow Regency (North Bolaang Mongondow District Environment and Forestry Service, 2019).
Based on an analysis of Landsat satellite imagery data through the NDVI method approach conducted in the periods of 2000, 2015, and 2020 found that in 2000 the land area did not vegetate 407.06 ha; in 2015 it increased to 784 ha, and by 2020 it increased to 1040 ha. This means that in the period 2000-2015 there was an increase of 377 ha of land not vegetating or increased by 48%, and the period in 2015-2020 increased by 257 ha or an increase of 25%. This increase in non-vegetated land reduces the area of forest land, which in 2000 an area of 124,695 ha to 124,320 ha in 2015 and in 2020 decreased to 124,062 ha. When viewed as a whole, Sangkub watershed’s land cover of as much as 99% is still dominated by forest vegetation. The change of vegetation to open land from the results of assessments using Google Earth imagery generally occurs in downstream parts that are 20 km away from settlements and major highways. It can be seen that natural vegetation is
Open Access 3995 turned into farmland and plantations. This is by data
accessed from https://bolmutkab.bps.go.id/ on April 04, 2022, at 15.43 there was a wide increase for several plantation commodities such as cocoa in 2015, covering an area of 30 ha to 372 ha in 2019, the area of coffee plantations from 2 ha in 2015 to 23 ha in 2019. Analysis from Table 2 shows that the region of Sangkub Watershed in three years is different from the area of Sangkub Watershed administration which is 125,104.9 ha. However, the difference in the total area that occurs is not significant. This is due to the influence of disruption of environmental conditions such as different cloud covers on each image data used (Mastu, 2018). The appearance of spatial change can be seen in Figure 3.
Table 2. Changes in the vegetation of Sangkub watershed.
Periode Non- vegetation
(ha)
Vegetation (ha)
Total area (ha) 2000 407.06 124,695 125,102.06
2015 784 124,320 125,104
2020 1,040 124,062 125,102
Land change non-vegetation (%) 2000-
2015
48 2015-
2020 25
Figure 3. Change NDVI in the Sangkub Watershed for the period 2000, 2015, and 2020; Google Earth (GE) displays as proof that there has been a change.
Open Access 3996 According to Ali et al. (2016), changes in cover and
land use that occur in upstream areas will affect all parts of the watershed and be one of the causes of flooding in downstream areas due to rainwater that cannot be absorbed by the soil due to a decrease in the quality of the catchment area. However, the Sangkub basin area has shown that changes in land cover and land use in the downstream areas also lead to a decrease in the quality of the watershed forests (Figure 3). The impact of flooding caused by the Sangkub River overflow in the rainy season is increasing year by year. The floods on January 15, 2019, affected 983 people (Regional Disaster Management Agency for North Bolaang Mongondow Regency, 2019), while the flood on March 4, 2020, affected 27,512 people (Regional Disaster Management Agency for North Bolaang Mongondow Regency, 2020). If the watershed is interpreted as a container of the water cycle, all parts of the watershed are interconnected, then solving the problem only by focusing on rehabilitating its upstream is not entirely appropriate (Pambudi, 2019). Sangkub watershed measuring 125,104.9 ha, is included in the category of large watersheds (≥1000 km2). In large watersheds, a 1%
change in forest cover can cause a 1.04% change in the annual runoff, while for small watersheds (<1000 km2), a 1% change in forest cover can cause about a 0.43% change in annual runoff (Zhang et al., 2017). In addition to basin size, the hydrological response is a complex process and is controlled by a number of factors, such as rainfall, topography, soil, land use and land cover, and basin characteristics (Zhao et al., 2022).
Natural vegetation that is much lost to open land in the Sangkub Watershed area is downstream with flat topography up to sloping and close to the river flow.
Research conducted by Assefa et al. (2015) showed that the slope of the sloping catchment area is mainly close to the groundwater level area close to the surface (saturated area), becoming a source of territory with high surface runoff because rainfall after saturation cannot absorb more water. When surface runoff is high, the risk of flooding during the rainy season becomes high. Therefore, it is important to consider saturated areas (lowland) when designing strategies and implementing watershed management to effectively minimize surface runoff from watersheds.
In addition, increased open land due to the conversion of forests into residential land, agriculture, and plantations will lead to increased erosion; this is due to LULC regulating the release and transportation of soil particles (Alem, 2022). Severe soil erosion will reduce the quality of the soil and will eventually become one of the causes of flooding. This is because the hydraulic conductivity of the soil and the rate of infiltration, groundwater storage capacity, and water flow path are all controlled by the soil (Berhanu et al., 2013; Assefa et al., 2015).
