Vegetation Distribution Pattern at Several Landforms and Its Implications towards Surface Run Off
Keywords:
Vegetation distribution, Landform, Run off, Land use, Regional planningAbstract
Mapping of vegetation and other land cover is very important for monitoring the development of land use change and regional planning. However, mapping that focuses on differences in landform characteristics is still very limited. This study aims to analyze the pattern of vegetation distribution in karst, volcanic, and fold landforms. NDVI was used to analyze the distribution of vegetation in several landforms, while MODIS data was used to analyze the intensity and fluctuation of run off in the study area. This study used Sentinel 2 imagery as a data source with a spatial resolution of 10 meters and a temporal resolution of 16-30 days. The results show that there is a different pattern of vegetation distribution in conical hills (holokarst), quaternary volcanic hills, and fold hills. In karst landforms, vegetation is spread out following the distribution of conical hills. In the folded hills, the vegetation is spread in the direction of the anticline axis distribution, while the vegetation is evenly distributed in the volcanic hills with high vegetation density. Differences in the distribution of vegetation also have an impact on differences in surface run off for the three landforms. The distribution of vegetation in several landforms can efficiently be identified using the vegetation index and sentinel 2 because of the wider area coverage, so that it can affect regional environmental management.
References
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Casalini, A.I., Bouza, P.J., Bisigato, A.J. 2019. Geomorphology, soil and vegetation patterns in an arid ecotone. Catena 174, 353-361.
Collignon, M., Schmid, D.W., Galerne, C., Lupi, M., Mazzini, A., 2018. Modelling fluid flow in clastic eruptions: Application to the lusi mud eruption. Marine and Petroleum Geology 90, 173-190. https://doi.org/10.1016/j.marpetgeo.2017.08.011.
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Gašparović, M., Jogun, T., 2018. The effect of fusing Sentinel-2 bands on land-cover classification. International Journal of Remote Sensing 39 (3), 822-841.
Ghosh, A., Joshi, P.K., 2014. A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. International Journal of Applied Earth Observation and Geoinformation 26, 298-311.
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Heydari, S.S., Mountrakis, G., 2018. Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites. Remote Sensing of Environment 204, 648-658.
Inzana, J., Kusky, T., Higgs, G., Tucker, R., 2003. Supervised classifications of landsat TM band ratio images and landsat TM band ratio image with radar for geological interpretations of central madagascar. Journal of African Earth Sciences 37 (1-2), 59-72. https://doi.org/10.1016/S0899-5362(03)00071-X.
Kaliraj, S., Chandrasekar, N., Magesh, N.S., 2015. Morphometric analysis of the river thamirabarani sub-basin in kanyakumari district, south west coast of tamil nadu, india, using remote sensing and GIS. Environmental Earth Sciences 73 (11), 7375-7401. https://doi.org/10.1007/s12665-014-3914-1.
Khatami, R., Mountrakis, G., Stehman, S.V., 2016. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment 177, 89-100.
Larsen, L.G., 2019. Multiscale flow-vegetation-sediment feedbacks in low-gradient landscapes. Geomorphology 334, 165-193.
Macintyre, P., Van Niekerk, A., Mucina, L., 2020. Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification. International Journal of Applied Earth Observation and Geoinformation 85, 101980. https://doi.org/10.1016/j.jag.2019.101980.
Martinez, J.M., Le Toan, T., 2007. Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote sensing of Environment 108 (3), 209-223.
Mercier, A., Betbeder, J., Rumiano, F., Baudry, J., Gond, V., Blanc, L., Bourgoin, C., Cornu, G., Ciudad, C., Marchamalo, M., Poccard-Chapuis, R. Hubert-Moy, L., 2019. Evaluation of Sentinel-1 and 2-time series for land cover classification of forest–agriculture mosaics in temperate and tropical landscapes. Remote Sensing 11 (8), 979.
Metcalfe, I., 2017. Tectonic evolution of Sundaland. Bulletin of the Geological Society of Malaysia 63, 27-60. https://doi.org/10.7186/bgsm63201702.
Mohammadi, A., Costelloe, J.F., Ryu, D., 2017. Application of time series of remotely sensed normalized difference water, vegetation and moisture indices in characterizing flood dynamics of large-scale arid zone floodplains. Remote sensing of Environment 190, 70-82.
