Urban energy exchanges monitoring from space

One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. However, satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (-64.1, +69.3 W m-2 for ±2 K perturbation); and also underestimate anthropogenic heat flux. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling

Chapter Earth Observation for Urban Climate Monitoring: Surface Cover and Land Surface Temperature

The rate at which global climate change is happening is arguably the most pressing environmental challenge of the century, and it affects our cities. Climate change exerts added stress on urban areas through increased numbers of heat waves threatening people’s well-being and, in many cases, human lives. Earth observation (EO) systems and the advances in remote sensing technology increase the opportunities for monitoring the thermal behavior of cities. The Sentinels constitute the first series of operational satellites for Copernicus, a program launched to provide data, information, services, and knowledge in support of Europe’s goals regarding sustainable development and global governance of the environment. This chapter examines the exploitation of EO data for monitoring the urban climate, with particular focus on the urban surface cover and temperature. Two example applications are analyzed: the mapping of the urban surface and its characteristics, using EO data and the estimation of urban temperatures. Approaches, like the ones described in this chapter, can become operational once adapted to Sentinels, since their long-term operation plan guarantees the future supply of satellite observations. Thus, the described methods may support planning activities related to climate change mitigation and adaptation in cities, as well as routine urban planning activities

Multiple Endmember Spectral Mixture Analysis (MESMA) Applied to the Study of Habitat Diversity in the Fine-Grained Landscapes of the Cantabrian Mountains

P. 1-19 ArtículoHeterogeneous and patchy landscapes where vegetation and abiotic factors vary at small spatial scale (fine-grained landscapes) represent a challenge for habitat diversity mapping using remote sensing imagery. In this context, techniques of spectral mixture analysis may have an advantage over traditional methods of land cover classification because they allow to decompose the spectral signature of a mixed pixel into several endmembers and their respective abundances. In this work, we present the application of Multiple Endmember Spectral Mixture Analysis (MESMA) to quantify habitat diversity and assess the compositional turnover at different spatial scales in the fine-grained landscapes of the Cantabrian Mountains (northwestern Iberian Peninsula). A Landsat-8 OLI scene and high-resolution orthophotographs (25 cm) were used to build a region-specific spectral library of the main types of habitats in this region (arboreal vegetation; shrubby vegetation; herbaceous vegetation; rocks–soil and water bodies). We optimized the spectral library with the Iterative Endmember Selection (IES) method and we applied MESMA to unmix the Landsat scene into five fraction images representing the five defined habitats (root mean square error, RMSE 0.025 in 99.45% of the pixels). The fraction images were validated by linear regressions using 250 reference plots from the orthophotographs and then used to calculate habitat diversity at the pixel ( -diversity: 30 30 m), landscape (-diversity: 1 1 km) and regional ("-diversity: 110 33 km) scales and thecompositional turnover ( - and -diversity) according to Simpson’s diversity index. Richness and evenness were also computed. Results showed that fraction images were highly related to reference data (R2 0.73 and RMSE 0.18). In general, our findings indicated that habitat diversity was highly dependent on the spatial scale, with values for the Simpson index ranging from 0.20 0.22 for -diversity to 0.60 0.09 for -diversity and 0.72 0.11 for "-diversity. Accordingly, we found -diversity to be higher than -diversity. This work contributes to advance in the estimation of ecological diversity in complex landscapes, showing the potential of MESMA to quantify habitat diversity in a comprehensive way using Landsat imageryS

Mapping Impervious Surface Using Phenology-Integrated and Fisher Transformed Linear Spectral Mixture Analysis

The impervious surface area (ISA) is a key indicator of urbanization, which brings out serious adverse environmental and ecological consequences. The ISA is often estimated from remotely sensed data via spectral mixture analysis (SMA). However, accurate extraction of ISA using SMA is compromised by two major factors, endmember spectral variability and plant phenology. This study developed a novel approach that incorporates phenology with Fisher transformation into a conventional linear spectral mixture analysis (PF-LSMA) to address these challenges. Four endmembers, high albedo, low albedo, evergreen vegetation, and seasonally exposed soil (H-L-EV-SS) were identified for PF-LSMA, considering the phenological characteristic of Shanghai. Our study demonstrated that the PF-LSMA effectively reduced the within-endmember spectral signature variation and accounted for the endmember phenology effects, and thus well-discriminated impervious surface from seasonally exposed soil, enhancing the accuracy of ISA extraction. The ISA fraction map produced by PF-LSMA (RMSE = 0.1112) outperforms the single-date image Fisher transformed unmixing method (F-LSMA) (RMSE = 0.1327) and the other existing major global ISA products. The PF-LSMA was implemented on the Google Earth Engine platform and thus can be easily adapted to extract ISA in other places with similar climate conditions.Peer Reviewe

