This page has only limited features, please log in for full access.
The sustainability of the global savanna ecosystem is currently under threat from climate and anthropological change. Despite the immense threats, the existence of the savanna ecosystem is undervalued and understudied. This study examined the dynamics of the savanna ecosystem in the southern part of Southeast Asia (SEA) using MODIS leaf area index (LAI) data (MOD15A3H) with 500-m spatial resolution and 4-day data, from 2002 to 2020. The annual phenological metrics comprising the start of season (SOS), end of season (EOS), length of season (LOS), rate of greening, rate of browning, and the maximum peak values were derived from the daily interpolated data using the spline function. Additional oscillation and trend analysis using empirical ensemble decomposition methods (EEMD) was conducted to derive the nonlinear trend dynamics of the savanna ecosystem in East Nusa Tenggara (ENT). We found that the SOS at the Savanna ecosystem in SEA is 253.76 ± 2.1 days, the EOS is 161.12 ± 4.0 days, and the average LOS is 170.68 ± 6.5 days. The rainfall variabilities can explain around 35% of the variability in the LAI of the savanna ecosystem. Our EEMD analysis captured the decreasing LAI trend, showing a net change between 2002 and 2015 from 1.08 LAI units (scale of 10−2) to − 0.17 LAI units (scale of 10−2) from 2015 onwards. The result indicated a declining trend of LAI values of savanna ecosystem in ENT, thus requiring further monitoring to ensure the sustainability of this ecosystem.
Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Siti Saringatin; Raihan Rafif. Assessing land surface phenology of the savanna ecosystem in Southeast Asia using Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index from 2002 to 2020. Applied Geomatics 2021, 1 -11.
AMA StyleSanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma, Siti Saringatin, Raihan Rafif. Assessing land surface phenology of the savanna ecosystem in Southeast Asia using Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index from 2002 to 2020. Applied Geomatics. 2021; ():1-11.
Chicago/Turabian StyleSanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Siti Saringatin; Raihan Rafif. 2021. "Assessing land surface phenology of the savanna ecosystem in Southeast Asia using Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index from 2002 to 2020." Applied Geomatics , no. : 1-11.
Coastal regions are one of the most vulnerable areas to the effects of global warming, which is accompanied by an increase in mean sea level and changing shoreline configurations. In Indonesia, the socioeconomic importance of coastal regions where the most populated cities are located is high. However, shoreline changes in Indonesia are relatively understudied. In particular, detailed monitoring with remote sensing data is lacking despite the abundance of datasets and the availability of easily accessible cloud computing platforms such as the Google Earth Engine that are able to perform multi-temporal and multi-sensor mapping. Our study aimed to assess shoreline changes in East Java Province Indonesia from 2000 to 2019 using variables derived from a multi-sensor combination of optical remote sensing data (Landsat-7 ETM and Landsat-8 OLI) and radar data (ALOS Palsar and Sentinel-1 data). Random forest and GMO maximum entropy (GMO-Maxent) accuracy was assessed for the classification of land and water, and the land polygons from the best algorithm were used for deriving shorelines. In addition, shoreline changes were quantified using Digital Shoreline Analysis System (DSAS). Our results showed that coastal accretion is more profound than coastal erosion in East Java Province with average rates of change of +4.12 (end point rate, EPR) and +4.26 m/year (weighted linear rate, WLR) from 2000 to 2019. In addition, some parts of the shorelines in the study area experienced massive changes, especially in the deltas of the Bengawan Solo and Brantas/Porong river with rates of change (EPR) between −87.44 to +89.65 and −18.98 to +111.75 m/year, respectively. In the study areas, coastal erosion happened mostly in the mangrove and aquaculture areas, while the accreted areas were used mostly as aquaculture and mangrove areas. The massive shoreline changes in this area require better monitoring to mitigate the potential risks of coastal erosion and to better manage coastal sedimentation.
Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Siti Saringatin; Pramaditya Wicaksono; Bachtiar Wahyu Mutaqin; Raihan Rafif. Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data. Land 2021, 10, 100 .
AMA StyleSanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma, Siti Saringatin, Pramaditya Wicaksono, Bachtiar Wahyu Mutaqin, Raihan Rafif. Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data. Land. 2021; 10 (2):100.
Chicago/Turabian StyleSanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Siti Saringatin; Pramaditya Wicaksono; Bachtiar Wahyu Mutaqin; Raihan Rafif. 2021. "Shoreline Dynamics in East Java Province, Indonesia, from 2000 to 2019 Using Multi-Sensor Remote Sensing Data." Land 10, no. 2: 100.
