Khansa Gulshad

Rapid urbanization influences green infrastructure (GI) development in cities. The government plans to optimize GI in urban areas, which requires understanding GI spatiotemporal trends in urban areas and driving forces influencing their pattern. Traditional GIS-based methods, used to determine the greening potential of vacant land in urban areas, are incapable of predicting future scenarios based on the past trend. Therefore, we propose a heterogeneous ensemble technique to determine the spatial pattern of GI development in Jinan, China, based on driving biophysical and socioeconomic factors. Data-driven artificial neural networks (ANN) and random forests (RF) are selected as base learners, while support vector machine (SVM) is used as a meta classifier. Results showed that the stacking model ANN-RF-SVM achieved the best test accuracy (AUC 0.941) compared to the individual ANN, RF, and SVM algorithms. Land surface temperature, distance to water bodies, population density, and rainfall are found to be the most influencing factors regarding vacant land conversion to GI in Jinan.

The temporal analysis of land surface temperature (LST) has generally been studied using data from the same season, as temperature varies greatly over time. However, the cloud cover in thermal remotely sensed images and the coarse resolution of passive sensor system significantly limits data availability of same season for comparative temporal analysis in many parts of the world. To address this problem, we propose a new method for temporal monitoring of surface temperature based on LST normalization (LSTn); deploying the average open water temperature to normalize LST when monitoring temporal change in the surface temperature of newly coastal reclaimed areas. This method was applied in the Lingding Bay area, Guangdong Province, Southern China. Original LST and LSTn values were calculated for years 1987, 1997, 2007, and 2017. In contrast to the original LST, results show that LSTn can reduce seasonal variability when monitoring temporal change in surface temperatures. Additionally, ...

Classification and ordination of vegetation of Mughal Garden, Wah, Pakistan was done along with assessment of diversity status. A total of 45 species were recorded in vegetation survey belonging to 24 families with Asteraceae and Poaceae being the largest families. Herbs dominated the flora of Wah Garden by 44.4%, shrubs 15.5%, trees 13.3%, grasses 11.1%, creeping herbs 11.1%, ferns 2.2% and aquatic herbs 2.2%. About 35.5% species were annuals, 28.8% perennials, 13.3% annuals or perennials, 8.8% annuals or biennials, 8.8% deciduous, 2.2% coniferous and evergreen species. In case of life form of species, Therophytes and Megaphanerophytes were the most prevalent among species indirect ordination techniques TWINSPAN and DCA were employed that produced two major groups which were further divided into five communities and three major groups, respectively. Shannon-Wiener diversity index, Simpson Index of diversity and Hill’s N1 and N2 diversity numbers were calculated and verified by data...

Vegetation plays significant role in ecology and is limited to their ecological optimum. Vegetationcomposition is function of changing habitat conditions along the soil gradients. Soil gradients affect distribution,growth and composition of vegetation within space and time. The vegetation data of Mughal Garden Wah wascollected along with soil samples for determining impact of soil parameters on plants growth and survival. A total of 50quadrat each measuring 1×1 square meter were applied for vegetation sampling. The soils at a depth of 5-15cm werealso collected from the study area. The ordination, CCA biplot and its sub-technique t-value biplot were established todetermine correlation between identified species and environmental parameters. The T-value bi-plot of organic matter(O.M) revealed that Ziziphus nummularia, Adiantum capillus-veneris, Sisymbrium irio and Solanum nigrum werestrongly correlated with it. There was strong relationship of the most abundant species such as Cannabi...