Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia
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Kristoko Dwi Hartomo | Sri Yulianto Joko Prasetyo | Mila Chrismawati Paseleng | M. Paseleng | K. Hartomo | S. Y. Prasetyo
[1] Maycol Alejandro Zaraza Aguilera. Classication Of Land-Cover Through Machine Learning Algorithms For Fusion Of Sentinel-2a And Planetscope Imagery , 2020, 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS).
[2] Tian Han,et al. Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data , 2009 .
[3] C. Maftei,et al. Assessment of Multi-spectral Vegetation Indices using Remote Sensing and Grid Computing , 2011 .
[4] Agricultural drought characteristics identification and analysis of Henan Province in China , 2014 .
[5] Bogdan Zagajewski,et al. Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images , 2017 .
[6] D. Bui,et al. Satellite-Based, Multi-Indices for Evaluation of Agricultural Droughts in a Highly Dynamic Tropical Catchment, Central Vietnam , 2018 .
[7] E. Chuvieco,et al. Global burned-land estimation in Latin America using MODIS composite data. , 2008, Ecological applications : a publication of the Ecological Society of America.
[8] Huawei Wan,et al. Estimating the area burned by agricultural fires from Landsat 8 Data using the Vegetation Difference Index and Burn Scar Index , 2018 .
[9] Hui Yang,et al. Machine learning and artificial intelligence to aid climate change research and preparedness , 2019, Environmental Research Letters.
[10] Jan Adamowski,et al. Drought forecasting using new machine learning methods / Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod , 2013 .
[11] Nguyen Doan Phuoc,et al. Temperature prediction and model predictive control (MPC) of a distillation column using an artificial neural network based model , 2017, 2017 International Conference on System Science and Engineering (ICSSE).
[12] C. Bourque,et al. Evaluation of the suitability of six drought indices in naturally growing, transitional vegetation zones in Inner Mongolia (China) , 2020, PloS one.
[13] Dar A. Roberts,et al. Exploring the correlation between Southern Africa NDVI and Pacific sea surface temperatures: Results for the 1998 maize growing season , 1999 .
[14] Jun Zhao,et al. The Classification Performance and Mechanism of Machine Learning Algorithms in Winter Wheat Mapping Using Sentinel-2 10 m Resolution Imagery , 2020, Applied Sciences.
[15] Christos Faloutsos,et al. Storage device performance prediction with CART models , 2004, The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004. (MASCOTS 2004). Proceedings..
[16] Jiahua Zhang,et al. Evaluation of Temperature Vegetation Dryness Index on Drought Monitoring Over Eurasia , 2020, IEEE Access.
[17] A. Karakacan Kuzucu,et al. TESTING THE POTENTIAL OF VEGETATION INDICES FOR LAND USE/COVER CLASSIFICATION USING HIGH RESOLUTION DATA , 2017 .
[18] Vinay Kumar Sehgal,et al. DEVELOPING VEGETATION HEALTH INDEX FROM BIOPHYSICAL VARIABLES DERIVEDUSING MODIS SATELLITE DATA IN THE TRANS-GANGETIC PLAINS OF INDIA , 2013 .
[19] Fan Yang,et al. Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China , 2017 .
[20] Ying Li,et al. K-Nearest Neighbor combined with guided filter for hyperspectral image classification , 2017, IIKI.
[21] D. Dutta,et al. Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI) , 2015 .
[22] Miguel Angel Uribe-Opazo,et al. Spatial autocorrelation of ndvi and gvi indices derived from landsat/tm images for soybean crops in the western of the state of Paraná in 2004/2005 crop season , 2013 .
[23] H. Hashim,et al. URBAN VEGETATION CLASSIFICATION WITH NDVI THRESHOLD VALUE METHOD WITH VERY HIGH RESOLUTION (VHR) PLEIADES IMAGERY , 2019, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
[24] M. Paseleng,et al. Computer Assisted Learning on Aridity Disaster Learning Using SIMIA (Satellite Imagery for Modelling Index of Aridity) , 2019, Journal of Physics: Conference Series.
[25] Jayawardhana Wgnn,et al. Investigate the Sensitivity of the Satellite-Based Agricultural Drought Indices to Monitor the Drought Condition of Paddy and Introduction to Enhanced Multi-Temporal Drought Indices , 2020 .
[26] Sergio M. Vicente-Serrano,et al. Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators , 2017 .
[27] O. Dikinya,et al. Using Drought Indices to Model the Statistical Relationships Between Meteorological and Agricultural Drought in Raya and Its Environs, Northern Ethiopia , 2018, Earth Systems and Environment.
[28] D. Godone,et al. COMPARISONS BETWEEN DIFFERENT INTERPOLATION TECHNIQUES , 2014 .
[29] Tsegaye Tadesse,et al. Evaluation of Satellite-Based Rainfall Estimates and Application to Monitor Meteorological Drought for the Upper Blue Nile Basin, Ethiopia , 2017, Remote. Sens..
[30] Yakov A. Pachepsky,et al. Developing the vegetation drought response index for South Korea (VegDRI-SKorea) to assess the vegetation condition during drought events , 2018 .
[31] Christos Faloutsos,et al. Storage device performance prediction with CART models , 2004, The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004. (MASCOTS 2004). Proceedings..
[32] Carol Miller,et al. A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio , 2014, Remote. Sens..
[33] Wanchang Zhang,et al. Monitoring and Assessment of Drought Focused on Its Impact on Sorghum Yield over Sudan by Using Meteorological Drought Indices for the Period 2001-2011 , 2018, Remote. Sens..
[34] László Makra,et al. The Palmer Drought Severity Index (PDSI) as an indicator of soil moisture , 2005 .
[35] ICOLAS,et al. Global burned-land estimation in Latin America using MODIS composite data , 2017 .
[36] Amir Hossein Alavi,et al. Machine learning in geosciences and remote sensing , 2016 .
[37] Timothy A. Warner,et al. Implementation of machine-learning classification in remote sensing: an applied review , 2018 .
[38] Xiaoxia Wang,et al. Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data , 2016, Remote. Sens..