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Barjeece Bashir
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

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vegetation dynamics

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Journal article
Published: 11 August 2021 in Remote Sensing
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Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6.

ACS Style

Talha Hassan; Jiahua Zhang; Foyez Ahmed Prodhan; Til Prasad Pangali Sharma; Barjeece Bashir. Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019). Remote Sensing 2021, 13, 3177 .

AMA Style

Talha Hassan, Jiahua Zhang, Foyez Ahmed Prodhan, Til Prasad Pangali Sharma, Barjeece Bashir. Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019). Remote Sensing. 2021; 13 (16):3177.

Chicago/Turabian Style

Talha Hassan; Jiahua Zhang; Foyez Ahmed Prodhan; Til Prasad Pangali Sharma; Barjeece Bashir. 2021. "Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019)." Remote Sensing 13, no. 16: 3177.

Journal article
Published: 30 April 2021 in Forests
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Accurate information on tree species is in high demand for forestry management and further investigations on biodiversity and environmental monitoring. Over regional or large areas, distinguishing tree species at high resolutions faces the challenges of a lack of representative features and computational power. A novel methodology was proposed to delineate the explicit spatial distribution of six dominant tree species (Pinus tabulaeformis, Quercus mongolia, Betula spp., Populus spp., Larix spp., and Armeniaca sibirica) and one residual class at 10 m resolution. Their spatial patterns were analyzed over an area covering over 90,000 km2 using the analysis-ready large volume of multisensor imagery within the Google Earth engine (GEE) platform afterwards. Random forest algorithm built into GEE was used together with the 20th and 80th percentiles of multitemporal features extracted from Sentinel-1/2, and topographic features. The composition of tree species in natural forests and plantations at the city and county-level were performed in detail afterwards. The classification achieved a reliable accuracy (77.5% overall accuracy, 0.71 kappa), and the spatial distribution revealed that plantations (Pinus tabulaeformis, Populus spp., Larix spp., and Armeniaca sibirica) outnumber natural forests (Quercus mongolia and Betula spp.) by 6% and were mainly concentrated in the northern and southern regions. Arhorchin had the largest forest area of over 4500 km2, while Hexingten and Aohan ranked first in natural forest and plantation area. Additionally, the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. We suggest focusing more on the suitable areas modeling for tree species using species’ distribution models and environmental factors based on the classification results rather than field survey plots in further studies.

ACS Style

Bo Xie; Chunxiang Cao; Min Xu; Robert Duerler; Xinwei Yang; Barjeece Bashir; Yiyu Chen; Kaimin Wang. Analysis of Regional Distribution of Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine. Forests 2021, 12, 565 .

AMA Style

Bo Xie, Chunxiang Cao, Min Xu, Robert Duerler, Xinwei Yang, Barjeece Bashir, Yiyu Chen, Kaimin Wang. Analysis of Regional Distribution of Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine. Forests. 2021; 12 (5):565.

Chicago/Turabian Style

Bo Xie; Chunxiang Cao; Min Xu; Robert Duerler; Xinwei Yang; Barjeece Bashir; Yiyu Chen; Kaimin Wang. 2021. "Analysis of Regional Distribution of Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine." Forests 12, no. 5: 565.

Journal article
Published: 29 March 2021 in Journal of Cleaner Production
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Aerosol pollution has become an increasingly serious problem in China. Among the multiple factors causing aerosol pollution, wildfires in China are occurring more frequently and have gradually become one of the most important contributing factors. However, little is known about their potential causality trends or spatial characteristics. In this research, satellite data of fire events and atmospheric aerosol datasets from 2001 to 2016 were applied in the geographical, statistical “Geodetector model” (GDM) to better understand their causal relationship. From long term observation data in China, we found that the increase in wildfires over the study period greatly enhanced their impacts on the aerosol optical depth (AOD) in recent years. The contribution of burning areas to AOD was 18.29% in 2001 and increased to 38.94% in 2016, and the contribution of fire radiative power (FRP) was 18.80% in 2001 and became 36.05% in 2016. In addition, seasonal research suggested that wildfires contributed rapidly to aerosol pollution, usually from April to September. The regional results in China showed that wildfires can be a relatively dominant factor for aerosol pollution in the southern regions, so that more importance should be attached to the complicated pollution conditions. Overall, our findings highlight the causal effects of wildfires on atmospheric aerosol pollution in China. We suggest that the rising contributions of wildfires to AOD in China should be noticed, and attention should be given to adaptions to local conditions regarding wildfires and aerosol pollution management.