Mosleh, Z., Salehi, M.H., Jafari, A., Borujeni, I.E., Mehnatkesh, A., 2016. The effectiveness of digital soil mapping to predict soil properties over low-relief areas. Environmental Monitoring and Assessment 188 (3), 1-13. https://doi.org/10.1007/s10661-016-5204-8.
Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q., 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment 115 (5), 1145-1161. https://doi.org/10.1016/j.rse.2010.12.017.
Naboureh, A., Rezaei Moghaddam, M.H., Feizizadeh, B., Blaschke, T., 2017. An integrated object-based image analysis and CA-markov model approach for modeling land use/land cover trends in the Sarab Plain. Arabian Journal of Geosciences 10 (12). https://doi.org/10.1007/s12517-017-3012-2.
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Novak, V., Renema, W., 2018. Ecological tolerances of miocene larger benthic foraminifera from indonesia. Journal of Asian Earth Sciences 151, 301-323. https://doi.org/10.1016/j.jseaes.2017.11.007.
Pu, G., Quackenbush, L.J., Stehman, S.V., 2021. Using google earth engine to assess temporal and spatial changes in river geomorphology and riparian vegetation. Journal of the American Water Resources Association 57 (5), 789-806. https://doi.org/10.1111/1752-1688.12950.
Putera, R., Junaidi, J., Junaidi, A., 2019. Analysis of Land Cover Changing and Vegetation Index at Kuranji Watershed in Padang, West Sumatera, Indonesia. Journal of Geoscience, Engineering, Environment and Technology 4 (4), 286-290.
Rahman, M.S., Di, L., Yu, E., Lin, L., Yu, Z., 2021. Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI). International Journal of Disaster Risk Science 12 (1), 90-110.
Setyorini, A., Khare, D., Pingale, S. M., 2017. Simulating the impact of land use/land cover change and climate variability on watershed hydrology in the upper brantas basin, Indonesia. Applied Geomatics 9 (3), 191-204. https://doi.org/10.1007/s12518-017-0193-z.
Slagter, B., Tsendbazar, N.E., Vollrath, A., Reiche, J., 2020. Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa. International Journal of Applied Earth Observation and Geoinformation 86, 102009. https://doi.org/10.1016/j.jag.2019.102009.
Tarolli, P., Sofia, G., 2016. Human topographic signatures and derived geomorphic processes across landscapes. Geomorphology 255, 140-161. https://doi.org/10.1016/j.geomorph.2015.12.007.
Obermann, A., Karyono, K., Diehl, T., Lupi, M., Mazzini, A., 2018. Seismicity at lusi and the adjacent volcanic complex, java, indonesia. Marine and Petroleum Geology 90, 149-156. https://doi.org/10.1016/j.marpetgeo.2017.07.033.
Orynbaikyzy, A., Gessner, U., Conrad, C., 2019. Crop type classification using a combination of optical and radar remote sensing data: A review. International Journal of Remote Sensing 40 (17), 6553-6595.
Puigdefábregas, J., 2005. The role of vegetation patterns in structuring runoff and sediment fluxes in drylands. Earth Surface Processes and Landforms 30 (2), 133-147. https://doi.org/10.1002/esp.1181.
Urgeghe, A.M., Mayor, Á.G., Turrión, D., Rodríguez, F., Bautista, S., 2021. Disentangling the independent effects of vegetation cover and pattern on runoff and sediment yield in dryland systems – uncovering processes through mimicked plant patches. Journal of Arid Environments 193, 104585 (1-8). https://doi.org/10.1016/j.jaridenv.2021.104585.
Wang, S., Di Tommaso, S., Faulkner, J., Friedel, T., Kennepohl, A., Strey, R., Lobell, D.B., 2020. Mapping crop types in southeast India with smartphone crowdsourcing and deep learning. Remote Sensing 12 (18), 2957.
Wardlow, B.D., Egbert, S.L., 2010. A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas. International Journal of Remote Sensing 31 (3), 805-830. https://doi.org/10.1080/01431160902897858.
