Volume 8, Number 3 (April 2021): 2731-2741, doi:10.15243/jdmlm.2021.083.2731 ISSN: 2339-076X (p); 2502-2458 (e), www.jdmlm.ub.ac.id
Open Access 2731 Research Article
Rapid detection of land cover change in tropical savanna environment using conditional change vector analysis on remote sensing data in Moyo watershed, Sumbawa Regency, West Nusa Tenggara Province, Indonesia
Gatot Nugroho*, Galdita Aruba Chulafak, Fajar Yulianto
Remote Sensing Application Center, Indonesian National Institute of Aeronautics and Space (LAPAN), Jl. Kalisari No. 8, Pekayon, Pasar Rebo, Jakarta 13710, Indonesia
*corresponding author: [email protected]; [email protected]
Abstract Article history:
Received 3 February 2021 Accepted 25 February 2021 Published 1 April 2021
In environmental management, land cover change is a crucial aspect. The area of tropical savanna environments is vulnerable to land degradation.
This study aimed to rapidly detect land cover changes in a tropical savanna environment based on remote sensing data. Conditional change detection was performed using the Change Vector Analysis (CVA) with input parameters such as the Enhanced Vegetation Index (EVI) and Normalized Difference Soil Index (NDSI). The results showed that during the period 2015 to 2019, changes were detected in the Moyo watershed every year.
From 2015 to 2016, the Moyo River Basin was dominated by changes with a change magnitude of less than 0.088, which was 63% of the Moyo River Basin area. From 2016 to 2017, the changes were dominated by the change magnitude value of 0.063, which was 58.6% of the Moyo River Basin area.
From 2017 to 2018, changes were dominated by the change magnitude value of 0.084 of 55.26% of the Moyo watershed area. From 2018 to 2019, the change was dominated by the change magnitude value of 0.057, which was 47.57% of the Moyo watershed area. The direction of land cover change was dominated by Q2 in 2016, Q4 in 2017 and 2018, and Q2 and Q4 in 2019. These changes generally occurred in the Moyo watershed middle and downstream parts, which are grasslands. The use of the Conditional Change Vector Analysis (CCVA) approach in a tropical savanna environment can detect changes and the direction of change with an accuracy of about 70%.
Keywords:
conditional change vector analysis Indonesia Moyo watershed remote sensing
To cite this article: Nugroho, G., Chulafak, G.A. and Yulianto, F. 2021. Rapid detection of land cover change in tropical savanna environment using conditional change vector analysis on remote sensing data in Moyo watershed, Sumbawa Regency, West Nusa Tenggara Province, Indonesia. Journal of Degraded and Mining Lands Management 8(3): 2731-2741, doi:
10.15243/jdmlm. 2021.083.2731.
Introduction
Land cover change is a crucial aspect of environmental management (Deng et al., 2015). Land use land cover change is also a very important aspect of watershed management (Munoth and Goyal, 2019). Changes in land use land cover that are not suitable for their function can reduce the watershed condition. If left unchecked, as a result, the condition of the watershed becomes critical. So that monitoring changes in land use land cover is needed to preserve the condition of
the watershed. Moyo watershed is one of 15 national priority watersheds located in a tropical savanna environment (Beck et al., 2018). Tropical savanna is an environment consisting of trees and grasses where grass dominates (Sankaran and Ratnam, 2013).
Excessive use of grasses, the encroachment of shrubs, and land degradation are characteristics of tropical savanna environments (Dube et al., 2019). Therefore, spatial and temporal monitoring is needed so that problems such as land degradation can be immediately
Open Access 2732 identified, and efforts can be made to restore them.
Remote sensing technology can be an effective monitoring tool for land cover change (Avdan et al., 2019). Analysis of land use land cover change can be carried out using multitemporal land cover maps (Yulianto et al., 2018) and Principal Component Analysis (Afify, 2011). Mapping of land use land cover is often done is by using image classification techniques. Abdullah et al. (2019) used a multitemporal Landsat image with Random Forest Classification to Analysis land use land cover change.
Chowdhury et al. (2020) used a multitemporal Landsat image with Maximum Likelihood Classification to Analysis land use land cover change in Halda Watershed. Besides using a multitemporal land cover map, land use land cover change analysis can be carried out with the Change Vector Analysis (CVA) approach (Singh and Talwar, 2014).