ACS Style

Yiyu Chen; Chunxiang Cao; Yunfeng Cao; Barjeece Bashir; Min Xu; Bo Xie; Kaimin Wang. Observed evidence of the growing contributions to aerosol pollution of wildfires with diverse spatiotemporal distinctions in China. Journal of Cleaner Production 2021, 298, 126860 .

AMA Style

Yiyu Chen, Chunxiang Cao, Yunfeng Cao, Barjeece Bashir, Min Xu, Bo Xie, Kaimin Wang. Observed evidence of the growing contributions to aerosol pollution of wildfires with diverse spatiotemporal distinctions in China. Journal of Cleaner Production. 2021; 298 ():126860.

Chicago/Turabian Style

Yiyu Chen; Chunxiang Cao; Yunfeng Cao; Barjeece Bashir; Min Xu; Bo Xie; Kaimin Wang. 2021. "Observed evidence of the growing contributions to aerosol pollution of wildfires with diverse spatiotemporal distinctions in China." Journal of Cleaner Production 298, no. : 126860.

Erratum
Published: 26 December 2020 in Remote Sensing
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The authors wish to make the following corrections to this paper

ACS Style

Faisal Mumtaz; Yu Tao; Gerrit de Leeuw; Limin Zhao; Cheng Fan; Abdelrazek Elnashar; Barjeece Bashir; Gengke Wang; Lingling Li; Shahid Naeem; Arfan Arshad; Dakang Wang. Erratum: Mumtaz, F., et al. Modeling Spatio-Temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST). Remote Sens. 2020, 12, 2987. Remote Sensing 2020, 13, 61 .

AMA Style

Faisal Mumtaz, Yu Tao, Gerrit de Leeuw, Limin Zhao, Cheng Fan, Abdelrazek Elnashar, Barjeece Bashir, Gengke Wang, Lingling Li, Shahid Naeem, Arfan Arshad, Dakang Wang. Erratum: Mumtaz, F., et al. Modeling Spatio-Temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST). Remote Sens. 2020, 12, 2987. Remote Sensing. 2020; 13 (1):61.

Chicago/Turabian Style

Faisal Mumtaz; Yu Tao; Gerrit de Leeuw; Limin Zhao; Cheng Fan; Abdelrazek Elnashar; Barjeece Bashir; Gengke Wang; Lingling Li; Shahid Naeem; Arfan Arshad; Dakang Wang. 2020. "Erratum: Mumtaz, F., et al. Modeling Spatio-Temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST). Remote Sens. 2020, 12, 2987." Remote Sensing 13, no. 1: 61.

Journal article
Published: 06 November 2020 in Remote Sensing
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Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.

ACS Style

Xiaojuan Lin; Min Xu; Chunxiang Cao; Yongfeng Dang; Barjeece Bashir; Bo Xie; Zhibin Huang. Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry. Remote Sensing 2020, 12, 3649 .

AMA Style

Xiaojuan Lin, Min Xu, Chunxiang Cao, Yongfeng Dang, Barjeece Bashir, Bo Xie, Zhibin Huang. Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry. Remote Sensing. 2020; 12 (21):3649.

Chicago/Turabian Style

Xiaojuan Lin; Min Xu; Chunxiang Cao; Yongfeng Dang; Barjeece Bashir; Bo Xie; Zhibin Huang. 2020. "Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry." Remote Sensing 12, no. 21: 3649.