The existence and services of mangrove ecosystems in Segara Anakan are threatened by changes in land use on land and global warming, which requires proper and intensive monitoring. The monitoring of mangrove and its trend over large areas can be done using multi-temporal remote sensing technology. However, remote sensing data is often contaminated by cloud cover, and its corresponding shadow resulted in missing data. This study aims to assess the performance of the existed gap-filling techniques, such as, linear, spline, stineman, data interpolation Empirical Orthogonal Function (dineof) and spatial downscaling strategy employing the Proba-V imagery in 100 m, when being used for estimating the missing data and depicting the trend in NDVI from Landsat 8 OLI by using Mann-Kendall test. Our result suggested that EOF-based interpolation gave better prediction results and more accurate in predicting longer missing data. Linear interpolation, on the other hand, was accurate to predict shorter missing data. Overall, all interpolation results can reconstruct 64 (spline) to 72 % (dineof) of missing data in NDVI with the RMSE of 0.10 (dineof) – 0.13 (spline). A consistent decreasing trend was also found from the four interpolation methods, which showed the consistency of the interpolated values when used for deriving trends with similar patterns of overall decreasing trend and magnitude of changes of -0.0095 to -0.0099 (NDVI unit) over the mangrove areas in 2015. The result demonstrated the potential ability of gap-filling methods for simulating the value of missing data and for deriving trends.
Sanjiwana Arjasakusuma; Abimanyu Putra Pratama; Intan Lestari. Assessment of Gap-Filling Interpolation Methods for Identifying Mangrove Trends at Segara Anakan in 2015 by using Landsat 8 OLI and Proba-V. Indonesian Journal of Geography 2020, 52, 341 -349.
AMA StyleSanjiwana Arjasakusuma, Abimanyu Putra Pratama, Intan Lestari. Assessment of Gap-Filling Interpolation Methods for Identifying Mangrove Trends at Segara Anakan in 2015 by using Landsat 8 OLI and Proba-V. Indonesian Journal of Geography. 2020; 52 (3):341-349.
Chicago/Turabian StyleSanjiwana Arjasakusuma; Abimanyu Putra Pratama; Intan Lestari. 2020. "Assessment of Gap-Filling Interpolation Methods for Identifying Mangrove Trends at Segara Anakan in 2015 by using Landsat 8 OLI and Proba-V." Indonesian Journal of Geography 52, no. 3: 341-349.
The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields.
Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Raihan Rafif; Siti Saringatin; Pramaditya Wicaksono. Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia. ISPRS International Journal of Geo-Information 2020, 9, 663 .
AMA StyleSanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma, Raihan Rafif, Siti Saringatin, Pramaditya Wicaksono. Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia. ISPRS International Journal of Geo-Information. 2020; 9 (11):663.
Chicago/Turabian StyleSanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Raihan Rafif; Siti Saringatin; Pramaditya Wicaksono. 2020. "Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia." ISPRS International Journal of Geo-Information 9, no. 11: 663.
The development of remote sensing (RS) technology has enabled the dynamics of various vegetation biophysical parameters to be monitored, such as the water content of vegetation, fraction of green vegetation, and fluorescence relating to photosynthesis. This study aims to estimate and compare the influence of climate and sea surface temperature (SST) variabilities on vegetation dynamics in Australia and parts of Southeast Asia by conducting lagged Pearson’s correlation coefficient (r), multilinear regression, and teleconnection analyses using the Empirical Orthogonal Teleconnection (EOT). The monthly vegetation anomalies from January 2013 to September 2018 (69 months) from several RS-based proxies such as, Solar Induced Fluorescence (SIF) from the Global Ozone Monitoring Experiment (GOME)-2B, Moderate Resolution Imaging Spectroradiomater (MODIS) based-Normalized Differenced Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI), and X-, C- and Ku-band microwave-based Vegetation Optical Depth Climate Archive (VODCA), were linked with precipitation and rainfall anomalies in Global Land Data Assimilation System (GLDAS) data and Optimum Interpolation Sea Surface Temperature (OISST) anomalies from National Oceanic and Atmospheric Administration (NOAA). The results showed the correlation strengths between vegetation dynamics and precipitation and rainfall were −0.23 (X- and Ku-band VOD) to 0.35 (SIF) and −0.41 (NDVI) to 0.39 (SIF), respectively. The climate variabilities can explain 22% to 37% (Radj2 of 19% to 35%) of the variance in vegetation dynamics in the study area. In addition, the two modes generated from EOT analysis formed spatial patterns relating to El Nino Southern Oscillation (ENSO) events that can explain 18% (SIF) to 62% (Ku-band VOD) of the variance in vegetation dynamics. These results highlight the influence of climate variabilities and ENSO on various vegetation biophysical properties.