CVA is a change detection technique using vectors in multi-dimensional space (Polykretis et al., 2020). The CVA approach has advantages in terms of speed of data processing for monitoring land cover. It is carried out by calculating the amplitude of change and the angle of change in the combination of indexes from multitemporal images (Aravind and Sivakumar, 2016). Vector changes direction is determined using the angle change obtained from the arccosine (Aravind and Sivakumar, 2016) and arctangent (Rahman and Mesev, 2019). The problem that occurs when using the arctangent and arccosine to determine the direction of the phase is that there are two different angles with the same values for the arctangent and arccosine. To solve this problem, we proposed a solution for determining the change vector’s direction using a conditional
approach, which we call the CCVA approach. This approach used a conditional function approach from a combination of changes in the direction of the positive and / or negative indices. This study aimed to detect land cover changes rapidly in the tropical savanna environment based on the remote sensing data using the CCVA approach that was performed using the CVA. We use the Enhanced Vegetation Index and Normalized Difference Soil Index as input parameters in this approach. We applied this approach in the Moyo watershed, Sumbawa Regency, West Nusa Tenggara Province, Indonesia, as a case study.
Materials and Methods Study area
This research was conducted in the Moyo watershed area. The Moyo River Basin is located in the Sumbawa Regency, West Nusa Tenggara Province (Figure 1).
The Moyo watershed is one of the critical watersheds that are priorities for recovery in Indonesia. Besides being in a critical condition, the Moyo River Basin also has a different environmental character. Generally, Indonesia’s watersheds are in a tropical environment, but the Moyo watershed is in a tropical savanna environment. Because it is located in a tropical savanna environment, the Moyo watershed land cover is dominated by grass and shrubs. This type of land cover is susceptible to changes in the weather. During the dry season, it is prone to drought. Conversely, during the rainy season, it is prone to flooding.
Therefore, this region really needs monitoring to restore its condition.
Figure 1. Study area in the Moyo watershed in West Nusa Tenggara Province, Indonesia.
Open Access 2733 Data and preprocessing
The main problem in using optical satellite imagery data is the closing of the objects on the surface of the earth by clouds. The closing of the constraints object can be solved by creating cloud-free satellite imagery data annually. In this study, creating cloud-free satellite imagery data for Landsat 8 was created using the Google Earth Engine (GEE) platform. Input data obtained based on Landsat collection Tier 1 that meets geometric and radiometric quality requirements.
Filters needed to determine the date range selection, time limit, and Landsat 8 data’s annual availability in 2015, 2016, 2017, 2018, and 2019. A simple composite algorithm approach has been used for creating cloud-free Landsat-8 composites available on the GEE platform. The working principle of the simple composite algorithm approach will choose a subset of scenes from several locations that are converted to Top of the Atmosphere (TOA) reflectance, apply a simple cloud score, and selecting or take the median of the least-cloudy pixel from the imagery collection data.
Enhanced vegetation index
Various methods of vegetation analysis using remote sensing imagery have been developed, one of which the use of Normalized Difference Vegetation Index (NDVI) that used to be to analyze changes in vegetation (Cheng et al., 2020), detection of deforestation (Puletti and Bascietto, 2019), and the dynamics of changes in vegetation due to drought (Measho et al., 2019). Besides NDVI, another vegetation index commonly used for analyzing vegetation is the Enhanced Vegetation Index (EVI) that has the advantage of minimizing the effects of soil and atmosphere (Jiang et al., 2008). The EVI value is calculated using equation (1) as follows:
EVI = G (1)
Where: N is the Near Infrared band (851-879 nm). R is the Red band (636-673 nm). B is the Blue band (452- 512 nm). G is the multiplier factor of 2.5. C1 and C2 which are the aerosol resistance coefficients in the R and B bands, which are 6 and 7.5 respectively, and L is the soil adjustment factor, which has a value of 1.
Normalized difference soil index
Normalized Difference Soil Index (NDSI) has been widely used to analyze the presence of open land, buildings, and land-related phenomena. NDSI can be used well to estimate soil surface temperature (Bala et al., 2018). In addition, the use of NDSI can improve the extraction of soil information (Deng et al., 2015).
NDSI is formulated using a combination of the Shortwave Infrared 2 (SWIR-2) band and the Green band, as shown in equation (2) as follows:
NDSI = (2)
Where: SWIR2 is the Shortwave Infrared-2 band (2.107-2.294 nm). G is the Green band (533-590 nm) in Landsat-8 imagary.
