Spatial Distribution Patterns Analysis of Hotspot in Central Kalimantan using FIMRS MODIS Data

Adisty Pratamasari, Ni Ketut Feny Permatasari, Tia Pramudiyasari, Masita Dwi Mandini Manessa, Supriatna Supriatna

Abstract


One of the ways to observe the hotspot created by forest fires in Indonesia is through Remote sensing imagery, such as MODIS, NOAA AVHRR, etc. Central Kalimantan is one of the areas in Indonesia with the highest hotspot data. In this research, MODIS FIRMS hotspot data in Central Kalimantan collected from 2017 – 2019, covering 13 districts: South Barito, East Barito, North Barito, Mount Mas, Kapuas, Katingan, Palangkaraya City, West Kotawaringin, East Kotawaringin, Lamandau, Murung Raya, Pulang Pisau, Seruyan, and Sukamara. That is four aspects that this research evaluated: 1) evaluating the spatial pattern using the Nearest Neighbor Analysis (NNA); 2) evaluate the hotspot density appearance using Kernel Density; and 3) correlation analysis between rainfall data and MODIS FIRMS. As a result, the hotspot in Central Kalimantan shows a clustered pattern. While the natural breaks KDE algorithm shows the most relevant result to represent the hotspot distribution. Finally, the hotspot is low correlated with rainfall; however, is see that most of the hotspot (~90%) appeared in low rainfall month (less than 3000 mm/month).

Keywords: Forest fire, Hotspot, NNA, Kernel density, Central Kalimantan

DOI: http://dx.doi.org/10.7454/jglitrop.v4i1.74


Full Text:

24-34 (PDF)

References


Aflahah, E., Hidayati, R., & Hidayat, R. (2019). Pendugaan hotspot sebagai indikator kebakaran hutan di Kalimantan berdasarkan faktor iklim. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan, 9(2), 405–418. https://doi.org/10.29244/jpsl.9.2.405-418

Albar, I., Jaya, I. N. S., Saharjo, B. H., Kuncahyo, B., & Vadrevu, K. P. (2018). Spatio-Temporal Analysis of Land and Forest Fires in Indonesia Using MODIS Active Fire Dataset BT - Land-Atmospheric Research Applications in South and Southeast Asia. In K. P. Vadrevu, T. Ohara, & C. Justice (Eds.) (pp. 105–127). Cham: Springer International Publishing.

https://doi.org/10.1007/978-3-319-67474-2_6

Alfandy, R., Tahmid, M., & Sari, J. (2017). Utilization Satellite of Aqua/Terra for Monitoring Meteorologist Drought Based on Hotspot in Central Kalimantan. Retrieved from http://repository.lapan.go.id/index.php?p=show_detail&id=6884&keywords=

Reuters (2019) Area burned in 2019 forest fires in Indonesia exceeds 2018. Retrieved February 28, 2020, from https://www.reuters.com/article/us-southeast-asia-haze/area-burned-in-2019-forest-fires-in-indonesia-exceeds-2018-official-idUSKBN1X00VU

Biba, M., Esposito, F., Ferilli, S., Di Mauro, N., & Basile, T. M. A. (2007). Unsupervised discretization using kernel density estimation. IJCAI International Joint Conference on Artificial Intelligence, (January), 696–701. https://www.ijcai.org/Proceedings/07/Papers/111.pdf

Bolstad, P. (2016). GIS fundamentals : A First Text on Geographic Information Systems. 5th Edition. XanEdu Publishers, Ann Arbor, MI.770 pp.

Brown, A. A., & Davis, K. P. (Kenneth P. (1973). Forest fire: control and use. New York,: McGraw-Hill.

Davies, D., Operations Manager, L., Ederer, G., Olsina, O., Wong, M., Cechini, M., & Boller, R. (2019). NASA’s Fire Information for Resource Management System (FIRMS): Near Real-Time Global Fire Monitoring using Data from MODIS and VIIRS. Retrieved from https://firms.modaps.eosdis.nasa.gov/

Davis, R., Yang, Z., Yost, A., Belongie, C., & Cohen, W. (2017). The normal fire environment—Modeling environmental suitability for large forest wildfires using past, present, and future climate normals. Forest Ecology and Management, 390, 173–186. https://doi.org/10.1016/j.foreco.2017.01.027

De La Riva, J., Pérez-Cabello, F., Lana-Renault, N., & Koutsias, N. (2004). Mapping wildfire occurrence at regional scale. Remote Sensing of Environment, 92(3), 363–369. https://doi.org/10.1016/j.rse.2004.06.022

Endarwati. (2016). Analisis data titik panas (hotspot) dan areal kebakaran hutan dan lahan tahun 2016. (J. Purwanto, Ed.). Jakarta: Direktorat Inventarisasi dan Pemantauan Sumber Daya Hutan, Ditjen Planologi Kehutanan dan Tata Lingkungan Kementerian Lingkungan Hidup dan Kehutanan.

