Validation of low spatial resolution and no-dichotomy global long-term burned area product by Pareto boundary

2021 ◽  
Author(s):  
Gonzalo Otón ◽  
Magi Franquesa ◽  
Joshua Lizundia-Loiola ◽  
Emilio Chuvieco
Keyword(s):  
Spatial Resolution ◽  
Burned Area ◽  
Area Product

scholarly journals Development of a consistent global long-term burned area product (1982–2018) based on AVHRR-LTDR data

2021 ◽  
Vol 103 ◽  
pp. 102473
Author(s):  
Gonzalo Otón ◽  
Joshua Lizundia-Loiola ◽  
M. Lucrecia Pettinari ◽  
Emilio Chuvieco
Keyword(s):  
Burned Area ◽  
Area Product

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

scholarly journals Evaluation of short-term changes of hydrological response in mountainous basins of the Vitim Plateau (Russia) after forest fires based on data analysis and hydrological modelling

2015 ◽  
Vol 371 ◽  
pp. 157-162 ◽  
Author(s):  
O. M. Semenova ◽  
L. S. Lebedeva ◽  
N. V. Nesterova ◽  
T. A. Vinogradova
Keyword(s):  
Forest Fires ◽  
Model Performance ◽  
Burned Area ◽  
Model Parameters ◽  
Hydrological Response ◽  
Short Term ◽  
Burnt Area ◽  
Area Product ◽  
Induced Changes ◽  
Fire Impact

Abstract. Twelve mountainous basins of the Vitim Plateau (Eastern Siberia, Russia) with areas ranging from 967 to 18 200 km2 affected by extensive fires in 2003 (from 13 to 78% of burnt area) were delineated based on MODIS Burned Area Product. The studied area is characterized by scarcity of hydrometeorological observations and complex hydrological processes. Combined analysis of monthly series of flow and precipitation was conducted to detect short-term fire impact on hydrological response of the basins. The idea of basin-analogues which have significant correlation of flow with "burnt" watersheds in stationary (pre-fire) period with the assumption that fire impact produced an outlier of established dependence was applied. Available data allowed for qualitative detection of fire-induced changes at two basins from twelve studied. Summer flow at the Amalat and Vitimkan Rivers (22 and 78% proportion of burnt area in 2003, respectively) increased by 40–50% following the fire.The impact of fire on flow from the other basins was not detectable.The hydrological model Hydrograph was applied to simulate runoff formation processes for stationary pre-fire and non-stationary post-fire conditions. It was assumed that landscape properties changed after the fire suggest a flow increase. These changes were used to assess the model parameters which allowed for better model performance in the post-fire period.

scholarly journals Modeling long-term fire impact on ecosystem characteristics and surface energy using a process-based vegetation–fire model SSiB4/TRIFFID-Fire v1.0

2020 ◽  
Vol 13 (12) ◽  
pp. 6029-6050
Author(s):  
Huilin Huang ◽  
Yongkang Xue ◽  
Fang Li ◽  
Ye Liu
Keyword(s):  
Surface Energy ◽  
Surface Fluxes ◽  
Surface Energy Budget ◽  
Biophysical Model ◽  
Burned Area ◽  
Area Index ◽  
Fire Model ◽  
Vegetation Height ◽  
Fire Impact

Abstract. Fire is one of the primary disturbances to the distribution and ecological properties of the world's major biomes and can influence the surface fluxes and climate through vegetation–climate interactions. This study incorporates a fire model of intermediate complexity to a biophysical model with dynamic vegetation, SSiB4/TRIFFID (The Simplified Simple Biosphere Model coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model). This new model, SSiB4/TRIFFID-Fire, updating fire impact on the terrestrial carbon cycle every 10 d, is then used to simulate the burned area during 1948–2014. The simulated global burned area in 2000–2014 is 471.9 Mha yr−1, close to the estimate of 478.1 Mha yr−1 in Global Fire Emission Database v4s (GFED4s), with a spatial correlation of 0.8. The SSiB4/TRIFFID-Fire reproduces temporal variations of the burned area at monthly to interannual scales. Specifically, it captures the observed decline trend in northern African savanna fire and accurately simulates the fire seasonality in most major fire regions. The simulated fire carbon emission is 2.19 Pg yr−1, slightly higher than the GFED4s (2.07 Pg yr−1). The SSiB4/TRIFFID-Fire is applied to assess the long-term fire impact on ecosystem characteristics and surface energy budget by comparing model runs with and without fire (FIRE-ON minus FIRE-OFF). The FIRE-ON simulation reduces tree cover over 4.5 % of the global land surface, accompanied by a decrease in leaf area index and vegetation height by 0.10 m2 m−2 and 1.24 m, respectively. The surface albedo and sensible heat are reduced throughout the year, while latent heat flux decreases in the fire season but increases in the rainy season. Fire results in an increase in surface temperature over most fire regions.