Flood risk increases as soil permeability decrease, leading to increased surface runoff. When water is supplied at a velocity greater than the infiltration capacity of the soil, water will flow downhill as runoff on steep soils and can cause flooding (Ouma and Tateishi, 2014). A diagram of the rainwater path in and out of the forest can be seen in Figure 4.
Figure 4. Schematic path of rainwater input and output in the forest. Source: Sun et al. (2018).
When rain falls early in a downpour, water quickly replenishes the available stock before "effective precipitation" encourages runoff into the river. Then, when the controlled moisture retention threshold is reached, the soil begins to release water into the river.
After precipitation stops, the moisture acquired by the soil allows water to flow continuously until the end of the event; when the system returns to a steady state and the ground's water holding capacity is high again (Lazo et al., 2019).
Open Access 3997 Land-use conversion not only alters land cover; but
also alters hydrological processes and soil properties.
One of the causes of changes in the nature of the soil is the existence of agricultural and plantation activities that cultivate the land too often, especially by burning (Setyastika et al., 2022), thereby significantly affecting nutrient transport mechanisms (Vanacker et al., 2019).
The deterioration of water quality is strongly correlated with an increase in the concentrations of suspended and dissolved particles in the upper soil layer in low forest cover basins (Plambeck, 2020). This phenomenon is due to the deterioration of water quality in the Sangkub River region. The results of the laboratory analysis were then processed using the calculations of the Storet method, which obtained seven water quality monitoring points in the Sangkub River, all of which were moderately to heavily polluted (North Bolaang Mongondow District Environment and Forestry Service, 2017). The main non-point source (NPS) pollution is thought to come from agriculture (Wang et al., 2019). Unsustainable agricultural practices have a huge impact on land degradation (Wang et al., 2022).
The integrated effect of some of these complicated processes likely forms the characteristics of different capture areas between catchment areas from one another. NDVI analysis of Landsat image data shows that in the Sangkub watershed, there has been a change in land cover in a negative direction.
LULC changes are characterized as positive or negative when contextualized with local information.
Some significant changes are considered negative, such as reduction of natural or semi-natural cover (deforestation) as well as expansion of arable and residential land. On the other hand, an increase in natural vegetation cover, water bodies, and a decrease in cultivated land are positive indicators (Orr et al., 2017).
Low to medium elevation areas with a higher risk of erosion in the Sangkub watershed should be considered for watershed protection priority. While areas with higher elevations may be less immediately risky, this is consistent with the study's findings of Bremer et al. (2021). However, the upstream area of the watershed with a steep slope needs to also be considered because the results of research by Wang et al. (2022) showed that vegetation cover ≥ 50% has become a major source of soil erosion because it is affected by the slope. Land improvements that have experienced a decline in quality include implementing soil and water conservation structures (Alem and Dinku, 2022; Kuntyastuti et al., 2022).
Conclusion
Quantitative evidence of LULC changes shows that non-vegetated land cover in the Sangkub Watershed region showed successive increases in real coverage between 2000-2015 and 2015-2020. In contrast,
natural forest vegetation shows a reduction in its area.
Conversion of forest land into other types of land use/cover is suspected to be the cause. According to the monitoring results of Google Earth, the natural forest vegetation in the Sangkub watershed area is mainly converted into agricultural land, and the plantations will become vacant land outside the growing season. This results in reduced land-setting capacity, which in turn worsens areas sensitive to a surface runoff which will increase the potential for flooding in each rainy season.
The results of this study can be used as a basis for decision making; can provide useful information for the design of land use plans to regulate the management of agricultural land and plantations for sustainable development in the Sangkub River Basin.
The advice of this study is that NDVI analysis is needed for different types of satellite imagery data with higher resolution. A more detailed analysis to find out which areas do not vegetative into plantations, farms, and settlements each change how much it needs to be done. Furthermore, an analysis of the driving factors for changes in vegetation land cover to non- vegetated needs to be done for a complete understanding. In addition, research to determine the effect of changing vegetation cover into open land on Sangkub watershed hydrological conditions is also important.
Acknowledgements
The authors would like to thank the Regional Government of North Bolaang Mongondow Regency, the North Sulawesi Provincial Environment Service, the Tondano Watershed Management Center and Protected Forest, IPB University, Rempongers S3 PSL IPB 2019 for providing support data dan facilities to conduct this study. A part of this paper was presented at The 2022-UN4DRR International Symposium on Disaster Risk Reduction, Mitigation and Environmental Sciences organized by Bogor Agricultural University, Indonesia on July 21, 2022.
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