Mapping Impervious Surface Using Phenology-Integrated and Fisher Transformed Linear Spectral Mixture Analysis

The impervious surface area (ISA) is a key indicator of urbanization, which brings out serious adverse environmental and ecological consequences. The ISA is often estimated from remotely sensed data via spectral mixture analysis (SMA). However, accurate extraction of ISA using SMA is compromised by two major factors, endmember spectral variability and plant phenology. This study developed a novel approach that incorporates phenology with Fisher transformation into a conventional linear spectral mixture analysis (PF-LSMA) to address these challenges. Four endmembers, high albedo, low albedo, evergreen vegetation, and seasonally exposed soil (H-L-EV-SS) were identified for PF-LSMA, considering the phenological characteristic of Shanghai. Our study demonstrated that the PF-LSMA effectively reduced the within-endmember spectral signature variation and accounted for the endmember phenology effects, and thus well-discriminated impervious surface from seasonally exposed soil, enhancing the accuracy of ISA extraction. The ISA fraction map produced by PF-LSMA (RMSE = 0.1112) outperforms the single-date image Fisher transformed unmixing method (F-LSMA) (RMSE = 0.1327) and the other existing major global ISA products. The PF-LSMA was implemented on the Google Earth Engine platform and thus can be easily adapted to extract ISA in other places with similar climate conditions

Regression-based surface water fraction mapping using a synthetic spectral library for monitoring small water bodies

Small water bodies (SWBs), such as ponds and on-farm reservoirs, are a key part of the hydrological system and play important roles in diverse domains from agriculture to conservation. The monitoring of SWBs has been greatly facilitated by medium-spatial-resolution satellite images, but the monitoring accuracy is considerably affected by the mixed-pixel problem. Although various spectral unmixing methods have been applied to map sub-pixel surface water fractions for large water bodies, such as lakes and reservoirs, it is challenging to map SWBs that are small in size relative to the image pixel and have dissimilar spectral properties. In this study, a novel regression-based surface water fraction mapping method (RSWFM) using a random forest and a synthetic spectral library is proposed for mapping 10 m spatial resolution surface water fractions from Sentinel-2 imagery. The RSWFM inputs a few endmembers of water, vegetation, impervious surfaces, and soil to simulate a spectral library, and considers spectral variations in endmembers for different SWBs. Additionally, RSWFM applies noise-based data augmentation on pure endmembers to overcome the limitation often arising from the use of a small set of pure spectra in training the regression model. RSWFM was assessed in ten study sites and compared with the fully constrained least squares (FCLS) linear spectral mixture analysis, multiple endmember spectral mixture analysis (MESMA), and the nonlinear random forest (RF) regression without data-augmentation. The results showed that RSWFM decreases the water fraction mapping errors by ~ 30%, ~15%, and ~ 11% in root mean square error compared with the linear FCLS, MESMA unmixings, and the nonlinear RF regression without data-augmentation respectively. RSWFM has an accuracy of approximately 0.85 in R2 in estimating the area of SWBs smaller than 1 ha

Unmixing-based Spatiotemporal Image Fusion Based on the Self-trained Random Forest Regression and Residual Compensation

