An approach to analyzing the intensity of the daytime surface urban heat island effect at a local scale

A landscape index LI is proposed to evaluate the intensity of the daytime surface urban heat island (SUHI) effect at a local scale. Three aspects of this landscape index are crucial: the source landscape, the sink landscape, and the contribution of source and sink landscapes to the intensity of the SUHI. Source and sink landscape types are identified using the thermo-band of Landsat 7 with a spatial resolution of 60 m, along with appropriate threshold values for the Normalized Difference Vegetation Index, Modified Normalized Difference Water Index, and Normalized Difference Built-up Index. The landscape index was defined as the ratio of the contributions of the source and sink landscapes to the intensity of the SUHI. The intensity of the daytime SUHI is assessed with the help of the landscape index. Our analysis indicates the landscape index can be used to evaluate and compare the intensity of the daytime SUHI for different areas.     

Land surface temperature estimating in urbanized landscapes using artificial neural networks

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.     

Spatio-temporal footprints of urbanisation in Surat, the Diamond City of India (1990–2009)

Urbanisation is a ubiquitous phenomenon with greater prominence in developing nations. Urban expansion involves land conversions from vegetated moisture-rich to impervious moisture-deficient land surfaces. The urban land transformations alter biophysical parameters in a mode that promotes development of heat islands and degrades environmental health. This study elaborates relationships among various environmental variables using remote sensing dataset to study spatio-temporal footprint of urbanisation in Surat city. Landsat Thematic Mapper satellite data were used in conjugation with geo-spatial techniques to study urbanisation and correlation among various satellite-derived biophysical parameters, [Normalised Difference Vegetation Index, Normalised Difference Built-up Index, Normalised Difference Water Index, Normalised Difference Bareness Index, Modified NDWI and land surface temperature (LST)]. Land use land cover was prepared using hierarchical decision tree classification with an accuracy of 90.4 % (kappa?=?0.88) for 1990 and 85 % (kappa?=?0.81) for 2009. It was found that the city has expanded over 42.75 km² within a decade, and these changes resulted in elevated surface temperatures. For example, transformation from vegetation to built-up has resulted in 5.5?±?2.6 °C increase in land surface temperature, vegetation to fallow 6.7?±?3 °C, fallow to built-up is 3.5?±?2.9 °C and built-up to dense built-up is 5.3?±?2.8 °C. Directional profiling for LST was done to study spatial patterns of LST in and around Surat city. Emergence of two new LST peaks for 2009 was observed in N–S and NE–SW profiles.     

The spatiotemporal dynamics of urbanisation and local climate: A case study of Islamabad,Pakistan

Rapid and unplanned urbanisation, together with climate change, are increasingly affecting the local climatic conditions of urban settlements. Spatiotemporal analysis using land use/land cover (LULC), land surface temperature (LST), and local climatic zone (LCZ) assessments have been helpful in understanding the urbanisation characteristics and morphology. Islamabad, the capital and the only planned city of Pakistan, has witnessed a consistent rise in local temperatures, increased built-up areas, and reduced vegetation cover during the past decades. This study explores the spatiotemporal dynamics of LULC, LST, and LCZ in Islamabad using satellite remote sensing data and spectral indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The results indicate a whopping increase in a built-up area in the city (113% during 2013 and 2019). A positive correlation between LST and NDBI, whereas a negative correlation between LST and NDVI clearly indicates how urbanisation (and reduction in vegetation cover) are impacting the local temperatures. Assessment and analysis of LCZs helped to understand the variations and deviations of current LULC from the master plan. It was observed that compact low-rise urban development is the most prevalent. The outcomes of this study are expected to inform the urban planners, climatologists, and policymakers with the knowledge helpful for devising climate-resilient development policies that could reduce thermal stresses in the capital cities.     

基于TM资料对上海区域热环境特征的分析

为了揭示城市热岛(UHI)形成机制,以TM卫星遥感资料为主要数据源,结合地面自动气象站(AWS)实测的气象资料,利用地表能量平衡参数化(SEBAL)的方法估算了净辐射通量和土壤热通量,分析了上海区域热环境特征.结果表明,TM地表温度与AWS实测地表温度具有较好的一致性；不同类型地表其地表温度差异明显,城市道路是城市中重要热量来源,绿地和水体面积的增加能有效地减轻城市热岛强度,同时也表明了卫星遥感资料在城市气候环境研究中的潜在价值.     

A change vector analysis technique for monitoring land cover changes in Copsa Mica,Romania, in the period 1985–2011

During the communist regime, Romania’s planned economy focused exclusively on production neglecting the environment protection. The lack of less polluting production technologies and of environmental protection measures led to excessive pollution in certain industrialized areas. This is the case of the town of Copsa Mica in Sibiu County, which in 1987 was considered one of the most polluted towns in Europe. The present study assesses the change vector analysis (CVA) technique using a Landsat Thematic Mapper (TM) image time series to monitor land cover changes caused by carbon black and heavy metal pollution. CVA was applied to the tasseled cap greenness (TCG) and tasseled cap brightness (TCB) indices, as well as to the Normalized Difference Vegetation Index (NDVI) and bare soil index (BI). Various maps were generated for the periods 1985–1994, 1994–2003, 2003–2011, and 1985–2011, and threshold values were determined for the detection of land cover change/no change. The change direction and magnitude values were cross-tabulated and classified. The technique was assessed based on the change versus no-change error matrix. The results show that in the area of Copsa Mica, land cover changes occurred because of a considerable decrease in the area affected by carbon black and heavy metal pollution. The CVA technique proved efficient in monitoring the land cover changes caused by pollution and especially by carbon black pollution. Soil pollution by heavy metals is reflected in the bare soil surfaces present in the imagery.     

Assessment of some remote sensing techniques used to detect land use/land cover changes in South-East Transilvania,Romania

This paper assesses the image differencing technique for the Normalized Difference Vegetation Index (NDVI), the second principal component (PC2), and the TM 4 band (TM 4), as well as the post-classification comparison (PCC) in order to analyze the land use/land cover changes in the South-East Transilvania, Romania. The analysis was performed using two frames from Landsat 5 TM satellite images acquired on August 5, 1993 and July 24, 2009. After applying the NDVI, PC2, and TM 4 image differencing techniques, the images obtained were transformed into change/no change maps. The thresholds identified to highlight the changes were set at 0.6 s for NDVI and 0.7 s for PC2 and TM 4. Before applying the PCC technique, the satellite images were classified through the supervised classification method. The overall accuracy obtained was 85.91 % and the kappa statistics 0.8249 for 1993, 88.18 % and 0.8497 for 2009, respectively. The assessment of the changes detection methods in the studied area shows that the first place is occupied by NDVI image differencing with an overall accuracy of 83.80 %, followed by PCC method with 83.20 %, PC2 difference with an overall accuracy of 81.60 %, and TM 4 difference with an overall accuracy of 79.40 %.     

Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area

Istanbul is the most populated city of Turkey with a population of around 10.58 M (2000) living on around 5,750 km². In 1980, the population was only 4.7 M and then it has been more than doubled in only two decades. The population has been increasing as a result of mass immigration. An urbanization process continues and it causes serious increases in urban areas while decreasing the amount of green areas. This rapid, uncontrolled, and illegal urbanization accompanied by insufficient infrastructure has caused degradation of forest and barren lands in the metropolitan area, especially through the last two decades. The watershed basins inside the metropolitan area and the transportation network have accelerated the land-cover changes, which have negative impacts on water quality of the basins. Monitoring urban growth and land cover change will enable better management of this complex urban area by the Greater Istanbul Metropolitan Municipality (GIMM). A temporal assessment of land-cover changes of Istanbul has been documented in this study. The study mainly focuses on the acquisition and analysis of Landsat TM and Landsat GeoCover LC satellite images reflecting the significant land-cover changes between the years of 1990 and 2005. Raster data were converted to vector data and used in Geographic Information Systems (GIS). A database was created for Istanbul metropolitan area to plan, manage, and utilize statistical attribute data covering population, water, forest, industry, and topographic position. Consequently an overlay analysis was carried out and land use/cover changes through years have been detected for the case study area. The capability of Landsat images in determining the alterations in the macro form of the city are also discussed.     

