Visualizing the Spatiotemporal Trends of Thermal Characteristics in a Peatland Plantation Forest in Indonesia: Pilot Test Using Unmanned Aerial Systems (UASs)
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Tsuyoshi Kato | Kazuo Watanabe | Kotaro Iizuka | Sisva Silsigia | Osamu Kozan | Niken Andika Putri | Taishin Kameoka | Kazuo Watanabe | O. Kozan | K. Iizuka | Tsuyoshi Kato | Sisva Silsigia | Taishin Kameoka
[1] Suyanto,et al. Fire, People and Pixels: Linking Social Science and Remote Sensing to Understand Underlying Causes and Impacts of Fires in Indonesia , 2005 .
[2] O. Ahmed,et al. Brief review on climate change and tropical peatlands , 2018, Geoscience Frontiers.
[3] Masayuki Itoh,et al. Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest , 2017, Remote. Sens..
[4] Abdul Wahab Al-Kayssi,et al. Influence of soil moisture content on soil temperature and heat storage under greenhouse conditions , 1990 .
[5] Xixi Lu,et al. Subsidence and carbon loss in drained tropical peatlands , 2012 .
[6] Michael Bock,et al. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 , 2015 .
[7] Upmanu Lall,et al. Interpreting variability in global SST data using independent component analysis and principal component analysis , 2009 .
[8] S. Limin,et al. Canal blocking strategies for hydrological restoration of degraded tropical peatlands in Central Kalimantan, Indonesia , 2014 .
[9] M. van Weele,et al. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997 , 2016, Scientific Reports.
[10] Christophe Delacourt,et al. Assessing the Accuracy of High Resolution Digital Surface Models Computed by PhotoScan® and MicMac® in Sub-Optimal Survey Conditions , 2016, Remote. Sens..
[11] G. Davenport,et al. Hydrogeological controls on spatial patterns of groundwater discharge in peatlands , 2017 .
[12] Bambang H. Trisasongko,et al. Identification of Agricultural Drought Extent Based on Vegetation Health Indices of Landsat Data: Case of Subang and Karawang, Indonesia☆ , 2016 .
[13] S. Leva,et al. IR real-time analyses for PV system monitoring by digital image processing techniques , 2015, 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP).
[14] J. Liski,et al. Temperature sensitivity of decomposition in a peat profile , 2013 .
[15] S. Limin,et al. Heterotrophic respiration in drained tropical peat is greatly affected by temperature—a passive ecosystem cooling experiment , 2014 .
[16] Takeshi Motohka,et al. Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology , 2010, Remote. Sens..
[17] J. You,et al. Characteristics of Wind Velocity and Temperature Change Near an Escarpment-Shaped Road Embankment , 2014, TheScientificWorldJournal.
[18] Takashi Matsubara,et al. Advantages of unmanned aerial vehicle (UAV) photogrammetry for landscape analysis compared with satellite data: A case study of postmining sites in Indonesia , 2018 .
[19] Fei Tang,et al. Impervious Surface Information Extraction Based on Hyperspectral Remote Sensing Imagery , 2017, Remote Sensing.
[20] J. Holden,et al. Water table dynamics in undisturbed, drained and restored blanket peat , 2011 .
[21] Thomas J. Jackson,et al. Soil moisture–temperature relationships: results from two field experiments , 2003 .
[22] Francesco Riccioli,et al. Artificial neural network for multifunctional areas , 2015, Environmental Monitoring and Assessment.
[23] E. Davidson,et al. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change , 2006, Nature.
[24] Steven C. Chapra,et al. Remote Sensing of Submerged Aquatic Vegetation in a Shallow Non-Turbid River Using an Unmanned Aerial Vehicle , 2014, Remote. Sens..
[25] Keechoo Choi,et al. Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh , 2013, Remote. Sens..
[26] J. Holden,et al. Restoration of blanket peatlands. , 2014, Journal of environmental management.
[27] S. Page,et al. Greenhouse gas dynamics in degraded and restored tropical peatlands , 2016 .
[28] F. Siegert,et al. Detection and Characterization of Low Temperature Peat Fires during the 2015 Fire Catastrophe in Indonesia Using a New High-Sensitivity Fire Monitoring Satellite Sensor (FireBird) , 2016, PloS one.
[29] Agustin Lobo,et al. Mapping Crop Planting Quality in Sugarcane from UAV Imagery: A Pilot Study in Nicaragua , 2016, Remote. Sens..
[30] Josep G. Canadell,et al. Current and future CO 2 emissions from drained peatlands in Southeast Asia , 2009 .
[31] D. Blake,et al. Field measurements of trace gases and aerosols emitted by peat fires in Central Kalimantan, Indonesia, during the 2015 El Nino , 2016 .
[32] Elham Sumarga,et al. Spatial Indicators for Human Activities May Explain the 2015 Fire Hotspot Distribution in Central Kalimantan Indonesia , 2017 .
[33] Ruth S. DeFries,et al. Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan, Indonesia , 2016 .
[34] S. Barreira,et al. Spatial fields of Antarctic sea-ice concentration anomalies for summer–autumn and their relationship to Southern Hemisphere atmospheric circulation during the period 1979–2009 , 2011, Annals of Glaciology.
[35] Guilin Liu,et al. Response of land cover types to land surface temperature derived from Landsat-5 TM in Nanjing Metropolitan Region, China , 2016, Environmental Earth Sciences.
[36] Kathy Steppe,et al. Optimizing the Processing of UAV-Based Thermal Imagery , 2017, Remote. Sens..
[37] M. Tomé,et al. Use of multi-temporal UAV-derived imagery for estimating individual tree growth in Pinus pinea stands , 2017 .
[38] Qiming Qin,et al. Shadow Segmentation and Compensation in High Resolution Satellite Images , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.
[39] Erik Meijaard,et al. Deforestation Projections for Carbon-Rich Peat Swamp Forests of Central Kalimantan, Indonesia , 2011, Environmental management.
[40] Sandra Johnson,et al. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation , 2016, Sensors.