Conditional change vector analysis
CVA is a change detection technique that changes vectors in multi-dimensional space (Polykretis et al., 2020). As a vector, change has a magnitude of value and direction. The value of change is stated by equation (3). Meanwhile, the magnitude change is determined by a function of the change’s value using equation (4).
∆M = (C1 − C1 ) + (C2 − C2 ) (3) Where: ∆M is the value of change, C1 and C1 are the pixel values in component C1 at time-1 and time- 2, C2 and C2 are pixel values in component C2 at time-1 and time-2.
LM(∆M) =
No change, ∆M ≤ σ Low, σ < ∆M ≤ 0.5
High, ∆M > 0.5 (4)
Where LM(∆M) is the change’s rate, and 𝜎 is the standard deviation of the change’s size. The direction of change in the form of an angle resulting from the change vector’s projection at the Cartesian coordinates is determined using equation (5) as follows:
tanθ = (5)
Where: θ is the direction of change. θhas a value of 0o to 360o. Determining the direction of change using an angle θlike this can lead to ambiguity. Ambiguity is caused by the existence of two different angles between 0 ≤ θ ≤ 360 that can have the same tangent value so that one value resulting from component comparisons can produce two different angles or directions of change. For example, tangent equals 1 can be formed from two different angles, namely 45o and 135o. To solve this problem, in this study, the direction of the vector of change was determined using the component change function in equation (6) as follows:
θ(∆C1, ∆C2) =
Q1, ∆C1 > 0 & ∆C2 > 0 Q2, ∆C1 < 0 & ∆C2 > 0 Q3, ∆C1 < 0 & ∆C2 < 0 Q4, ∆C1 > 0 & ∆C2 < 0
(6)
Where ∆C1 is the change in component 1 and ∆C2 is the change in component 2. With this approach, there is no ambiguity of two angles as the direction change.
In this study, component 1 uses the parameter ΔNDSI and component 2 uses ΔEVI. Q1 shows an increase in EVI and NDSI. Q2 shows an increase in EVI and a decrease in NDSI. Q3 shows a decrease in EVI and NDSI. Q4 shows a decrease in EVI and an increase in NDSI.
Open Access 2734 Results and Discussion
Normalized difference soil index
Changes in land cover in the Moyo watershed can be seen from the distribution of changes in the NDSI value (Figure 2). There was a change in the NDSI value of -1.08 to 0.81 from 2015 to 2016; -0.93 to 0.90
from 2016 to 2017; -0.76 to 0.93 from 2017 to 2018;
and -1.03 to 0.77 from 2018 to 2019. Positive NDSI changes indicate there is a change in the watershed area so that land cover to be open land. Open land causes the reflectance captured by the sensor in the SWIR band to be high. A high SWIR value causes a high NDSI value too.
Figure 2. NDSI change in Moyo watershed area.
High NDSI value can be interpreted as the presence of open land. A high rate of positive NDSI change indicates a change in the type of land cover from the previous vegetation to open land or built-up land. High positive NDSI changes also occur when the previous land cover in water bodies changes to land areas. Small positive changes in NDSI are not caused by changes in vegetation to open land, but due to changes in the level of the greenness of vegetation. This greenness level of vegetation depends on the precipitation and the temperature in the area.
Enhanced vegetation index
The EVI value in the Moyo watershed from 2016-2019 is very dynamic, as shown in Figure 3. There is a change in the vegetation’s greenness level, which causes a change in the EVI value, because EVI is very sensitive to the vegetation’s greenness level. There was a change in the EVI value of -0.44 to 0.49 from 2015 to 2016; -0.56 to 0.38 from 2016 to 2017; -0.52 to 0.46 from 2017 to 2018; and -0.46 to 0.42 from 2018 to 2019. The positive changes indicate an increase in the EVI value from the previous year.
Open Access 2735 Figure 3. EVI Change in Moyo Water Area.
The increase in the EVI value indicates an increase in the vegetation’s greenness level. Conversely, the reduced level of greenness in the vegetation causes the EVI value to decrease, resulting in negative changes.