Spatiotemporal satellite image fusion (STIF) has been widely applied in land surface monitoring to generate high spatial and high temporal reflectance images from satellite sensors. This paper proposed a new unmixing-based spatiotemporal fusion method that is composed of a self-trained random forest machine learning regression (R), low resolution (LR) endmember estimation (E), high resolution (HR) surface reflectance image reconstruction (R), and residual compensation (C), that is, RERC. RERC uses a self-trained random forest to train and predict the relationship between spectra and the corresponding class fractions. This process is flexible without any ancillary training dataset, and does not possess the limitations of linear spectral unmixing, which requires the number of endmembers to be no more than the number of spectral bands. The running time of the random forest regression is about ~1% of the running time of the linear mixture model. In addition, RERC adopts a spectral reflectance residual compensation approach to refine the fused image to make full use of the information from the LR image. RERC was assessed in the fusion of a prediction time MODIS with a Landsat image using two benchmark datasets, and was assessed in fusing images with different numbers of spectral bands by fusing a known time Landsat image (seven bands used) with a known time very-high-resolution PlanetScope image (four spectral bands). RERC was assessed in the fusion of MODIS-Landsat imagery in large areas at the national scale for the Republic of Ireland and France. The code is available at https://www.researchgate.net/proiile/Xiao_Li52

Assessing the Impact of Gold Mining on Forest Cover in the Surinamese Amazon Rainforest from 1997 - 2019: A Semi-Automated Satellite-Based Approach

The Amazon rainforest, as a biodiversity hotspot and regulator of the earths climate, is one of the most important ecosystems on earth, but has been facing extensive deforestation for decades due to urban growth, agricultural expansion, logging and mining. Mining (and the use of remote sensing methods to detect it) has been relatively understudied in the Amazon compared to the other drivers up until a decade ago, highlighting the importance of current research. The objectives of this study are: To quantify the increase in industrial and artisanal mining and its impact on forest cover in the northern Amazonian country of Suriname between 1997 and 2019; Evaluate the impact of this expansion on the structure (fragmentation) and health (phenology) of the forest; and improve existing remote sensing techniques for mining detection through the development of a pioneer method based on cloud processing and semi-automated mining reclassification. The cloud processing software known as Google Earth Engine (GEE) was used for the initial land use land cover classification of the study area. Landsat 5 and 8 images and the classification and regression trees (C.A.R.T) algorithm were used in this step. The resulting classified maps were fed into the semi-automated re-classification model developed for this study, producing final re-classified output maps, which were used to analyse the expansion of mining and its associated impacts on forest fragmentation and phenology. The proposed method is the first documented method which combines cloud processing with a semi-automated re-classification model, providing a technologically advanced approach capable of rapid and efficient detection of mines. This approach resulted in an 89.5% accuracy of mining detection, and the combination of speed, efficiency, and highly accurate detection outperformed many of the other currently documented methods for mining detection in the Amazon. The results highlighted that mining increased from 69.4km² in 1997 to 431.6km² in 2019, an increase of 522% over 22 years. This growth led directly to 351.9km² of forest loss, 83% of which was due to artisanal mining. This loss of forest led to a 122.8km² reduction in the effective mesh size for the artisanal mine sub-area, compared to a decrease of 83km² for the Industrial mine sub-area. Mining also caused a decrease in the health of the surrounding forest, with the decrease in peak greenness being more pronounced for artisanal mining compared to industrial mining. Recommendations for future research include exploring the use of higher resolution imagery such as Sentinel for better results, as well as the use of microwave data in the classification to combat the issue of extensive cloud cover in the Amazon. The issue of overclassification present in the proposed method can potentially be combated by exploring combinations of different classification algorithms with the reclassification model

Nonlinear Spectral Unmixing of Landsat Imagery for Urban Surface Cover Mapping

The high spatial diversity of man-made structures, the spectral variability of urban materials, and the three-dimensional structure of the cities make the mapping of urban surfaces using Earth Observation data, one of the most challenging tasks in remote sensing field. Spectral unmixing techniques can be proven useful with medium spectral resolution data to assess urban surface cover information on a subpixel level. Due to the large spectral variability of urban materials and the multiple scattering of light between surfaces in urban areas, multiple endmembers should be used, and the nonlinearity of spectral mixture should be accounted for. In this study, these issues are addressed using an artificial neural network trained with endmember and nonlinearly mixed synthetic spectra to inverse the pixel spectral mixture in Landsat imagery. A spectral library is built, consisting of endmember spectra collected from the image and synthetic spectra, produced using a nonlinear model specifically developed for urban areas. The method was tested over a case study, and the validation against higher resolution products revealed an accuracy of around 90% for all abundance maps. The comparison performed between the linear and nonlinear implementation of the method proved the need for including the nonlinear term, especially for improving the built-up abundance map. The proposed method is easily transferable to any city and fast in terms of computations, which makes it ideal for the implementation of operational urban services