Sanjiwana Arjasakusuma; Bachtiar Wahyu Mutaqin; Andung Bayu Sekaranom; Muh Aris Marfai. Sensitivity of remote sensing-based vegetation proxies to climate and sea surface temperature variabilities in Australia and parts of Southeast Asia. International Journal of Remote Sensing 2020, 41, 8631 -8653.
AMA StyleSanjiwana Arjasakusuma, Bachtiar Wahyu Mutaqin, Andung Bayu Sekaranom, Muh Aris Marfai. Sensitivity of remote sensing-based vegetation proxies to climate and sea surface temperature variabilities in Australia and parts of Southeast Asia. International Journal of Remote Sensing. 2020; 41 (22):8631-8653.
Chicago/Turabian StyleSanjiwana Arjasakusuma; Bachtiar Wahyu Mutaqin; Andung Bayu Sekaranom; Muh Aris Marfai. 2020. "Sensitivity of remote sensing-based vegetation proxies to climate and sea surface temperature variabilities in Australia and parts of Southeast Asia." International Journal of Remote Sensing 41, no. 22: 8631-8653.
Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection; it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data.
Sanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Stuart Phinn. Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data. ISPRS International Journal of Geo-Information 2020, 9, 507 .
AMA StyleSanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma, Stuart Phinn. Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data. ISPRS International Journal of Geo-Information. 2020; 9 (9):507.
Chicago/Turabian StyleSanjiwana Arjasakusuma; Sandiaga Swahyu Kusuma; Stuart Phinn. 2020. "Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data." ISPRS International Journal of Geo-Information 9, no. 9: 507.
Massive deforestation in Indonesia drives the need for proper monitoring using appropriate technology and method. The continuing mission of Landsat sensor extends the observation to almost 30 years back, initiating the ability to monitor the dynamics of vegetation intensively. By taking the advantage of the Landsat archive, advanced semi-automatic classification method, namely ClasLite developed by Asner et al. (J Appl Remote Sens 3:33543–33543, 2009) and a new end-product of 30 m Global Forest Cover Change cover (GFC) datasets developed by (Hansen et al. in Science 342:850–853, 2013a), offered the ability to easily monitor deforestation and forest degradation with little or few knowledge of mapping. This study aims to assess the performance of these newly available products of GFC and the ClasLite method against the traditional pixel-based supervised classification of minimum distance to mean (MD), maximum likelihood (ML), spectral angle mapper (SAM), and random forest (RF). Visual image interpretation of pan-sharpened Landsat was carried out to measure the accuracy of each final map. Result demonstrated that GFC and CLaslite performance has 3 to 18% higher overall accuracy for mapping vegetation cover change compared with the conventional supervised analysis using MD, ML, SAM, and RF with ClasLite as the most accurate method with 78.14 ± 2%. Further adjustment of the cover change map of GFC by using forest extent from ClasLite was able to increase the accuracy of the original GFC data by 10%. Therefore, GFC and ClasLite ensure the ability to monitor vegetation cover change accurately in a simple manner.
Sanjiwana Arjasakusuma; Muhammad Kamal; Muhammad Hafizt; Hernandea Frieda Forestriko. Local-scale accuracy assessment of vegetation cover change maps derived from Global Forest Change data, ClasLite, and supervised classifications: case study at part of Riau Province, Indonesia. Applied Geomatics 2018, 10, 205 -217.
AMA StyleSanjiwana Arjasakusuma, Muhammad Kamal, Muhammad Hafizt, Hernandea Frieda Forestriko. Local-scale accuracy assessment of vegetation cover change maps derived from Global Forest Change data, ClasLite, and supervised classifications: case study at part of Riau Province, Indonesia. Applied Geomatics. 2018; 10 (3):205-217.
