scholarly journals A Global Dynamic Long-Term Inundation Extent Dataset at High Spatial Resolution Derived through Downscaling of Satellite Observations

2017 ◽  
Vol 18 (5) ◽  
pp. 1305-1325 ◽  
Author(s):  
Filipe Aires ◽  
Léo Miolane ◽  
Catherine Prigent ◽  
Binh Pham ◽  
Etienne Fluet-Chouinard ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Google Earth ◽  
Satellite Observations ◽  
Smooth Transition ◽  
Spatial And Temporal Variability ◽  
Global Dynamic ◽  
Inundation Extent ◽  
Smoothing Procedure

Abstract A new procedure is introduced to downscale low-spatial-resolution inundation extents from Global Inundation Extent from Multi-Satellites (GIEMS) to a 3-arc-s (90 m) dataset (known as GIEMS-D3). The methodology is based on topography and hydrography information from the HydroSHEDS database. A new floodability index is introduced and an innovative smoothing procedure is developed to ensure a smooth transition, in the high-resolution maps, between the low-resolution boxes from GIEMS. Topography information is pertinent for natural hydrology environments controlled by elevation but is more limited in human-modified basins. However, the proposed downscaling approach is compatible with forthcoming fusion of other, more pertinent satellite information in these difficult regions. The resulting GIEMS-D3 database is the only high-spatial-resolution inundation database available globally at a monthly time scale over the 1993–2007 period. GIEMS-D3 is assessed by analyzing its spatial and temporal variability and evaluated by comparisons to other independent satellite observations from visible (Google Earth and Landsat), infrared (MODIS), and active microwave (synthetic aperture radar).

scholarly journals A Comparison of Methods for Determining Forest Composition from High-Spatial-Resolution Remotely Sensed Imagery

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1290
Author(s):  
Benjamin T. Fraser ◽  
Russell G. Congalton
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Local Scale ◽  
Google Earth ◽  
Forest Composition ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Remotely Sensed Imagery ◽  
Composition Classes

Remotely sensed imagery has been used to support forest ecology and management for decades. In modern times, the propagation of high-spatial-resolution image analysis techniques and automated workflows have further strengthened this synergy, leading to the inquiry into more complex, local-scale, ecosystem characteristics. To appropriately inform decisions in forestry ecology and management, the most reliable and efficient methods should be adopted. For this reason, our research compares visual interpretation to digital (automated) processing for forest plot composition and individual tree identification. During this investigation, we qualitatively and quantitatively evaluated the process of classifying species groups within complex, mixed-species forests in New England. This analysis included a comparison of three high-resolution remotely sensed imagery sources: Google Earth, National Agriculture Imagery Program (NAIP) imagery, and unmanned aerial system (UAS) imagery. We discovered that, although the level of detail afforded by the UAS imagery spatial resolution (3.02 cm average pixel size) improved the visual interpretation results (7.87–9.59%), the highest thematic accuracy was still only 54.44% for the generalized composition groups. Our qualitative analysis of the uncertainty for visually interpreting different composition classes revealed the persistence of mislabeled hardwood compositions (including an early successional class) and an inability to consistently differentiate between ‘pure’ and ‘mixed’ stands. The results of digitally classifying the same forest compositions produced a higher level of accuracy for both detecting individual trees (93.9%) and labeling them (59.62–70.48%) using machine learning algorithms including classification and regression trees, random forest, and support vector machines. These results indicate that digital, automated, classification produced an increase in overall accuracy of 16.04% over visual interpretation for generalized forest composition classes. Other studies, which incorporate multitemporal, multispectral, or data fusion approaches provide evidence for further widening this gap. Further refinement of the methods for individual tree detection, delineation, and classification should be developed for structurally and compositionally complex forests to supplement the critical deficiency in local-scale forest information around the world.

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.

Analysis of the long-term surface wind variability over complex terrain using a high spatial resolution WRF simulation

2012 ◽  
Vol 40 (7-8) ◽  
pp. 1643-1656 ◽  
Author(s):  
Pedro A. Jiménez ◽  
J. Fidel González-Rouco ◽  
Juan P. Montávez ◽  
E. García-Bustamante ◽  
J. Navarro ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Complex Terrain ◽  
High Spatial Resolution ◽  
Surface Wind ◽  
Wind Variability

scholarly journals High Spatial Resolution Topsoil Organic Matter Content Mapping Across Desertified Land in Northern China

2021 ◽  
Vol 9 ◽  
Author(s):  
Yang Junting ◽  
Li Xiaosong ◽  
Wu Bo ◽  
Wu Junjun ◽  
Sun Bin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Matter ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Organic Matter Content ◽  
Multiple Linear Regression Model ◽  
Northern China ◽  
Google Earth ◽  
Support Vector ◽  
Combating Desertification

Soil organic matter (SOM) content is an effective indicator of desertification; thus, monitoring its spatial‒temporal changes on a large scale is important for combating desertification. However, mapping SOM content in desertified land is challenging owing to the heterogeneous landscape, relatively low SOM content and vegetation coverage. Here, we modeled the SOM content in topsoil (0–20 cm) of desertified land in northern China by employing a high spatial resolution dataset and machine learning methods, with an emphasis on quarterly green and non-photosynthetic vegetation information, based on the Google Earth Engine (GEE). The results show: 1) the machine learning model performed better than the traditional multiple linear regression model (MLR) for SOM content estimation, and the Random Forest (RF) model was more accurate than the Support Vector Machine (SVM) model; 2) the quarterly information regarding green vegetation and non-photosynthetic were identified as key covariates for estimating the SOM content in desertified land, and an obvious improvement could be observed after simultaneously combining the Dead Fuel Index (DFI) and Normalized Difference Vegetation Index (NDVI) of the four quarters (R2 increased by 0.06, the root mean square error decreased by 0.05, the ratio of prediction deviation increased by 0.2, and the ratio of performance to interquartile distance increased by 0.5). In particular, the effects of the DFI in Q1 (the first quarter) and Q2 (the second quarter) on estimating low SOM content (<1%) were identified; finally, a timely (2019) and high spatial resolution (30 m) SOM content map for the desertified land in northern China was drawn which shows obvious advantages over existing SOM products, thus providing key data support for monitoring and combating desertification.