Erran Seaman, D., & Powell, R. A. (1996). An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology, 77(7), 2075–2085. https://doi.org/10.2307/2265701

ESRI. (2020). ArcGIS Help (10.2, 10.2.1, and 10.2.2). Retrieved February 27, 2020, from https://resources.arcgis.com/ja/help/main/10.2/index.html#//005p0000000p000000

Fuller, M. (1991). Forest fires : an introduction to wildland fire behavior, management, firefighting, and prevention. Wiley.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031

Hart, T. C. (2020). Hot Spot of Crime: Method and Predictibe Analytics. In Geographies of Behavioural Health, Crime, and Disorder: The Intersection of Sosial Problem and Place. Retrieved from https://books.google.co.id/

Hart, T., & Zandbergen, P. (2014). Kernel density estimation and hotspot mapping: Examining the influence of interpolation method, grid cell size, and bandwidth on crime forecasting. Policing, 37(2), 305–323. https://doi.org/10.1108/PIJPSM-04-2013-0039

Manepalli, U., Bham, G., & Kandada, S. (2011). Evaluation of Hotspots Identification Using Kernel Density. The 3rd International Conference on Road Safety and Simulation, 1750, 1–17.

Mitchell, A., (2005). The ESRI guide to GIS analysis. Volume 2, Spatial Measurements and Statistics.

Moore, D. A., & Carpenter, T. E. (1999). Spatial Analytical Methods and Geographic Information Systems: Use in Health Research and Epidemiology. Epidemiologic Reviews, 21(2), 143–161. https://doi.org/10.1093/oxfordjournals.epirev.a017993

Putra, E. I., Hayasaka, H., Takahashi, H., & Usup, A. (2008). Recent Peat Fire Activity in the Mega Rice Project Area, Central Kalimantan, Indonesia. Journal of Disaster Research, 3(5), 334–341. https://doi.org/10.20965/jdr.2008.p0334

Sabani, W., Rahmadewi, D. P., Rahmi, K. I. N., Priyatna, M., & Kurniawan, E. (2019). Utilization of MODIS data to analyze the forest/land fires frequency and distribution (case study : Central Kalimantan Province). IOP Conference Series: Earth and Environmental Science, 243(1). https://doi.org/10.1088/1755-1315/243/1/012032

Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. New York, NY: Chapman and Hall.

Sumarga, E. (2017). Spatial indicators for human activities may explain the 2015 fire hotspot distribution in central Kalimantan Indonesia. Tropical Conservation Science, 10, 1-9. https://doi.org/10.1177/1940082917706168

Turmudi, Yustisi, A. L. G., Riadi, B., Suwarno, Y., & Purwono, N. (2018). Spatial Analysis of the Correlation between Hot Spot Distribution and Land Cover Type in Sumatra, Indonesia. IOP Conference Series: Earth and Environmental Science, 165(1). https://doi.org/10.1088/1755-1315/165/1/012017

Vetrita, Y., & Haryani, N. S. (2012). Validasi Hotspot MODIS Indofire di Provinsi Riau. Jurnal Ilmiah Geomatika, 18(1), 17–28.

Wickramasinghe, C., Wallace, L., Reinke, K., & Jones, S. (2018). Intercomparison of Himawari-8 AHI-FSA with MODIS and VIIRS active fire products. International Journal of Digital Earth. https://doi.org/10.1080/17538947.2018.1527402

Zou, K. H., Tuncali, K., & Silverman, S. G. (2003, June 1). Correlation and simple linear regression. Radiology, Vol. 227, pp. 617–622. https://doi.org/10.1148/radiol.2273011499

Zubaidah, A., Vetrita, Y., & Khomarudin, M. . R. (2014). Validasi hotspot MODIS di wilayah sumatera dan kalimantan berdasarkan data penginderaan jauh SPOT-4 tahun 2012. Jurnal Penginderaan Jauh, 11(No. 1 Juni 2014), 1–15.




DOI: http://dx.doi.org/10.7454/jglitrop.v4i1.74

Refbacks

  • There are currently no refbacks.


View My Stats