Zulfakriza, Z., Saygin, E., Cummins, P.R., Widiyantoro, S., Nugraha, A.D., Lühr, B., Bodin, T., 2014. Upper crustal structure of central java, indonesia, from transdimensional seismic ambient noise tomography. Geophysical Journal International 197 (1), 630-635. https://doi.org/10.1093/gji/ggu016.
Zwaan, F., Corti, G., Sani, F., Keir, D., Muluneh, A.A., Illsley-Kemp, F., Papini, M., 2020. Structural analysis of the western afar margin, east africa: Evidence for multiphase rotational rifting. Tectonics 39 (7). e2019TC006043 (1-25). https://doi.org/10.1029/2019TC006043.
Advokaat, E.L., Marshall, N.T., Li, S., Spakman, W., Krijgsman, W., van Hinsbergen, D.J.J., 2018. Cenozoic rotation history of borneo and sundaland, SE asia revealed by paleomagnetism, seismic tomography, and kinematic reconstruction. Tectonics 37 (8), 2486-2512. https://doi.org/10.1029/2018TC005010.
Alqurashi, A.F., Kumar, L., Sinha, P., 2016. Urban land cover change modelling using time-series satellite images: A case study of urban growth in five cities of Saudi Arabia. Remote Sensing 8 (10), 838. https://doi.org/10.3390/rs8100838.
Anurogo, W., Lubis, M.Z., Mufida, M.A.K., 2018. Modified soil-adjusted vegetation index in multispectral remote sensing data for estimating tree canopy cover density at rubber plantation. Journal of Geoscience, Engineering, Environment, and Technology 3 (1), 15-24.
Berghuis, H.W.K., Troelstra, S.R., Zaim, Y., 2019. Plio-pleistocene foraminiferal biostratigraphy of the eastern kendeng zone (java, indonesia): The marmoyo and sumberingin sections. Palaeogeography, Palaeoclimatology, Palaeoecology 528, 218-231. https://doi.org/10.1016/j.palaeo.2019.05.008.
Boardman, J., 2016. The value of google earth™ for erosion mapping. Catena 143, 123-127. https://doi.org/10.1016/j.catena.2016.03.031.
Boulton, S.J., Stokes, M., 2018. Which DEM is best for analyzing fluvial landscape development in mountainous terrains? Geomorphology 310, 168-187. https://doi.org/10.1016/j.geomorph.2018.03.002.
Casalini, A.I., Bouza, P.J., Bisigato, A.J. 2019. Geomorphology, soil and vegetation patterns in an arid ecotone. Catena 174, 353-361.
Collignon, M., Schmid, D.W., Galerne, C., Lupi, M., Mazzini, A., 2018. Modelling fluid flow in clastic eruptions: Application to the lusi mud eruption. Marine and Petroleum Geology 90, 173-190. https://doi.org/10.1016/j.marpetgeo.2017.08.011.
Corenblit, D., Tabacchi, E., Steiger, J., Gurnell, A.M., 2007. Reciprocal interactions and adjustments between fluvial landforms and vegetation dynamics in river corridors: A review of complementary approaches. Earth-Science Reviews 84 (1-2), 56-86. https://doi.org/10.1016/j.earscirev.2007.05.004.
Endar, B.N.B., Rizal, K., Susilowati, Kaswandhi, T., Sri, W.S., Rizka, A.H.M., Rizal, a.M., Rio, K.M., Elfa, F., Insan, R.K., 2019. Integrated Subsurface Temperature Modeling beneath Mt. Lawu and Mt. Muriah in The Northeast Java Basin, Indonesia. Open Geosciences 11 (1), 341-351. https://doi.org/10.1515/geo-2019-0027.
Garosi, Y., Sheklabadi, M., Pourghasemi, H.R., Besalatpour, A. A., Conoscenti, C., Van Oost, K., 2018. Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping. Geoderma 330, 65-78. https://doi.org/10.1016/j.geoderma.2018.05.027.
Gašparović, M., Jogun, T., 2018. The effect of fusing Sentinel-2 bands on land-cover classification. International Journal of Remote Sensing 39 (3), 822-841.
Ghosh, A., Joshi, P.K., 2014. A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. International Journal of Applied Earth Observation and Geoinformation 26, 298-311.
