Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing, which ignore the sub-pixel spectral variation resulted from spectral mixture. Especially for medium-resolution remote sensing images used in urban land-cover change monitoring, land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution. Thus, traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably, degrading the overall accuracy of change detection. In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level, a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion. Nonlinear spectral mixture model is selected for spectral unmixing, and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple compositi..

Study of the urban heat island (UHI) using remote sensing data/techniques: a systematic review.

Urban Heat Islands (UHI) consist of the occurrence of higher temperatures in urbanized areas when compared to rural areas. During the warmer seasons, this effect can lead to thermal discomfort, higher energy consumption, and aggravated pollution effects. The application of Remote Sensing (RS) data/techniques using thermal sensors onboard satellites, drones, or aircraft, allow for the estimation of Land Surface Temperature (LST). This article presents a systematic review of publications in Scopus andWeb of Science (WOS) on UHI analysis using RS data/techniques and LST, from 2000 to 2020. The selection of articles considered keywords, title, abstract, and when deemed necessary, the full text. The process was conducted by two independent researchers and 579 articles, published in English, were selected. Qualitative and quantitative analyses were performed. Cfa climate areas are the most represented, as the Northern Hemisphere concentrates the most studied areas, especially in Asia (69.94%); Landsat products were the most applied to estimates LST (68.39%) and LULC (55.96%); ArcGIS (30.74%) was most used software for data treatment, and correlation (38.69%) was the most applied statistic technique. There is an increasing number of publications, especially from 2016, and the transversality of UHI studies corroborates the relevance of this topic.This work was funded by National Funds through the FCT-Foundation for Science and Technology and FEDER, under the projects UIDB/04683/2020 and PT2020 Program for financial support to CIMO UIDB/00690/2020. This work was funded by National Funds through the FCT-Foundation for Science and Technology and FEDER, under the projects UIDB/04683/2020 and PT2020 Program for financial support to CIMO UIDB/00690/2020.info:eu-repo/semantics/publishedVersio

CEOS Land Surface Imaging Constellation Mid-Resolution Optical Guidelines

The LSI community of users is large and varied. To reach all these users as well as potential instrument contributors this document has been organized by measurement parameters of interest such as Leaf Area Index and Land Surface Temperature. These measurement parameters and the data presented in this document are drawn from multiple sources, listed at the end of the document, although the two primary ones are "The Space-Based Global Observing System in 2010 (GOS-2010)" that was compiled for the World Meteorological Organization (WMO) by Bizzarro Bizzarri, and the CEOS Missions, Instruments, and Measurements online database (CEOS MIM). For each measurement parameter the following topics will be discussed: (1) measurement description, (2) applications, (3) measurement spectral bands, and (4) example instruments and mission information. The description of each measurement parameter starts with a definition and includes a graphic displaying the relationships to four general land surface imaging user communities: vegetation, water, earth, and geo-hazards, since the LSI community of users is large and varied. The vegetation community uses LSI data to assess factors related to topics such as agriculture, forest management, crop type, chlorophyll, vegetation land cover, and leaf or canopy differences. The water community analyzes snow and lake cover, water properties such as clarity, and body of water delineation. The earth community focuses on minerals, soils, and sediments. The geo-hazards community is designed to address and aid in emergencies such as volcanic eruptions, forest fires, and large-scale damaging weather-related events

The Spatial and Temporal Characteristics of the Urban Thermal Environment in East Africa: Implications for Sustainable Urban Development

Targeting cities in East Africa, where urbanisation and climate change are posing unprecedented threats to livelihoods and ecosystems, this thesis focuses on the combined effects of rapid urbanisation and climate change on Land Surface Temperature (LST), Surface Urban Heat Island (SUHI) effects and the role of Blue Green infrastructure (BGI) and vegetation dynamics. The aim of this thesis is to advance understanding of the urban thermal environment and the role of factors such as climate, vegetation and urbanisation patterns that add to its complexity. Through the use of satellite and remote sensing data (e.g., Google Earth Engine), spatial and statistical analyses, conducted in ArcGIS, Geoda and R, this thesis provides analyses of temporal trends between 2003 and 2017, and spatial differences in LST and SUHI in five East African cities (Khartoum, Addis Ababa, Kampala, Nairobi, Dar es Salaam). It advances understanding of how the configuration of urban areas affects the urban thermal environment, the amount of vegetation and surface water, and demonstrates the influence of urban density on the changes in SUHI intensity in both space and time. By linking the findings from the three results chapters and placing this in the context of the broader literature, corresponding policy implications and solutions are presented. The urgent need to provide a more detailed understanding of urban thermal environments, including macroclimate differences, seasonal variation and urban morphological characteristics, is highlighted. Recommendations emphasise the use of cloud-based analysis methods to overcome data scarcity, while the results point towards the utility of nature-based solutions for urban sustainable development. The methods and lessons emerging from this study can also be applied in other rapidly urbanising cities, where climate change is posing an unprecedented threat to livelihoods and ecosystems, and where resources are limited

