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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.
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 StyleBarjeece 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 StyleBarjeece 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.
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.
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 StyleMehdi 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 StyleMehdi 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.
Monitoring global vegetation dynamics is of great importance for many environmental applications. The vegetation optical depth (VOD), derived from passive microwave observation, is sensitive to the water content in all aboveground vegetation and could serve as complementary information to optical observations for global vegetation monitoring. The microwave vegetation index (MVI), which is originally derived from the zero-order model, is a potential approach to derive VOD and vegetation water content (VWC), however, it has limited application at dense vegetation in the global scale. In this study, we preferred to use a more complex vegetation model, the Tor Vergata model, which takes into account multi-scattering effects inside the vegetation and between the vegetation and soil layer. Validation with ground-based measurements proved this model is an efficient tool to describe the microwave emissions of corn and wheat. The MVI has been derived through two methods: (i) polarization independent ( MVI B P ) and (ii) time invariant ( MVI B T ), based on model simulations at the L band. Results show that the MVI B T has a stronger sensitivity to vegetation properties compared with MVI B P . MVI B T is used to retrieve VOD and VWC, and the results were compared to physical VOD and measured VWC. Comparisons indicated that MVI B T has a great potential to retrieve VOD and VWC. By using L band time-series information, the performance of MVIs could be enhanced and its application in a global scale could be improved while paying attention to vegetation structure and saturation effects.
Somayeh Talebiesfandarani; Tianjie Zhao; Jiancheng Shi; Paolo Ferrazzoli; Jean-Pierre Wigneron; Mehdi Zamani; Peejush Pani. Microwave Vegetation Index from Multi-Angular Observations and Its Application in Vegetation Properties Retrieval: Theoretical Modelling. Remote Sensing 2019, 11, 730 .
AMA StyleSomayeh Talebiesfandarani, Tianjie Zhao, Jiancheng Shi, Paolo Ferrazzoli, Jean-Pierre Wigneron, Mehdi Zamani, Peejush Pani. Microwave Vegetation Index from Multi-Angular Observations and Its Application in Vegetation Properties Retrieval: Theoretical Modelling. Remote Sensing. 2019; 11 (6):730.
Chicago/Turabian StyleSomayeh Talebiesfandarani; Tianjie Zhao; Jiancheng Shi; Paolo Ferrazzoli; Jean-Pierre Wigneron; Mehdi Zamani; Peejush Pani. 2019. "Microwave Vegetation Index from Multi-Angular Observations and Its Application in Vegetation Properties Retrieval: Theoretical Modelling." Remote Sensing 11, no. 6: 730.
Increasing trends of urbanization lead to vegetation degradation in big cities and affect the urban thermal environment. This study investigated (1) the cooling effect of urban green space spatial patterns on Land Surface Temperature (LST); (2) how the surrounding environment influences the green space cool islands (GCI), and vice versa. The study was conducted in two Asian capitals: Beijing, China and Islamabad, Pakistan by utilizing Gaofen-1 (GF-1) and Landsat-8 satellite imagery. Pearson’s correlation and normalized mutual information (NMI) were applied to investigate the relationship between green space characteristics and LST. Landscape metrics of green spaces including Percentage of Landscape (PLAND), Patch Density (PD), Edge Density (ED), and Landscape Shape Index (LSI) were selected to calculate the spatial patterns of green spaces, whereas GCI indicators were defined by Green Space Range (GR), Temperature Difference (TD), and Temperature Gradient (TG). The results indicate that both vegetation composition and configuration influence LST distributions; however, vegetation composition appeared to have a slightly greater effect. The cooling effect can be produced more effectively by increasing green space percentage, planting trees in large patches with equal distribution, and avoiding complex-shaped green spaces. The GCI principle indicates that LST can be decreased by increasing the green space area, increasing the water body fraction, or by decreasing the fraction of impervious surfaces. GCI can also be strengthened by decreasing the fraction of impervious surfaces and increasing the fraction of water body or vegetation in the surrounding environment. The cooling effect of vegetation and water could be explained based on their thermal properties. Beijing has already enacted the green-wedge initiative to increase the vegetation canopy. While designing the future urban layout of Islamabad, the construction of artificial lakes within the urban green spaces would also be beneficial, as is the case with Beijing.
Shahid Naeem; Chunxiang Cao; Waqas Ahmed Qazi; Mehdi Zamani; Chen Wei; Bipin Kumar Acharya; Asid Ur Rehman. Studying the Association between Green Space Characteristics and Land Surface Temperature for Sustainable Urban Environments: An Analysis of Beijing and Islamabad. ISPRS International Journal of Geo-Information 2018, 7, 38 .
AMA StyleShahid Naeem, Chunxiang Cao, Waqas Ahmed Qazi, Mehdi Zamani, Chen Wei, Bipin Kumar Acharya, Asid Ur Rehman. Studying the Association between Green Space Characteristics and Land Surface Temperature for Sustainable Urban Environments: An Analysis of Beijing and Islamabad. ISPRS International Journal of Geo-Information. 2018; 7 (2):38.
Chicago/Turabian StyleShahid Naeem; Chunxiang Cao; Waqas Ahmed Qazi; Mehdi Zamani; Chen Wei; Bipin Kumar Acharya; Asid Ur Rehman. 2018. "Studying the Association between Green Space Characteristics and Land Surface Temperature for Sustainable Urban Environments: An Analysis of Beijing and Islamabad." ISPRS International Journal of Geo-Information 7, no. 2: 38.