Gilani, H., Shrestha, H.L., Murthy, M.S.R., Phuntso, P., Pradhan, S., Bajracharya, B., Shrestha, B., 2015. Decadal land cover change dynamics in bhutan. Journal of Environmental Management 148, 91-100. https://doi.org/10.1016/j.jenvman.2014.02.014.
Gregory, S., Wildman, R., Hulse, D., Ashkenas, L., Boyer, K., 2019. Historical changes in hydrology, geomorphology, and floodplain vegetation of the Willamette River, Oregon. River Research and Applications 35 (8), 1279-1290.
Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., Hokao, K., 2011. Modeling urban land use change by the integration of cellular automaton and markov model. Ecological Modelling 222 (20-22), 3761-3772. https://doi.org/10.1016/j.ecolmodel.2011.09.009.
Hearon, T.E., Rowan, M.G., Lawton, T.F., Hannah, P.T., Giles, K.A., 2015. Geology and tectonics of neoproterozoic salt diapirs and salt sheets in the eastern willouran ranges, south australia. Basin Research 27 (2), 183-207. https://doi.org/10.1111/bre.12067.
Heydari, S.S., Mountrakis, G., 2018. Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites. Remote Sensing of Environment 204, 648-658.
Inzana, J., Kusky, T., Higgs, G., Tucker, R., 2003. Supervised classifications of landsat TM band ratio images and landsat TM band ratio image with radar for geological interpretations of central madagascar. Journal of African Earth Sciences 37 (1-2), 59-72. https://doi.org/10.1016/S0899-5362(03)00071-X.
Kaliraj, S., Chandrasekar, N., Magesh, N.S., 2015. Morphometric analysis of the river thamirabarani sub-basin in kanyakumari district, south west coast of tamil nadu, india, using remote sensing and GIS. Environmental Earth Sciences 73 (11), 7375-7401. https://doi.org/10.1007/s12665-014-3914-1.
Khatami, R., Mountrakis, G., Stehman, S.V., 2016. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment 177, 89-100.
Larsen, L.G., 2019. Multiscale flow-vegetation-sediment feedbacks in low-gradient landscapes. Geomorphology 334, 165-193.
Macintyre, P., Van Niekerk, A., Mucina, L., 2020. Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification. International Journal of Applied Earth Observation and Geoinformation 85, 101980. https://doi.org/10.1016/j.jag.2019.101980.
Martinez, J.M., Le Toan, T., 2007. Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote sensing of Environment 108 (3), 209-223.
Mercier, A., Betbeder, J., Rumiano, F., Baudry, J., Gond, V., Blanc, L., Bourgoin, C., Cornu, G., Ciudad, C., Marchamalo, M., Poccard-Chapuis, R. Hubert-Moy, L., 2019. Evaluation of Sentinel-1 and 2-time series for land cover classification of forest–agriculture mosaics in temperate and tropical landscapes. Remote Sensing 11 (8), 979.
Metcalfe, I., 2017. Tectonic evolution of Sundaland. Bulletin of the Geological Society of Malaysia 63, 27-60. https://doi.org/10.7186/bgsm63201702.
Mohammadi, A., Costelloe, J.F., Ryu, D., 2017. Application of time series of remotely sensed normalized difference water, vegetation and moisture indices in characterizing flood dynamics of large-scale arid zone floodplains. Remote sensing of Environment 190, 70-82.
Mosleh, Z., Salehi, M.H., Jafari, A., Borujeni, I.E., Mehnatkesh, A., 2016. The effectiveness of digital soil mapping to predict soil properties over low-relief areas. Environmental Monitoring and Assessment 188 (3), 1-13. https://doi.org/10.1007/s10661-016-5204-8.
Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q., 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment 115 (5), 1145-1161. https://doi.org/10.1016/j.rse.2010.12.017.
Naboureh, A., Rezaei Moghaddam, M.H., Feizizadeh, B., Blaschke, T., 2017. An integrated object-based image analysis and CA-markov model approach for modeling land use/land cover trends in the Sarab Plain. Arabian Journal of Geosciences 10 (12). https://doi.org/10.1007/s12517-017-3012-2.