Investigating the Use of Remote Sensing and GIS Techniques to Detect Land Use and Land Cover Change: A Review

The accuracy of change detection on the earth's surface is important for understanding the relationships and interactions between human and natural phenomena. Remote Sensing and Geographic Information Systems (GIS) have the potential to provide accurate information regarding land use and land cover changes. In this paper, we investigate the major techniques that are utilized to detect land use and land cover changes. Eleven change detection techniques are reviewed. An analysis of the related literature shows that the most used techniques are post-classification comparison and principle component analysis. Post-classification comparison can minimize the impacts of atmospheric and sensor differences between two dates. Image differencing and image ratioing are easy to implement, but at times they do not provide accurate results. Hybrid change detection is a useful technique that makes full use of the benefits of many techniques, but it is complex and depends on the characteristics of the other techniques such as supervised and unsupervised classifications. Change vector analysis is complicated to implement, but it is useful for providing the direction and magnitude of change. Recently, artificial neural networks, chi-square, decision tree and image fusion have been frequently used in change detection. Research on integrating remote sensing data and GIS into change detection has also increased

The use of satellite data, meteorology and land use data to define high resolution temperature exposure for the estimation of health effects in Italy

Introduction. Despite the mounting evidence on heat-related health risks, there is limited evidence in suburban and rural areas. The limited spatial resolution of temperature data also hinders the evidence of the differential heat effect within cities due to individual and area-based characteristics. Methods. Satellite land surface temperature (LST), observed meteorological and spatial and spatio-temporal land use data were combined in mixed-effects regression models to estimate daily mean air temperature with a 1x1km resolution for the period 2000-2010. For each day, random intercepts and slopes for LST were estimated to capture the day-to-day temporal variability of the Ta–LST relationship. The models were also nested by climate zones to better capture local climates and daily weather patterns across Italy. The daily exposure data was used to estimate the effects and impacts of heat on cause-specific mortality and hospital admissions in the Lazio region at municipal level in a time series framework. Furthermore, to address the differential effect of heat within an urban area and account for potential effect modifiers a case cross-over study was conducted in Rome. Mean temperature was attributed at the individual level to the Rome Population Cohort and the urban heat island (UHI) intensity using air temperature data was calculated for Rome. Results. Exposure model performance was very good: in the stage 1 model (only on grid cells with both LST and observed data) a mean R2 value of 0.96 and RMSPE of 1.1°C and R2 of 0.89 and 0.97 for the spatial and temporal domains respectively. The model was also validated with regional weather forecasting model data and gave excellent results (R2=0.95 RMSPE=1.8°C. The time series study showed significant effects and impacts on cause-specific mortality in suburban and rural areas of the Lazio region, with risk estimates comparable to those found in urban areas. High temperatures also had an effect on respiratory hospital admissions. Age, gender, pre-existing cardiovascular disease, marital status, education and occupation were found to be effect modifiers of the temperature-mortality association. No risk gradient was found by socio-economic position (SEP) in Rome. Considering the urban heat island (UHI) and SEP combined, differential effects of heat were observed by UHI among same SEP groupings. Impervious surfaces and high urban development were also effect modifiers of the heat-related mortality risk. Finally, the study found that high resolution gridded data provided more accurate effect estimates especially for extreme temperature intervals. Conclusions. Results will help improve heat adaptation and response measures and can be used predict the future heat-related burden under different climate change scenarios.Open Acces

The Changing Matrix: Reforestation and Connectivity in a Tropical Habitat Corridor