Journal article
Published: 14 September 2020 in Remote Sensing
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Land use land cover (LULC) of city regions is strongly affected by urbanization and affects the thermal environment of urban centers by influencing the surface temperature of core city areas and their surroundings. These issues are addressed in the current study, which focuses on two provincial capitals in Pakistan, i.e., Lahore and Peshawar. Using Landsat data, LULC is determined with the aim to (a) examine the spatio-temporal changes in LULC over a period of 20 years from 1998 to 2018 using a CA-Markov model, (b) predict the future scenarios of LULC changes for the years 2023 and 2028, and (c) study the evolution of different LULC categories and investigate its impacts on land surface temperature (LST). The results for Peshawar city indicate the significant expansion in vegetation and built-up area replacing barren land. The vegetation cover and urban area of Peshawar have increased by 25.6%, and 16.3% respectively. In contrast, Lahore city urban land has expanded by 11.2% while vegetation cover decreased by (22.6%). These transitions between LULC classes also affect the LST in the study areas. Transformation of vegetation cover and water surface into built-up areas or barren land results in the increase in the LST. In contrast, the transformation of urban areas and barren land into vegetation cover or water results in the decrease in LST. The different LULC evolutions in Lahore and Peshawar clearly indicate their effects on the thermal environment, with an increasing LST trend in Lahore and a decrease in Peshawar. This study provides a baseline reference to urban planners and policymakers for informed decisions.

ACS Style

Faisal Mumtaz; Yu Tao; Gerrit de Leeuw; Limin Zhao; Cheng Fan; Abdelrazek Elnashar; Barjeece Bashir; Gengke Wang; Lingling Li; Shahid Naeem; Arfan Arshad; Dakang Wang. Modeling Spatio-Temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST). Remote Sensing 2020, 12, 2987 .

AMA Style

Faisal Mumtaz, Yu Tao, Gerrit de Leeuw, Limin Zhao, Cheng Fan, Abdelrazek Elnashar, Barjeece Bashir, Gengke Wang, Lingling Li, Shahid Naeem, Arfan Arshad, Dakang Wang. Modeling Spatio-Temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST). Remote Sensing. 2020; 12 (18):2987.

Chicago/Turabian Style

Faisal Mumtaz; Yu Tao; Gerrit de Leeuw; Limin Zhao; Cheng Fan; Abdelrazek Elnashar; Barjeece Bashir; Gengke Wang; Lingling Li; Shahid Naeem; Arfan Arshad; Dakang Wang. 2020. "Modeling Spatio-Temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST)." Remote Sensing 12, no. 18: 2987.

Journal article
Published: 13 August 2020 in Remote Sensing
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Land degradation reflected by vegetation is a commonly used practice to monitor desertification. To retrieve important information for ecosystem management accurate assessment of desertification is necessary. The major factors that drive vegetation dynamics in arid and semi-arid regions are climate and anthropogenic activities. Progression of desertification is expected to exacerbate under future climate change scenarios, through precipitation variability, increased drought frequency and persistence of dry conditions. This study examined spatiotemporal vegetation dynamics in arid regions of Sindh, Pakistan, using annual and growing season Normalized Difference Vegetation Index (NDVI) data from 2000 to 2017, and explored the climatic and anthropogenic effects on vegetation. Results showed an overall upward trend (annual 86.71% and growing season 82.7%) and partial downward trend (annual 13.28% and growing season 17.3%) in the study area. NDVI showed the highest significant increase in cropland region during annual, whereas during growing season the highest significant increase was observed in savannas. Overall high consistency in future vegetation trends in arid regions of Sindh province is observed. Stable and steady development region (annual 48.45% and growing 42.80%) dominates the future vegetation trends. Based on the Hurst exponent and vegetation dynamics of the past, improvement in vegetation cover is predicted for a large area (annual 44.49% and growing 30.77%), and a small area is predicted to have decline in vegetation activity (annual 0.09% and growing 3.04%). Results revealed that vegetation growth in the study area is a combined result of climatic and anthropogenic factors; however, in the future multi-controls are expected to have a slightly larger impact on annual positive development than climate whereas positive development in growing season is more likely to continue in future under the control of climate variability.