Nazeer, M., Nichol, J.E., Yung, Y.K., 2014. Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment. International Journal of Remote Sensing 35 (16), 6271-6291. https://doi.org/10.1080/01431161.2014.951742.
Novak, V., Renema, W., 2018. Ecological tolerances of miocene larger benthic foraminifera from indonesia. Journal of Asian Earth Sciences 151, 301-323. https://doi.org/10.1016/j.jseaes.2017.11.007.
Pu, G., Quackenbush, L.J., Stehman, S.V., 2021. Using google earth engine to assess temporal and spatial changes in river geomorphology and riparian vegetation. Journal of the American Water Resources Association 57 (5), 789-806. https://doi.org/10.1111/1752-1688.12950.
Putera, R., Junaidi, J., Junaidi, A., 2019. Analysis of Land Cover Changing and Vegetation Index at Kuranji Watershed in Padang, West Sumatera, Indonesia. Journal of Geoscience, Engineering, Environment and Technology 4 (4), 286-290.
Rahman, M.S., Di, L., Yu, E., Lin, L., Yu, Z., 2021. Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel Disaster Vegetation Damage Index (DVDI). International Journal of Disaster Risk Science 12 (1), 90-110.
Setyorini, A., Khare, D., Pingale, S. M., 2017. Simulating the impact of land use/land cover change and climate variability on watershed hydrology in the upper brantas basin, Indonesia. Applied Geomatics 9 (3), 191-204. https://doi.org/10.1007/s12518-017-0193-z.
Slagter, B., Tsendbazar, N.E., Vollrath, A., Reiche, J., 2020. Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa. International Journal of Applied Earth Observation and Geoinformation 86, 102009. https://doi.org/10.1016/j.jag.2019.102009.
Tarolli, P., Sofia, G., 2016. Human topographic signatures and derived geomorphic processes across landscapes. Geomorphology 255, 140-161. https://doi.org/10.1016/j.geomorph.2015.12.007.
Obermann, A., Karyono, K., Diehl, T., Lupi, M., Mazzini, A., 2018. Seismicity at lusi and the adjacent volcanic complex, java, indonesia. Marine and Petroleum Geology 90, 149-156. https://doi.org/10.1016/j.marpetgeo.2017.07.033.
Orynbaikyzy, A., Gessner, U., Conrad, C., 2019. Crop type classification using a combination of optical and radar remote sensing data: A review. International Journal of Remote Sensing 40 (17), 6553-6595.
Puigdefábregas, J., 2005. The role of vegetation patterns in structuring runoff and sediment fluxes in drylands. Earth Surface Processes and Landforms 30 (2), 133-147. https://doi.org/10.1002/esp.1181.
Urgeghe, A.M., Mayor, Á.G., Turrión, D., Rodríguez, F., Bautista, S., 2021. Disentangling the independent effects of vegetation cover and pattern on runoff and sediment yield in dryland systems – uncovering processes through mimicked plant patches. Journal of Arid Environments 193, 104585 (1-8). https://doi.org/10.1016/j.jaridenv.2021.104585.
Wang, S., Di Tommaso, S., Faulkner, J., Friedel, T., Kennepohl, A., Strey, R., Lobell, D.B., 2020. Mapping crop types in southeast India with smartphone crowdsourcing and deep learning. Remote Sensing 12 (18), 2957.
Wardlow, B.D., Egbert, S.L., 2010. A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas. International Journal of Remote Sensing 31 (3), 805-830. https://doi.org/10.1080/01431160902897858.
Zulfakriza, Z., Saygin, E., Cummins, P.R., Widiyantoro, S., Nugraha, A.D., Lühr, B., Bodin, T., 2014. Upper crustal structure of central java, indonesia, from transdimensional seismic ambient noise tomography. Geophysical Journal International 197 (1), 630-635. https://doi.org/10.1093/gji/ggu016.
Zwaan, F., Corti, G., Sani, F., Keir, D., Muluneh, A.A., Illsley-Kemp, F., Papini, M., 2020. Structural analysis of the western afar margin, east africa: Evidence for multiphase rotational rifting. Tectonics 39 (7). e2019TC006043 (1-25). https://doi.org/10.1029/2019TC006043.
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2023-08-25
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