Measuring the effect of an ongoing urbanization process on biodiversity conservation suitability index: integrating scenario-based urban growth modelling with Conservation Assessment and Prioritization System (CAPS)

Goodarzi, Mehdi Sheikh; Sakieh, Yousef; Navardi, Shabnam

doi:10.1080/10106049.2017.1299799

Cited by 8 publications

(6 citation statements)

References 40 publications

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“…In other words, a polycentric pattern of growth instead of high-density urban development is emphasized and urban vegetation covers under such pattern have a better chance to survive for encroachment of builtstructures. As indicated in previous studies (Sakieh et al 2015;Afrakhteh et al 2016;Sakieh et al 2017), such pattern of urban growth allocation is associated with many ecological benefits such as lower LST values, calmer landscapes and higher land potential for urban construction activities and biodiversity conservation (Goodarzi et al 2017). In addition, Afrakhteh et al (2016) highlights this strategy under the concept of smart rural development in central Iran.…”

Section: 4 Planning Implicationsmentioning

confidence: 70%

Spatiotemporal Monitoring of Thermal Environment in Isfahan Metropolitan Area

Bozorgi

Nejadkoorki

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Urban Heat Island (UHI) phenomenon is regarded as one of the most critical issues in cities caused by unsustainable urban development. UHI has significant effects on air quality and energy consumption of urban buildings and it directly affects life quality the citizens. The current study aims to analyze and evaluate the spatiotemporal changes of thermal environment in Isfahan Metropolitan Area (IMA), Iran, during 1998-2014 time profile. A mono-window algorithm was applied to extract Land Surface Temperature (LST) values from Landsat thermal bands (TM and OLI sensors). Temporal and spatial changes in IMA’s thermal environment were then analyzed using statistical methods, Mann-Kendall analysis and Urban-Heat-Island Ration Index (URI). Results indicated LST in IMA is of an increasing trend and the intensity of this trend is mainly concentrated in northwest and northeast of the city and around Zayandeh-Rood River in the south. This arrangement of UHI distribution is primarily due to compact and high-density patterns of city development in its boundaries, destruction of Zayandeh-Rood riverine ecosystem and its drying up during the last decade as well as excessive consumption of green covers by urban structures. Finally, it was concluded that the URI has witnessed an increasing trend from 0.25 in 1998 to 0.312 in 2014, which is in line with the spatiotemporal dynamics of thermal environment in the study location. The results of this study provide practical planning implications for identifying UHI hotspots in growing cities and optimization of green cover plantations to alleviate UHI effects in urban environments.

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Section: 4 Planning Implicationsmentioning

confidence: 70%

Spatiotemporal Monitoring of Thermal Environment in Isfahan Metropolitan Area

Bozorgi

Nejadkoorki

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…A forecast based on fewer observations (mono-temporal, bi-temporal) is possible but is only made in few and special cases. We observed mono-temporal forecasts only in studies, in which projected non-EO data is combined with EO-derived recent or future LULC maps [65,78,134,138,143,[156][157][158]168]. The same occurs in two bi-temporal projection-based studies by Patil et al [175] and Yao et al [177], while others employ transition probabilities or transition vectors between two time steps to forecast future conditions [65,81,83,174].…”

Section: Temporal Scopementioning

confidence: 85%

“…In general, however, next to OLS, machine learning methods are used much less frequently in this forecast model category. Of the 22 projection-based forecasting studies identified in this review, 15 were based on climate projections [78,85,130,131,134,142,[154][155][156][168][169][170]175,177,178], six on LULC projections [65,143,144,157,158,167], and one study on both [118].…”

Section: Categorization Of Forecasting Methodsmentioning

confidence: 99%

“…In a direct comparison by Ozturk [60] the MC-MLP approach resulted in a slightly higher simulation accuracy than MC-CA. A specialized method, which is established in urban LULC forecasting, is the CA-based SLEUTH model [61][62][63]143]. It has been calibrated and used for many cities around the world with good results but has a fixed set of input parameters and cannot be integrated with other models [35].…”

Section: Categorization Of Forecasting Methodsmentioning

confidence: 99%

“…Biophysical parameters have been forecast by Jiang et al [141], who compared time-series based forecasting methods for LAI, by Donmez et al [142] forecasting ecosystem productivity via NPP in a Mediterranean pine forest, and by Shrestha et al [131] who forecast LAI, GPP, and leaf chlorophyll content in a mangrove forest. Based on different urban growth scenarios Sheikh Goodarzi et al [143] simulated future conservation suitability. Arantes et al [144] forecast evapotranspiration based on different EVI and LULC scenarios in the Brazilian Cerrado biome, while Park et al [145] developed a short-term drought forecast system utilizing satellite-based NDVI, land surface temperature and rainfall products, as well as oscillation indices.…”

Section: Biospherementioning

confidence: 99%

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Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Koehler

Kuenzer

2020

Remote Sensing

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Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.

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Land surface temperature estimating in urbanized landscapes using artificial neural networks

Bozorgi

Nejadkoorki

Mousavi

2018

Environ Monit Assess

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Scenario-based land surface temperature (LST) modeling is a powerful tool for adopting proper urban land use planning policies. In this study, using greater Isfahan as a case study, the artificial neural network (ANN) algorithm was utilized to explore the non-linear relationships between urban LST and green cover spatial patterns derived from Landsat 8 OLI imagery. The model was calibrated using two sets of variables: Normalized Difference Built Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Furthermore, Compact Development Scenario (CDS) and Green Development Scenario (GDS) were defined. The results showed that GDS is more successful in mitigating urban LST (mean LST = 40.93) compared to CDS (mean LST = 44.88). In addition, urban LST retrieved from the CDS was more accurate in terms of ANOVA significance (sig = 0.043) than the GDS (sig = 0.010). The findings of this study suggest that developing green spaces is a key strategy to combat against the risk of LST concerns in urban areas.

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Measuring the effect of an ongoing urbanization process on biodiversity conservation suitability index: integrating scenario-based urban growth modelling with Conservation Assessment and Prioritization System (CAPS)

Cited by 8 publications

References 40 publications

Spatiotemporal Monitoring of Thermal Environment in Isfahan Metropolitan Area

Spatiotemporal Monitoring of Thermal Environment in Isfahan Metropolitan Area

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Land surface temperature estimating in urbanized landscapes using artificial neural networks

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