Study on Global Burned Forest Areas Based on Landsat Data

2020 ◽  
Vol 86 (8) ◽  
pp. 503-508
Author(s):  
Zhaoming Zhang ◽  
Tengfei Long ◽  
Guojin He ◽  
Mingyue Wei ◽  
Chao Tang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Forest Cover ◽  
Time Series Data ◽  
Global Climate ◽  
Distribution Patterns ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Series Data ◽  
Burned Area ◽  
Burned Forest

Forests are an extremely valuable natural resource for human development. Satellite remote sensing technology has been widely used in global and regional forest monitoring and management. Accurate data on forest degradation and disturbances due to forest fire is important to understand forest ecosystem health and forest cover conditions. For a long time, satellite-based global burned area products were only available at coarse native spatial resolution, which was difficult for detecting small and highly fragmented fires. In order to analyze global burned forest areas at finer spatial resolution, in this study a novel, multi-year 30 meter resolution global burned forest area product was generated and released based on Landsat time series data. Statistics indicate that in 2000, 2005, 2010, 2015, and 2018 the total area of burned forest land in the world was 94.14 million hm2, 96.65 million hm2, 59.52 million hm2, 76.42 million hm2, and 83.70 million hm2, respectively, with an average value of 82.09 million hm2. Spatial distribution patterns of global burned forest areas were investigated across different continents and climatic domains. It was found that burned forest areas were mainly distributed in Africa and Oceania, which accounted for approximately 73.85% and 6.81% of the globe, respectively. By climatic domain, the largest burned forest areas occurred in the tropics, with proportions between 88.44% and 95.05% of the world's total during the study period. Multi-year dynamic analysis shows the global burned forest areas varied considerably due to global climate anomalies, e.g., the La Niña phenomenon.

scholarly journals Estimation of Burned Area in the Northeastern Siberian Boreal Forest from a Long-Term Data Record (LTDR) 1982–2015 Time Series

2018 ◽  
Vol 10 (6) ◽  
pp. 940 ◽  
Author(s):  
José García-Lázaro ◽  
José Moreno-Ruiz ◽  
David Riaño ◽  
Manuel Arbelo
Keyword(s):  
Time Series ◽  
Boreal Forest ◽  
Burned Area ◽  
Data Record ◽  
Term Data

scholarly journals Estimating 2009–2017 Impervious Surface Change in Gwadar, Pakistan Using the HJ-1A/B Constellation, GF-1/2 Data, and the Random Forest Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 443 ◽  
Author(s):  
Bian ◽  
Li ◽  
Zuo ◽  
Lei ◽  
Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Satellite Images ◽  
Reference Data ◽  
High Spatial Resolution ◽  
Impervious Surface ◽  
Urbanization Process ◽  
Rapid Urbanization

The China–Pakistan Economic Corridor (CPEC) is the flagship project of the Belt and Road Initiative. At the end of the CPEC, the Gwadar port on the Arabian Sea is being built quickly, providing an important economical route for the flow of Central Asia’s natural resources to the world. Gwadar city is in a rapid urbanization process and will be developed as a modern, world-class port city in the near future. Therefore, monitoring the urbanization process of Gwadar at both high spatial and temporal resolution is vital for its urban planning, city ecosystem management, and the sustainable development of CPEC. The impervious surface percentage (ISP) is an essential quantitative indicator for the assessment of urban development. Through the integration of remote sensing images and ISP estimation models, ISP can be routinely and periodically estimated. However, due to clouds’ influence and spatial–temporal resolution trade-offs in sensor design, it is difficult to estimate the ISP with both high spatial resolution and dense temporal frequency from only one satellite sensor. In recent years, China has launched a series of Earth resource satellites, such as the HJ (Huangjing, which means environment in Chinese)-1A/B constellation, showing great application potential for rapid Earth surface mapping. This study employs the Random Forest (RF) method for a long-term and fine-scale ISP estimation and analysis of the city of Gwadar, based on the density in temporal and multi-source Chinese satellite images. In the method, high spatial resolution ISP reference data partially covering Gwadar city was first extracted from the 1–2 meter (m) GF (GaoFen, which means high spatial resolution in Chinese)-1/2 fused images. An RF retrieval model was then built based on the training samples extracted from ISP reference data and multi-temporal 30-m HJ-1A/B satellite images. Lastly, the model was used to generate the 30-m time series ISP from 2009 to 2017 for the whole city area based on the HJ-1A/B images. Results showed that the mean absolute error of the estimated ISP was 6.1–8.1% and that the root mean square error (RMSE) of the estimation results was 12.82–15.03%, indicating the consistently high performance of the model. This study highlights the feasibility and potential of using multi-source Chinese satellite images and an RF model to generate long-term ISP estimations for monitoring the urbanization process of the key node city in the CPEC.

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