scholarly journals Long-term changes of tropospheric NO<sub>2</sub> over megacities derived from multiple satellite instruments

2012 ◽  
Vol 12 (12) ◽  
pp. 31767-31828 ◽  
Author(s):  
A. Hilboll ◽  
A. Richter ◽  
J. P. Burrows
Keyword(s):  
Time Series ◽  
Middle East ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
The United States ◽  
Central China ◽  
Full Time ◽  
Data Set ◽  
Tropospheric No2

Abstract. Tropospheric NO2, a key pollutant in particular in cities, has been measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. These data provide a unique global long-term data set of tropospheric pollution. However, the measurements differ in spatial resolution, local time of measurement, and measurement geometry. All these factors can severely impact the retrieved NO2 columns, which is why they need to be taken into account when analysing time series spanning more than one instrument. In this study, we present several ways to explicitly account for the instrumental differences in trend analyses of the NO2 columns derived from satellite measurements, while preserving their high spatial resolution. Both a physical method, based on spatial averaging of the measured earthshine spectra and extraction of a resolution pattern, and statistical methods, including instrument-dependent offsets in the fitted trend function, are developed. These methods are applied to data from GOME and SCIAMACHY separately, to the combined time series and to an extended data set comprising also GOME-2 and OMI measurements. All approaches show consistent trends of tropospheric NO2 for a selection of areas on both regional and city scales, for the first time allowing consistent trend analysis of the full time series at high spatial resolution and significantly reducing the uncertainties of the retrieved trend estimates compared to previous studies. We show that measured tropospheric NO2 columns have been strongly increasing over China, the Middle East, and India, with values over East Central China triplicating from 1996 to 2011. All parts of the developed world, including Western Europe, the United States, and Japan, show significantly decreasing NO2 amounts in the same time period. On a megacity level, individual trends can be as large as +27 ± 3.7% yr−1 and +20 ± 1.9% yr−1 in Dhaka and Baghdad, respectively, while Los Angeles shows a very strong decrease of −6.0 ± 0.37% yr−1. Most megacities in China, India, and the Middle East show increasing NO2 columns of +5–10% yr−1, leading to a doubling to triplication within the observed period. While linear trends derived with the different methods are consistent, comparison of the GOME and SCIAMACHY time series as well as inspection of time series over individual areas shows clear indication of non-linear changes in NO2 columns in response to rapid changes in technology used and the economical situation.

scholarly journals Prediction of Sea Surface Temperature in the China Seas Based on Long Short-Term Memory Neural Networks

2020 ◽  
Vol 12 (17) ◽  
pp. 2697
Author(s):  
Li Wei ◽  
Lei Guan ◽  
Liqin Qu ◽  
Dongsheng Guo
Keyword(s):  
Spatial Resolution ◽  
Lead Time ◽  
Short Term Memory ◽  
High Spatial Resolution ◽  
Sea Surface ◽  
China Seas ◽  
Short Term ◽  
Long Short Term Memory ◽  
The China Seas

Sea surface temperature (SST) in the China Seas has shown an enhanced response in the accelerated global warming period and the hiatus period, causing local climate changes and affecting the health of coastal marine ecological systems. Therefore, SST distribution prediction in this area, especially seasonal and yearly predictions, could provide information to help understand and assess the future consequences of SST changes. The past few years have witnessed the applications and achievements of neural network technology in SST prediction. Due to the diversity of SST features in the China Seas, long-term and high-spatial-resolution prediction remains a crucial challenge. In this study, we adopted long short-term memory (LSTM)-based deep neural networks for 12-month lead time SST prediction from 2015 to 2018 at a 0.05° spatial resolution. Considering the sub-regional differences in the SST features of the study area, we applied self-organizing feature maps (SOM) to classify the SST data first, and then used the classification results as additional inputs for model training and validation. We selected nine models differing in structure and initial parameters for ensemble to overcome the high variance in the output. The statistics of four years’ SST difference between the predicted SST and Operational SST and Ice Analysis (OSTIA) data shows the average root mean square error (RMSE) is 0.5 °C for a one-month lead time and is 0.66 °C for a 12-month lead time. The southeast of the study area shows the highest predictable accuracy, with an RMSE less than 0.4 °C for a 12-month prediction lead time. The results indicate that our model is feasible and provides accurate long-term and high-spatial-resolution SST prediction. The experiments prove that introducing appropriate class labels as auxiliary information can improve the prediction accuracy, and integrating models with different structures and parameters can increase the stability of the prediction results.

scholarly journals Long-term, high-spatial resolution carbon balance monitoring of the Amazonian frontier: Predisturbance and postdisturbance carbon emissions and uptake

2013 ◽  
Vol 118 (2) ◽  
pp. 400-411 ◽  
Author(s):  
Michael Toomey ◽  
Dar A. Roberts ◽  
Jill Caviglia-Harris ◽  
Mark A. Cochrane ◽  
Candida F. Dewes ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Carbon Emissions ◽  
Carbon Balance ◽  
High Spatial Resolution
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