Chicago/Turabian StyleSanjiwana Arjasakusuma; Muhammad Kamal; Muhammad Hafizt; Hernandea Frieda Forestriko. 2018. "Local-scale accuracy assessment of vegetation cover change maps derived from Global Forest Change data, ClasLite, and supervised classifications: case study at part of Riau Province, Indonesia." Applied Geomatics 10, no. 3: 205-217.
Ongoing global warming has triggered extreme climate events of increasing magnitude and frequency. Under this effect, a series of extreme climate events such as drought and increased rainfall during the El Nino Southern Oscillation (ENSO) are expected to be amplified in the coming years. Adequate mapping of regions with climate-sensitive vegetation and its associated time lag is required for appropriate mitigation planning to avoid potential negative ecological impacts towards vegetation. In this study, ENSO and climate indicator time series data, for example, Multivariate ENSO Index (MEI) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data for rainfall were linked with long-term time series vegetation proxies from remote sensing (RS proxies). ENSO- and rainfall-sensitive areas were identified from each RS proxy using the bivariate Granger test, and the areas identified by multiple RS proxies were taken to identify climate-sensitive regions in Indonesia. Of the biome types in Indonesia, savanna was the most sensitive, with approximately 53% of the total savanna area in Indonesia shown to be sensitive to ENSO and rainfall by two or more RS proxies. Rolling correlation analysis also found that the ENSO effect on the vegetation region after rainfall was positively correlated with the RS proxies with a time lag of +5 months. Therefore, rainfall can be taken as a proxy of the effects of ENSO on the temporal dynamics of sensitive vegetation regions in Indonesia.
Sanjiwana Arjasakusuma; Yasushi Yamaguchi; Yasuhiro Hirano; Xiang Zhou. ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data. ISPRS International Journal of Geo-Information 2018, 7, 103 .
AMA StyleSanjiwana Arjasakusuma, Yasushi Yamaguchi, Yasuhiro Hirano, Xiang Zhou. ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data. ISPRS International Journal of Geo-Information. 2018; 7 (3):103.
Chicago/Turabian StyleSanjiwana Arjasakusuma; Yasushi Yamaguchi; Yasuhiro Hirano; Xiang Zhou. 2018. "ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data." ISPRS International Journal of Geo-Information 7, no. 3: 103.
Normalized difference vegetation index (NDVI) has been widely applied for monitoring vegetation dynamics. However, NDVI values are known to be profoundly affected by various external factors. In this study, the variation of NDVI values and trends among the several long-term NDVI datasets with resolution of 1, 4 and 8 km were assessed to understand the differences between the available datasets. The assessment items were 1) Pearson’s correlation coefficient, 2) trend map and breakpoint spatial similarities and 3) comparison of NDVI from Landsat and Flux tower in 2007–2015. The comparison revealed a maximum correlation coefficient of 0.67 among NDVI datasets and average spatial similarity of 37.2% among the trend maps estimated from NDVI datasets. Furthermore, there was a possibility of having significantly opposite trends between two trend maps from different NDVI products. Comparisons with NDVI from vegetation pixel in Landsat 5 TM and 8 OLI resulted in the R2 between 0.06 and 0.68 and RMSE of 0.07–0.2, while comparison with NDVI from flux tower data yielded the RMSE of 0.04–0.41, although the R2 was relatively weak at 0–0.18. Our study highlights the possibility of differences among NDVI datasets, and suggests that these differences should be reconciled especially in time-series analysis.
Sanjiwana Arjasakusuma; Yasushi Yamaguchi; Tatsuro Nakaji; Yoshiko Kosugi; Siti-Aisah Shamsuddin; Marryanna Lion. Assessment of values and trends in coarse spatial resolution NDVI datasets in Southeast Asia landscapes. European Journal of Remote Sensing 2018, 51, 863 -877.
AMA StyleSanjiwana Arjasakusuma, Yasushi Yamaguchi, Tatsuro Nakaji, Yoshiko Kosugi, Siti-Aisah Shamsuddin, Marryanna Lion. Assessment of values and trends in coarse spatial resolution NDVI datasets in Southeast Asia landscapes. European Journal of Remote Sensing. 2018; 51 (1):863-877.
Chicago/Turabian StyleSanjiwana Arjasakusuma; Yasushi Yamaguchi; Tatsuro Nakaji; Yoshiko Kosugi; Siti-Aisah Shamsuddin; Marryanna Lion. 2018. "Assessment of values and trends in coarse spatial resolution NDVI datasets in Southeast Asia landscapes." European Journal of Remote Sensing 51, no. 1: 863-877.