In the last two decades, export-oriented crops and timber and fruit plantations have joined small-scale cultivation and pasture as important causes of tropical deforestation. Widespread conversion of tropical forest to agriculture threatens to isolate protected areas, which has led to efforts to maintain functional connectivity in landscapes between protected areas. Relatively few "landscape conservation" efforts have been assessed for their effect on deforestation, but advances in remote sensing now permit detailed monitoring of tropical land uses over time, including mapping of tree crops and plantations. This dissertation evaluates the long-term impact of forest conservation and reforestation policies on tropical forests in a habitat corridor. The following chapters test the capability of remote sensing to monitor tropical conservation efforts and assess whether landscape conservation policies can maintain forest cover and connectivity in the face of rapid agricultural expansion. Costa Rica has one of the most comprehensive landscape conservation policies in the tropics: a 1996 Forest Law banned deforestation and expanded payments for environmental services (PES) to protect forests and plant trees, prioritizing designated habitat corridors between protected areas. The long-term effect of the program on land-use transitions is not well known. To take advantage of this regional policy experiment, I used a time-series of five moderate-resolution Landsat images to track land-use change from 1986 to 2011in the oldest habitat corridor, the San Juan-La Selva Biological Corridor (SJLSBC). Forest conservation policies were associated with a 40% decline in deforestation after 1996 despite a doubling in the area of cropland in the last decade. The proportion of cropland derived from mature forest dropped from 16.4% to 1.9% after 1996, while one fifth of pasture expansion continued to be derived from mature forest. These results suggest that forest conservation policies can successfully lower deforestation, and that they can be more effective with large export producers than small-scale cattle producers. Tree plantations are an important component of Costa Rican PES, but knowledge of their distribution and contribution to connectivity in the corridor region is poor. After reviewing the remote sensing literature, I employed a novel integration of hyperspectral images and a Landsat time-series to create the first regional map of tropical tree plantation species. Including multitemporal data significantly improved overall hyperspectral map accuracy to 91%; the six tree plantation species were classified with 83% mean producer's accuracy. Non-native species made up 89% of tree plantations, and they were cleared more rapidly than native tree plantations and secondary forests. I combined existing land cover maps, field behavioral experiments, and a graph connectivity model to estimate whether landscape conservation policies increased connectivity for understory insectivorous birds, a representative forest-dependent group. The field playback experiments indicated both native and exotic tree plantations with a dense shrubby understory were acceptable dispersal habitat for all species, and that birds traveled readily near secondary forest edges but rarely into forested pasture. Graph model parameters were informed by these results. For all of these bird species, functional connectivity declined by 14-21% with only a 4.9% decline in forest area over time, implying that conservation policies have not caused a net increase in functional connectivity in the SJLSBC region. Despite making up 2% of the region, tree plantations had little effect on regional connectivity because of their placement in the landscape; we demonstrate that spatially-targeted reforestation of 0.1% of the region could increase connectivity by 1.8%. Collectively, the results presented in these chapters underline the potential and limitations of landscape conservation policies and corridor plans in the tropics; combining regulations and PES can lower deforestation over the medium-term, but increased enforcement, improved monitoring with remote sensing, and targeted conservation effort is needed to combat illegal deforestation and restore functional connectivity. Given numerous new tropical corridor and PES programs and the qualified successes of landscape conservation policies in Costa Rica and other tropical countries, our approach to the analysis can be applied to monitor and evaluate connectivity across the tropics

Geospatial Analysis of Horizontal and Vertical Urban Expansion Using Multi-Spatial Resolution Data: A Case Study of Surabaya, Indonesia

Urbanization addresses urban expansion, and it leads conversion of the green space into the built-up area. However, previous studies mainly focused on two-dimensional (2D) urban expansion rather than three-dimensional (3D) growth. Here, the purpose of this study is to examine the urban expansion, including built-up and green space for both horizontal and vertical dimensions using geospatial analysis including remote sensing (RS) and Geographic Information System (GIS) in the sub-Central Business District (CBD) area of Surabaya, Indonesia. The medium resolution remote sensing data for both image and Digital Surface Model (DSM) acquired by Advanced Land-Observing Satellite (ALOS) were applied for time-1 (2010). The orthophoto and DSM derived by LiDAR were used for time-2 (2016). We quantified the built-up and green expansions in 2D (area), which were extracted from land use/land cover (LU/LC) by applying hybrid classification. The built-up and green expansions in 3D (volume) were estimated by generating a surface feature model. The spatial configuration of area expansion was investigated using patch metric, while the volume growth was examined using the volume expansion rate. We got three findings. (1) The built-up and green area had expanded about 11.54% and 95.61%, respectively, from 2010 to 2016. The expansion of green area presented in a notable portion, which was mainly contributed by the conversion of bareland to playground or park. However, the expansion of built-up area was less than the volume expansion of 20.6%. It revealed that built-up growth led to vertical rather than horizontal development. (2) The built-up area expansion tended to scatter configuration, whereas, the green area expansion tended to aggregate in a linear pattern. (3) The ratio of built-up volume expansion to green volume expansion showed a mean of 3.7, indicating that the development of built-up and green volume was imbalanced. The built-up growth presented higher than the green growth, mainly in the areas with more vertical building establishment. The pressing need for higher green volume in the study area was identified in several sites located at surrounding artery and toll roads. Overall, our approach can be applied as a reference in monitoring neighborhood environment through greening programs for sustainable urban development

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought