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The changing climate has introduced new and unique challenges and threats to humans and their environment. Urban dwellers in particular have suffered from increased levels of heat stress, and the situation is predicted to continue to worsen in the future. Attention toward urban climate change adaptation has increased more than ever before, but previous studies have focused on indoor and outdoor temperature patterns separately. The objective of this research is to assess the indoor and outdoor temperature patterns of different urban settlements. Remote sensing data, together with air temperature data collected with temperature data loggers, were used to analyze land surface temperature (outdoor temperature) and air temperature (indoor temperature). A hot and cold spot analysis was performed to identify the statistically significant clusters of high and low temperature data. The results showed a distinct temperature pattern across different residential units. Districts with dense urban settlements show a warmer outdoor temperature than do more sparsely developed districts. Dense urban settlements show cooler indoor temperatures during the day and night, while newly built districts show cooler outdoor temperatures during the warm season. Understanding indoor and outdoor temperature patterns simultaneously could help to better identify districts that are vulnerable to heat stress in each city. Recognizing vulnerable districts could minimize the impact of heat stress on inhabitants.
Saddrodin Alavipanah; Dagmar Haase; Mohsen Makki; Mir Nizamani; Salman Qureshi. On the Spatial Patterns of Urban Thermal Conditions Using Indoor and Outdoor Temperatures. Remote Sensing 2021, 13, 640 .
AMA StyleSaddrodin Alavipanah, Dagmar Haase, Mohsen Makki, Mir Nizamani, Salman Qureshi. On the Spatial Patterns of Urban Thermal Conditions Using Indoor and Outdoor Temperatures. Remote Sensing. 2021; 13 (4):640.
Chicago/Turabian StyleSaddrodin Alavipanah; Dagmar Haase; Mohsen Makki; Mir Nizamani; Salman Qureshi. 2021. "On the Spatial Patterns of Urban Thermal Conditions Using Indoor and Outdoor Temperatures." Remote Sensing 13, no. 4: 640.
Mining activities and associated actions cause land-use/land-cover (LULC) changes across the world. The objective of this study were to evaluate the historical impacts of mining activities on surface biophysical characteristics, and for the first time, to predict the future changes in pattern of vegetation cover and land surface temperature (LST). In terms of the utilized data, satellite images of Landsat, and meteorological data of Sungun mine in Iran, Athabasca oil sands in Canada, Singrauli coalfield in India and Hambach mine in Germany, were used over the period of 1989–2019. In the first step, the spectral bands of Landsat images were employed to extract historical LULC changes in the study areas based on the homogeneity distance classification algorithm (HDCA). Thereafter, a CA-Markov model was used to predict the future of LULC changes based on the historical changes. In addition, LST and vegetation cover maps were calculated using the single channel algorithm, and the normalized difference vegetation index (NDVI), respectively. In the second step, the trends of LST and NDVI variations in different LULC change types and over different time periods were investigated. Finally, a CA-Markov model was used to predict the LST and NDVI maps and the trend of their variations in future. The results indicated that the forest and green space cover was reduced from 9.95 in 1989 to 5.9 Km2 in 2019 for Sungun mine, from 42.14 in 1999 to 33.09 Km2 in 2019 for Athabasca oil sands, from 231.46 in 1996 to 263.95 Km2 in 2016 for Singrauli coalfield, and from 180.38 in 1989 to 133.99 Km2 in 2017 for Hambach mine, as a result of expansion and development of of mineral activities. Our findings about Sungun revealed that the areal coverage of forest and green space will decrease to 15% of the total study area by 2039, resulting in reduction of the mean NDVI by almost 0.06 and increase of mean standardized LST from 0.52 in 2019 to 0.61 in 2039. our results further indicate that for Athabasca oil sands (Singrauli coalfield, Hambach mine), the mean values of standardized LST and NDVI will change from 0.5 (0.44 and 0.4) and 0.38 (0.38, 0.35) in 2019 (2016, 2017) to 0.57 (0.5, 0.47) and 0.33 (0.32, 0.28), in 2039 (2036, 2035), respectively. This can be mainly attributed to the increasing mining activities in the past as well as future years. The discussion and conclusions presented in this study can be of interest to local planners, policy makers, and environmentalists in order to observe the damages brought to the environment and the society in a larger picture.
Mohammad Karimi Firozjaei; Amir Sedighi; Hamzeh Karimi Firozjaei; Majid Kiavarz; Mehdi Homaee; Jamal Jokar Arsanjani; Mohsen Makki; Babak Naimi; Seyed Kazem Alavipanah. A historical and future impact assessment of mining activities on surface biophysical characteristics change: A remote sensing-based approach. Ecological Indicators 2020, 122, 107264 .
AMA StyleMohammad Karimi Firozjaei, Amir Sedighi, Hamzeh Karimi Firozjaei, Majid Kiavarz, Mehdi Homaee, Jamal Jokar Arsanjani, Mohsen Makki, Babak Naimi, Seyed Kazem Alavipanah. A historical and future impact assessment of mining activities on surface biophysical characteristics change: A remote sensing-based approach. Ecological Indicators. 2020; 122 ():107264.
Chicago/Turabian StyleMohammad Karimi Firozjaei; Amir Sedighi; Hamzeh Karimi Firozjaei; Majid Kiavarz; Mehdi Homaee; Jamal Jokar Arsanjani; Mohsen Makki; Babak Naimi; Seyed Kazem Alavipanah. 2020. "A historical and future impact assessment of mining activities on surface biophysical characteristics change: A remote sensing-based approach." Ecological Indicators 122, no. : 107264.
Purpose In urban areas, humans shape the surface, (re-)deposit natural or technogenic material, and thus become the dominant soil formation factor. The 2015 edition of the World Reference Base for Soil Resources (WRB) describes anthropogenic urban soils as Anthrosols or Technosols, but the methodological approaches and classification criteria of national soil classification systems are rather inconsistent. Stringent criteria for describing and mapping anthropogenic soils in urban areas and their application are still lacking, although more than half (53%) of the urban soils in Berlin are built-up by or contain anthropogenic material. Materials and methods On behalf of the Berlin Senate Department for the Environment, Transport and Climate Protection and in close cooperation with the German Working Group for Urban Soils, a comprehensive guideline for soil description in the Berlin metropolitan area (BMA), with special regard to anthropogenic/technogenic parent material and anthropogenic soils, has been developed. Our approach includes all previous standard works for soil description and mapping and is based on studies that have been conducted in the BMA over the last five decades. Special emphasis was placed on the integration of our manual into the classification system of the German soil mapping guideline (KA5). Results and discussion The extension of existing data fields (e.g., the further subdivision of land use types) as well as the creation of new data fields (e.g., pH value) adapted to the requirements of urban soil mapping has been carried out. Additional technogenic materials that occur in urban environments have been added to the list of anthropogenic parent materials. Furthermore, we designed appendices that clearly characterize typical soil profiles of the BMA and depict technogenic materials, their physical and chemical characteristics, as well as their origin and distribution. Our approach will set new benchmarks for soil description and mapping in urban environments, which will improve the quality of urban soil research in the BMA. It is expected that our approach will provide baselines for urban soil mapping in other metropolitan areas. Conclusions Our guideline is a comprehensive manual for the description of urban soils within a national soil classification system. This mapping guideline will be the future standard work for soil surveys and soil mapping in the federal state of Berlin. Currently, representatives from federal and state authorities are reviewing our guideline, with a view to potentially integrating key components into the classification system of the forthcoming 6th edition of the German soil mapping guideline (KA6).
Mohsen Makki; Kolja Thestorf; Sabine Hilbert; Michael Thelemann; Lutz Makowsky. Guideline for the description of soils in the Berlin metropolitan area: an extension for surveying and mapping anthropogenic and natural soils in urban environments within the German soil classification system. Journal of Soils and Sediments 2020, 21, 1998 -2012.
AMA StyleMohsen Makki, Kolja Thestorf, Sabine Hilbert, Michael Thelemann, Lutz Makowsky. Guideline for the description of soils in the Berlin metropolitan area: an extension for surveying and mapping anthropogenic and natural soils in urban environments within the German soil classification system. Journal of Soils and Sediments. 2020; 21 (5):1998-2012.
Chicago/Turabian StyleMohsen Makki; Kolja Thestorf; Sabine Hilbert; Michael Thelemann; Lutz Makowsky. 2020. "Guideline for the description of soils in the Berlin metropolitan area: an extension for surveying and mapping anthropogenic and natural soils in urban environments within the German soil classification system." Journal of Soils and Sediments 21, no. 5: 1998-2012.
Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.
M. K. Firozjaei; M. Makki; J. Lentschke; M. Kiavarz; S. K. Alavipanah. SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-4/W18, 401 -405.
AMA StyleM. K. Firozjaei, M. Makki, J. Lentschke, M. Kiavarz, S. K. Alavipanah. SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-4/W18 ():401-405.
Chicago/Turabian StyleM. K. Firozjaei; M. Makki; J. Lentschke; M. Kiavarz; S. K. Alavipanah. 2019. "SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18, no. : 401-405.
In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.
Angela Lausch; Jussi Baade; Lutz Bannehr; Erik Borg; Jan Bumberger; Sabine Chabrilliat; Peter Dietrich; Heike Gerighausen; Cornelia Glässer; Jorg M. Hacker; Dagmar Haase; Thomas Jagdhuber; Sven Jany; András Jung; Arnon Karnieli; Roland Kraemer; Mohsen Makki; Christian Mielke; Markus Möller; Hannes Mollenhauer; Carsten Montzka; Marion Pause; Christian Rogass; Offer Rozenstein; Christiane Schmullius; Franziska Schrodt; Martin Schrön; Karsten Schulz; Claudia Schütze; Christian Schweitzer; Peter Selsam; Andrew K. Skidmore; Daniel Spengler; Christian Thiel; Sina C. Truckenbrodt; Michael Vohland; Robert Wagner; Ute Weber; Ulrike Werban; Ute Wollschläger; Steffen Zacharias; Michael E. Schaepman. Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics. Remote Sensing 2019, 11, 2356 .
AMA StyleAngela Lausch, Jussi Baade, Lutz Bannehr, Erik Borg, Jan Bumberger, Sabine Chabrilliat, Peter Dietrich, Heike Gerighausen, Cornelia Glässer, Jorg M. Hacker, Dagmar Haase, Thomas Jagdhuber, Sven Jany, András Jung, Arnon Karnieli, Roland Kraemer, Mohsen Makki, Christian Mielke, Markus Möller, Hannes Mollenhauer, Carsten Montzka, Marion Pause, Christian Rogass, Offer Rozenstein, Christiane Schmullius, Franziska Schrodt, Martin Schrön, Karsten Schulz, Claudia Schütze, Christian Schweitzer, Peter Selsam, Andrew K. Skidmore, Daniel Spengler, Christian Thiel, Sina C. Truckenbrodt, Michael Vohland, Robert Wagner, Ute Weber, Ulrike Werban, Ute Wollschläger, Steffen Zacharias, Michael E. Schaepman. Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics. Remote Sensing. 2019; 11 (20):2356.
Chicago/Turabian StyleAngela Lausch; Jussi Baade; Lutz Bannehr; Erik Borg; Jan Bumberger; Sabine Chabrilliat; Peter Dietrich; Heike Gerighausen; Cornelia Glässer; Jorg M. Hacker; Dagmar Haase; Thomas Jagdhuber; Sven Jany; András Jung; Arnon Karnieli; Roland Kraemer; Mohsen Makki; Christian Mielke; Markus Möller; Hannes Mollenhauer; Carsten Montzka; Marion Pause; Christian Rogass; Offer Rozenstein; Christiane Schmullius; Franziska Schrodt; Martin Schrön; Karsten Schulz; Claudia Schütze; Christian Schweitzer; Peter Selsam; Andrew K. Skidmore; Daniel Spengler; Christian Thiel; Sina C. Truckenbrodt; Michael Vohland; Robert Wagner; Ute Weber; Ulrike Werban; Ute Wollschläger; Steffen Zacharias; Michael E. Schaepman. 2019. "Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics." Remote Sensing 11, no. 20: 2356.