Temporal and spatial characteristics of the urban heat island during rapid urbanization in Shanghai, China

Based on meteorological data from 1978 to 2007, we investigated the annual, seasonal, and fixed hourly variations of Shanghai urban heat island (UHI) in urban and suburban areas and spatial distribution of UHI. The results demonstrated a remarkable interannual increase of UHI intensity in Shanghai. The UHI was the strongest in autumn and the weakest in summer, as a consequence of the prevailing weather conditions. Similar to previous studies of other cities, the UHI in Shanghai was stronger in the nighttime than in the daytime. The SW–NE cross section followed the general cross section of the typical UHI described by Oke (1987) who defines its characteristic parts as “cliff”, “plateau”, and “peak”. Analysis of the association of UHI and urbanization indicated that the UHI increased with the expansion of population and rapid increase of gross domestic product. The continuous increase of power consumption and area of paved road and decrease of area of cropland caused the growth of UHI intensity. Green land had a positive effect on mitigation of heat island based on an inversed U-shaped curve with UHI intensity.     

西藏拉萨市热岛效应及其影响因子分析

采用2001年、2004年以及2007年三年的EOS/MODIS遥感信息反演的地面温度以及多年常规气象观测资料,讨论了拉萨市热岛现象及其可能影响因子。结果表明:(1)热岛强度的年、季节变化呈现逐渐增强的趋势,其中,冬季的热岛强度最强,其次是春季,秋季和夏季的热岛效应较弱;高温区基本位于城市中心或者县城所在地及其周围,低温区主要集中在各县的郊区;近年来拉萨地区的城市高温区域逐渐扩大,有些高温中心可能向某些区域偏移;遥感资料所获取的地表温度与平均气温之间存在一定的正相关性。(2)无论是年变化,还是季节变化,热岛强度都与风速呈正相关,与日照时数呈负相关,与蒸发量的相关在夏季和冬季分别呈正相关、负相关的相反状况;地表温度与植被分布具有较好的负相关关系,即在城区存在较高的地表温度分布和较小的NDVI,过渡到郊区具有温度减小、NDVI增加的特征;随着城市化进程的加剧,建筑面积不断扩大,人类活动明显增加,排放至大气的人为热增加,这些因素都可能导致热岛强度的增强。     

An energy and mortality impact assessment of the urban heat island in the US

Increased summer energy use and increased summer heat related mortality are the two most cited detrimental impacts of the urban heat island (UHI). An assessment of these impacts was made that considered the annual impact of the UHI, not just the summer impact. It was found that in north of the US there was a net decrease in energy use from the UHI, as heating energy reductions were larger than the increase in cooling energy. In the south there was a net energy increase from the UHI. The impact of the UHI on heat related deaths was an estimated increase of 1.1 deaths per million people. The impact of the UHI on cold related deaths was an estimated decrease of 4.0 deaths per million people. These estimates are caveated by the acknowledgement that compounding factors influence mortality. Hypothermia related death rates were three times higher in rural areas than urban areas. This is surprising as the homeless population is usually considered the most at risk, yet they mostly live in urban areas.     

Ecosystem evaluation (1989–2012) of Ramsar wetland Deepor Beel using satellite-derived indices

The unprecedented urban growth especially in developing countries has laid immense pressure on wetlands, finally threatening their existence altogether. A long-term monitoring of wetland ecosystems is the basis of planning conservation measures for a sustainable development. Deepor Beel, a Ramsar wetland and major storm water basin of the River Brahmaputra in the northeastern region of India, needs particular attention due to its constant degradation over the past decades. A rule-based classification algorithm was developed using Landsat (2011)-derived indices, namely Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), Normalised Difference Pond Index (NDPI), Normalised Difference Vegetation Index (NDVI) and field data as ancillary information. Field data, ALOS AVNIR and Google Earth images were used for accuracy assessment. A fuzzy accuracy assessment of the classified data sets showed an overall accuracy of 82 % for MAX criteria and 90 % for RIGHT criteria. The rules were used to classify major wetland cover types during low water season (January) in 1989, 2001 and 2012. The statistical analysis of the classified wetland showed heavy manifestation in aquatic vegetation and other features indicating severe eutrophication over the past 23 years. This degradation was closely related to major contributing anthropogenic factors, such as a railway line construction, growing croplands, waste disposal and illegal human settlements in the wetland catchment. In addition, the landscape development index (LDI) indicated a rapid increase in the impact of the surrounding land use on the wetland from 1989 to 2012. The techniques and results from this study may prove useful for top-down landscape analyses of this and other freshwater wetlands.     

Inter-comparison of remote sensing sensing-based shoreline mapping techniques at different coastal stretches of India

Many techniques are available for detection of shorelines from multispectral satellite imagery, but the choice of a certain technique for a particular study area can be tough. Hence, for the first time in literature, an inter-comparison of the most widely used shoreline mapping techniques such as Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Improved Band Ratio (IBR) Method, and Automatic Water Extraction Index (AWEI) has been done along four different coastal stretches of India using multitemporal Landsat data. The obtained results have been validated with the high-resolution images of Cartosat-2 (panchromatic) and multispectral images from Google Earth. Performance of the above indices has been analyzed based on the statistics, such as overall accuracy, kappa coefficient, user’s accuracy, producer’s accuracy, and the average deviation from the reference line. It is observed that the performance of NDWI and IBR techniques are dependent on the physical characteristics of the sites, and therefore, it varies from one site to another. Results indicate that unlike these two indices, the AWEI algorithm performs consistently well followed by MNDWI irrespective of the land cover types.     

Analysis of multi-temporal landsat satellite images for monitoring land surface temperature of municipal solid waste disposal sites

This studypresents a remote sensing application of using time series Landsat satellite images for monitoring the Trail Road and Nepean municipal solid waste (MSW) disposal sites in Ottawa, Ontario, Canada. Currently, the Trail Road landfill is in operation; however, during the 1960s and 1980s, the city relied heavily on the Nepean landfill. More than 400 Landsat satellite images were acquired from the US Geological Survey (USGS) data archive between 1984 and 2011. Atmospheric correction was conducted on the Landsat images in order to derive the landfill sites’ land surface temperature (LST). The findings unveil that the average LST of the landfill was always higher than the immediate surrounding vegetation and air temperature by 4 to 10 °C and 5 to 11.5 °C, respectively. During the summer, higher differences of LST between the landfill and its immediate surrounding vegetation were apparent, while minima were mostly found in fall. Furthermore, there was no significant temperature difference between the Nepean landfill (closed) and the Trail Road landfill (active) from 1984 to 2007. Nevertheless, the LST of the Trail Road landfill was much higher than the Nepean by 15 to 20 °C after 2007. This is mainly due to the construction and dumping activities (which were found to be active within the past few years) associated with the expansion of the Trail Road landfill. The study demonstrates that the use of the Landsat data archive can provide additional and viable information for the aid of MSW disposal site monitoring.     

Impacts of urban growth on forest cover in Istanbul (1987–2007)

This article reports findings of a study that examined the impacts of urban growth on forest cover in Istanbul between 1987 and 2007. Four Landsat images from 1987, 1990, 2000, and 2007 were classified with maximum likelihood supervised classification method by using ERDAS IMAGINE 9.1. Forest and urban areas were given highest importance in the classification, while other land-use characteristics, like agriculture and bare soil, were grouped into a third class as others. The study revealed that rapid increase in population and accompanying unplanned urban growth in Istanbul resulted in significant changes in land use after 1987. Urban areas have expanded 87.9% from 1987 to 2007, while forest areas declined 5.4% in the same period. One of the most significant results of the study was that total forest areas in Istanbul have increased 0.3% between 2000 and 2007. This suggests that progress has been made in Istanbul not only in having sustainable urban growth, but also in preserving, restoring, and even expanding forest areas, especially after the year 2000.     

Monitoring the changes in impervious surface ratio and urban heat island intensity between 1987 and 2011 in Szeged,Hungary

Landsat time series data make it possible to continuously map and examine urban land cover changes and effects on urban environments. The objectives of this study are (1) to map and analyse an impervious surface and its changes within a census district and (2) to monitor the effects of increasing impervious surface ratios on population and environment. We used satellite images from 1987, 2003 and 2011 to map the impervious surface ratio in the census district of Szeged, Hungary through normalized spectral mixture analysis. Significant increases were detected from 1987 to 2011 in industrial areas (5.7–9.1%) and inner residential areas (2.5–4.8%), whereas decreases were observed in the city centre and housing estates due to vegetation growth. Urban heat island (UHI) values were derived from the impervious surface fraction map to analyse the impact of urban land cover changes. In 2011, the average value in the industrial area was 1.76 °C, whereas that in the inner residential area was 1.35–1.69 °C. In the city centre zones and housing estates, values ranging from 1.4 to 1.5 °C and from 1.29 to 1.5 °C, respectively, were observed. Our study reveals that long-term land cover changes can be derived at the district level from Landsat images and that their effects can be identified and analysed, providing important information for city planners and policy makers.     

A Special Vegetation Index for the Weed Detection in Sensor Based Precision Agriculture

Many technologies in precision agriculture (PA) require image analysis and image- processing with weed and background differentiations. The detection of weeds on mulched cropland is one important image-processing task for sensor based precision herbicide applications. The article introduces a special vegetation index, the Difference Index with Red Threshold (DIRT), for the weed detection on mulched croplands. Experimental investigations in weed detection on mulched areas point out that the DIRT performs better than the Normalized Difference Vegetation Index (NDVI). The result of the evaluation with four different decision criteria indicate, that the new DIRT gives the highest reliability in weed/background differentiation on mulched areas. While using the same spectral bands (infrared and red) as the NDVI, the new DIRT is more suitable for weed detection than the other vegetation indices and requires only a small amount of additional calculation power. The new vegetation index DIRT was tested on mulched areas during automatic ratings with a special weed camera system. The test results compare the new DIRT and three other decision criteria: the difference between infrared and red intensity (Diff), the soil-adjusted quotient between infrared and red intensity (Quotient) and the NDVI. The decision criteria were compared with the definition of a worse case decision quality parameter Q, suitable for mulched croplands. Although this new index DIRT needs further testing, the index seems to be a good decision criterion for the weed detection on mulched areas and should also be useful for other image processing applications in precision agriculture. The weed detection hardware and the PC program for the weed image processing were developed with funds from the German Federal Ministry of Education and Research (BMBF).     

基于Landsat 7的武汉市热岛效应研究

城市化使得城市数量和规模不断增加,城市作为人类对自然环境改造最为剧烈的部分,具有独特的区域环境,其中城市内部温度显著高于周边郊区的热岛效应是一个突出的城市环境问题,受到了普遍关注和研究。武汉市是长江中游特大城市,城市热岛效应明显,但对其研究很少。利用Landsat TM6波段数据,生成武汉市城市亮温场,分析了武汉市热场的分布、大小,利用亮温场均值、方差和相对温度评价城市热岛强度,并对城市热场的范围及强度变化与城市发展规模及方向进行了分析,取得了较好的效果。     

Assessing the effects of dam building on land degradation in central Iran with Landsat LST and LULC time series

This study aimed to analyze the impact of Zayandehrood Dam on desertification using the spatio-temporal dynamics of land use/land cover (LULC) and land surface temperature (LST) in an arid environment in central Iran from 1987 to 2014. The LULC and LST images were calculated from Landsat TM, ETM+, and OLI data, and their accuracies were assessed against reference data using error matrix and linear regression analysis. Results showed that salty and bare lands increased up to 57,302 ha, while agricultural lands declined substantially (28,275.58 ha) in the region. The changes in LULC classes resulted in dramatic variations in LST values. The average temperature showed a 5.03 °C increase, and the minimum temperature increased by 5.66 °C. LST had an increasing trend in bare lands (8.74 °C), poor rangelands (6.8 °C), agricultural lands (9.46 °C), salty lands (9.6 °C), and residential areas (3.18 °C) in this 27-year period. Rainfall and temperature trend analysis revealed that the main cause of these extreme changes in LULC and LST was largely attributed to the drying up of Zayandehrood River due to dam construction and allocating water mainly for industrial sectors. Results indicate that in addition to LULC changes, the spatio-temporal variations of LST can be used as an effective index in desertification assessment and monitoring in arid environments.