A significant change in the EVI value indicates a change in the type of land use. Meanwhile, a small level of change occurred due to changes in the greenness of the vegetation. The significant increase in the EVI value indicates a revegetation process. The small increase in the EVI value indicates a change in vegetation conditions from dense green vegetation to dry vegetation. These small changes are also common in agricultural land due to plant growth dynamics. In addition, agricultural land changes in the EVI value at a small level also occur in land cover in the form of grasslands. The greenness level of the meadow often changes with a small degree of change. The level of greenness in the grasslands is very dependent on the availability of water. In dry areas such as the Moyo watershed, water availability is highly dependent on rainfall conditions in the area. The change in the EVI
value that occurred from 2015 to 2016 tends to be positive. This is because, in 2016, the Moyo watershed area received higher rainfall than in 2015 that caused the grassland area in the Moyo watershed to become greener than the previous year. Meanwhile, from 2017 to 2019, the changes tend to be negative due to the rainfall received by the Moyo watershed has also decreased during this period.
Rainfall based on CHIRPS data
Rainfall based on CHIRPS data from 2015 to 2019 (Figure 4) shows that the average rainfall received by the Moyo watershed varies from 3 to 9 mm/day, with the highest average rainfall occurring in 2016. The lowest rainfall occurs in 2019, where the Moyo watershed only received 3 to 5.5 mm/day. From 2015 until 2019, high precipitation occurred in the middle and upstream of the Moyo Watershed. This condition will affect the greenness level of the vegetation and also the EVI value.
Open Access 2736 Figure 4. Average rainfall of the Moyo watershed in mm/day based on the CHIRPS data
during the period 2015 to 2019.
Change magnitude
The level of land cover change in the Moyo watershed derived from ΔEVI and ΔNDSI from 2015 to 2019 is relatively the same as shown in Figure 5. The magnitude change is classified into three classes:
unchanged, low change, and high change. Unchanged is defined by an area that has a magnitude change value less than the standard deviation of magnitude change in the Moyo Watershed. Low change is defined by an area that has a magnitude change between the standard deviation of magnitude change in the Moyo Watershed and 0.5. Hight change is defined by an area that has a magnitude change value of more than 0.5. The majority of the rate of change that occurs is in the low
change category and does not change. From 2015 to 2016, the Moyo watershed was dominated by low changes, which is 50,251 ha or 63.31% of the Moyo watershed area. From 2016 to 2017, the Moyo watershed experienced a low change of 46,512 ha or 58.60%, 32,823 ha or 41.35% did not change, and 38 ha or 0.05% had changes in the high category.
Meanwhile, in the period 2017 to 2018 and 2018 to 2019, there was an increase in change in the high category. From 2017 to 2018, there was a change in the high category, namely 163 ha or 0.21%, the change in the low category was 43,863 ha or 55.26%, and did not experience a change of 35,347 ha or 44.53%.
Whereas in 2018 to 2019, the high category change occurred in 146 ha or 0.18%.
Open Access 2737 Figure 5. The rate of land cover change in the Moyo watershed.
Change direction
Land cover change in the Moyo watershed can be indicated by a combination of changes in EVI and NDSI, which are positive changes and negative changes. From 2015 to 2016, the Moyo watershed change direction was dominated with change direction in quadrant II (Q2), 58.36%. This means that from 2015 to 2016, there has been a change of EVI in a positive direction and NDSI in a negative direction.
From 2016 to 2017 and 2017 to 2018, the change direction was guided by change direction in quadrant
IV (Q4) of 41.93% and 46.56%, respectively. From 2016 to 2018, there has been a decrease in EVI and NDSI, which are spread over almost the entire Moyo watershed. From 2018 to 2019, from 47.57% of the regions that experienced changes, 10.41% experienced changes towards quadrant I (Q1), 17.97% towards quadrant II (Q2), 5.27% towards quadrant III (Q3), and 14.10% towards quadrant IV (Q4). The direction of change in quadrant II occurs in the upstream, middle, and downstream parts, as shown in Figure 6. The direction of change in quadrant IV occurs in the upstream to downstream areas of the Moyo watershed.
Open Access 2738 Figure 6. The direction of land cover change in the Moyo watershed.
Discussion
The middle and upstream parts of the Moyo Watershed are mostly agricultural land and shrubs. Agriculture is a very dynamic object. Change in this object is caused by the vegetation growth pattern and precipitation captured in this area. The change in the agricultural land cause changes in the reflectance received by the satellite image sensors. As a result, the vegetation index (EVI) and soil index (NDSI) also changed.