ACS Style

Barjeece Bashir; Chunxiang Cao; Shahid Naeem; Mehdi Zamani Joharestani; Xie Bo; Huma Afzal; Kashif Jamal; Faisal Mumtaz. Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors. Remote Sensing 2020, 12, 2612 .

AMA Style

Barjeece Bashir, Chunxiang Cao, Shahid Naeem, Mehdi Zamani Joharestani, Xie Bo, Huma Afzal, Kashif Jamal, Faisal Mumtaz. Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors. Remote Sensing. 2020; 12 (16):2612.

Chicago/Turabian Style

Barjeece Bashir; Chunxiang Cao; Shahid Naeem; Mehdi Zamani Joharestani; Xie Bo; Huma Afzal; Kashif Jamal; Faisal Mumtaz. 2020. "Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors." Remote Sensing 12, no. 16: 2612.

Journal article
Published: 22 January 2020 in Remote Sensing
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Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models.

ACS Style

Bo Xie; Chunxiang Cao; Min Xu; Barjeece Bashir; Ramesh P. Singh; Zhibin Huang; Xiaojuan Lin. Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data. Remote Sensing 2020, 12, 360 .

AMA Style

Bo Xie, Chunxiang Cao, Min Xu, Barjeece Bashir, Ramesh P. Singh, Zhibin Huang, Xiaojuan Lin. Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data. Remote Sensing. 2020; 12 (3):360.

Chicago/Turabian Style

Bo Xie; Chunxiang Cao; Min Xu; Barjeece Bashir; Ramesh P. Singh; Zhibin Huang; Xiaojuan Lin. 2020. "Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data." Remote Sensing 12, no. 3: 360.

Journal article
Published: 02 January 2020 in Remote Sensing
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Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources.

ACS Style

Zhibin Huang; Chunxiang Cao; Wei Chen; Min Xu; Yongfeng Dang; Ramesh P. Singh; Barjeece Bashir; Bo Xie; Xiaojuan Lin. Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts. Remote Sensing 2020, 12, 156 .

AMA Style

Zhibin Huang, Chunxiang Cao, Wei Chen, Min Xu, Yongfeng Dang, Ramesh P. Singh, Barjeece Bashir, Bo Xie, Xiaojuan Lin. Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts. Remote Sensing. 2020; 12 (1):156.

Chicago/Turabian Style

Zhibin Huang; Chunxiang Cao; Wei Chen; Min Xu; Yongfeng Dang; Ramesh P. Singh; Barjeece Bashir; Bo Xie; Xiaojuan Lin. 2020. "Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts." Remote Sensing 12, no. 1: 156.

Journal article
Published: 04 July 2019 in Atmosphere
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In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM2.5, and geographical data, in the modeling. The best model performance obtained was R2 = 0.81 (R = 0.9), MAE = 9.93 µg/m3, and RMSE = 13.58 µg/m3 using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R2 varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM2.5 lag data, satellite-derived AODs did not improve model performance.

ACS Style

Mehdi Zamani Joharestani; Chunxiang Cao; Xiliang Ni; Barjeece Bashir; Somayeh Talebiesfandarani. PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. Atmosphere 2019, 10, 373 .

AMA Style

Mehdi Zamani Joharestani, Chunxiang Cao, Xiliang Ni, Barjeece Bashir, Somayeh Talebiesfandarani. PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. Atmosphere. 2019; 10 (7):373.

Chicago/Turabian Style

Mehdi Zamani Joharestani; Chunxiang Cao; Xiliang Ni; Barjeece Bashir; Somayeh Talebiesfandarani. 2019. "PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data." Atmosphere 10, no. 7: 373.