Although these conditions resulted in changes in EVI and NDSI, the resulting changes were also insignificant. The insignificant changes in agricultural land are caused by the growth pattern of crops on agricultural land and the level of the greenness of the grasses in the grassland areas, which depend on the water content from the rainfall. When it is in a fallow
condition on agricultural land, the land will be overgrown with grass. When it is during the planting period, the agricultural land will be overgrown by crops. The vegetation and soil index between grasses and crops did not differ much. Thus, when changes occur due to cropping patterns, the changes that occur are not significant.
Changes that occur in agricultural land are also largely determined by the precipitation that agricultural land receives. Precipitation and temperature are important factors affecting agricultural land (Aravind and Sivakumar, 2016).
When the precipitation during one period relatively equals the next period, then the agricultural land changes also insignificant. The same thing happened in the grasslands and the shrubs. Grassland areas and
Open Access 2739 the shrubs experience changes in the vegetation’s
greenness level very quickly because the conditions are very dependent on water availability. So, when the precipitation during one period is relatively equals to the next period, then the grassland and shrubs changes are also insignificant.
Significant changes have occurred around the lake area. This significant change is caused by changes in water bodies that dry up and become land. Changes in water bodies to land occurred from 2017 to 2018.
This change resulted in a significant increase in NDSI, which made the change magnitude high. Meanwhile, in 2019 there was an expansion of the water body in the lake area, which made NDSI in 2019 decrease drastically from the previous year so that the change magnitude was high.
The change that leads to Q2 that occurred from 2015 to 2016 shows that in 2016 there was a general increase in vegetation in the Moyo watershed. The vegetation increase in almost the entire Moyo watershed is related to the average rainfall in the Moyo watershed, which has increased from 2015 to 2016. A large amount of water intake has caused the Moyo watershed vegetation to grow better than in 2015. In 2015, 2017, and 2018 the changes that occurred were towards Q4. This change was due to the Moyo watershed being drier than the previous year. The vegetation in the Moyo watershed has not grown as well as in 2016. This condition is caused by a decrease in the water intake from the average rainfall received by the Moyo watershed compared to 2016. This change towards Q4 can also be caused by a change in land use from vegetated land such as forest to open land, dry agriculture, and built-up area. Changes towards Q1 that occur in the downstream part of the Moyo watershed are due to biomass variations.
Changes in biomass variations are related to changes in cropping patterns on agricultural land. From 2018 to 2019, the changes that occurred significantly varied.
These changes occur in the direction of Q1, Q2, and Q4, and also towards Q3. An increase in humidity causes changes that occur in the middle of the Moyo watershed. CCVA is very sensitive to the change. The vegetation index and soil index used can detect changes even though the changes are very small. With this capability, CCVA can be used for the early detection of damage caused by changes in land cover use. Repair efforts can be made immediately to prevent further damage. In critical conditions, such as in the Moyo watershed, it can be seen that local changes have undergone high-level changes so that recovery efforts can be made to improve the condition of the watershed.
Thus, natural disasters, such as floods and droughts,
can be minimized. CCVA can also minimize misinterpretation of the direction of change caused by using the arctangent to determine the direction of change. With the use of conditional functions, The success of CCVA is very dependent on the quality of data input. Because CCVA is very sensitive, the slightest changes that occur can affect the results of the change analysis. With the input in the form of an optical image, which is very vulnerable to the influence of clouds, the CCVA is prone to analysis errors caused by the influence of clouds or haze. The presence of clouds or haze in the image can affect the reflectance value captured by the sensor. This condition can make the index value, which is used to analyze changes, not in accordance with the actual conditions. In tropical areas such as Indonesia, clouds are one of the main obstacles when using optical imagery due to high cloud coverage. The success of CCVA also depends heavily on the use of thresholds in determining change.
The problem of cloud influence can be minimized by using cloud masking methods and multitemporal image mosaics. The development of cloud masking methods can support and enhance the ability of the CCVA to analyze changes in land cover use. The use of radar images as input for CCVA can also be an alternative solution to overcome clouds. The effect of clouds on the relatively small radar data might increase the capabilities of the CCVA. Increasing the accuracy of change analysis by using better threshold determination techniques can also be an alternative solution.
Accuracy assessment
The results of change detection are tested for accuracy using the Random Forest classification reference. The random forest classification results are used because they have a high accuracy of results (Rodriguez- Galiano et al., 2012). There are 4 (four) classes used in this classification. These classes are vegetation, agriculture, settlement, and water bodies. Vegetation was chosen to show changes in vegetation increase, agriculture is used to determine changes due to changes in biomass, settlements are used to show the expansion of settlements and open land, and water body is used to indicate a change in water content (Polykretis et al., 2020). The accuracy test is done using the confusion matrix with the overall accuracy index. The accuracy of change detection is shown in Table 1. The complete configuration matrix is shown in Table 2-6. The confusion matrix is an effective way to test accuracy (Ji and Niu, 2014).
Table 1. Accuracy of land cover change detection in the study area.
No. Test Type Accuracy (%)
2015-2016 2016-2017 2017-2018 2018-2019
1 Change direction 76.31 59.94 70.31 72.73
2 Change 78.26 68.61 70.53 77.30
Open Access 2740 Table 2. Confusion matrix for change direction in
Moyo Watershed during 2015 to 2016.
CCVA Reference
Q1 Q2 Q3 Q4 No
Change
Q1 3 35 2 2 78
Q2 7 129 14 6 394
Q3 5 19 20 1 322
Q4 0 6 2 0 10
No
Change 38 184 42 33 3713
Table 3. Confusion matrix for change direction in Moyo Watershed during 2016 to 2017.
CCVA Reference
Q1 Q2 Q3 Q4 No Change
Q1 0 135 0 0 611
Q2 12 13 4 0 71
Q3 13 48 157 9 301
Q4 2 59 12 0 462
No Change
15 82 20 28 3011
Table 4. Confusion matrix for change direction in Moyo Watershed during 2017 to 2018.
CCVA Reference
Q1 Q2 Q3 Q4 No
Change Q1 118 61 28 10 429
Q2 0 0 0 0 8
Q3 0 11 102 4 355
Q4 23 63 14 49 301
No
Change 30 76 24 67 3292
Table 5. Confusion matrix for change direction in Moyo Watershed during 2018 to 2019.
CCVA Reference
Q1 Q2 Q3 Q4 No
Change
Q1 16 26 3 2 129
Q2 10 175 2 0 225
Q3 24 137 103 7 339
Q4 2 16 2 2 79
No
Change 78 265 17 18 3388
Table 6. Confusion matrix for change direction in Moyo Watershed during 2018 to 2019.
Year 2015 to 2016 2016 to 2017 2017 to 2018 2018 to 2019 Type of
Change Change No
Change Change No
Change Change No
Change Change No Change
Change 251 804 464 1445 483 1093 527 772
No
Change 297 3713 145 3011 197 3292 378 3388
The accuracy-test using the overall accuracy method on several samples allows cases to justify the classification results (Gómez and Montero, 2011). To solve this problem, we used a randomly selected sample from the Random Forest classification results as a reference for accuracy testing. Table 1 shows the accuracy of land cover change detection in the study area. The use of the CCVA method to detect land cover changes in the Moyo watershed with the EVI and NDSI indexes has a varying degree of accuracy for each data years pairs. The highest accuracy was obtained in the change from 2018 to 2019, with a value of 56.02%. This means that more than half of the change detection results have the same results for change detection using the Random Forest classification method. Meanwhile, to detect the change direction, the highest accuracy is also obtained for changes from 2018 to 2019 with a value of 48.23%.
The overall change detection accuracy rate is higher than the change direction accuracy. There is a large difference between CCVA and Random Forest caused by determining the CCVA threshold.
Conclusion
Remote sensing data have been used to detect land cover changes in the tropical savanna environment in the study area. The CCVA approach was used to detect changes and direction of land cover based on the EVI and NDSI inputs to perform the CCA. The use of the CCVA approach in a tropical savanna environment can detect changes and the change direction with an accuracy of about 70%. The success of CCVA is very dependent on the quality of data input. The presence of clouds or haze in the image can affect the reflectance value captured by the sensor so that caused an error in CCVA analysis.
Acknowledgements
This paper is part of the research activities entitled ‘The utilization of remote sensing data to support monitoring of watersheds and lakes in Indonesia. This research was funded by the National Research Priority activity at the Remote Sensing Application Center. Indonesian National Institute of Aeronautics and Space (LAPAN) in 2020. Thanks go to Dr Orbita Roswintiarti as the Deputy Remote Sensing LAPAN,
Open Access 2741 Dr Rokhis Khomarudin as the Director of Remote Sensing
Application Center LAPAN, and the colleagues at the Remote Sensing Application Center. LAPAN for their discussions and suggestions. The authors thank the anonymous reviewers for their efforts and constructive comments, which allowed us to improve the manuscript.
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