Monitoring of Urbanization and Analysis of Environmental Impact in Stockholm with Sentinel-2A and SPOT-5 Multispectral Data

Spatiotemporal changes of ecological environment quality and climate drivers in Zoige Plateau

Ecological environment is the essential material basis of human survival and connects regional economy with socially sustainable development. However, climate changes characterized by global climate warming have caused a series of ecological environmental problems in recent years. Few studies have discussed various climate factors affecting the ecological environment, and the spatial non-stationary effects of different climate factors on the ecological environment are still unclear. Dynamically monitoring ecological environment changes in fragile areas and identifying its climate-driving mechanism are essential for ecological protection and environmental repair. Taking Zoige Plateau as a case, this paper simulated the eco-environmental quality during 1987-2020 using remote sensing data, utilized Geodetector method to identify the contributions of various climate drivers to ecological environment quality, and then adopted the Geographically Weighted Regression model to explore the spatial non-stationary impacts of climate factors on ecological environment quality. The results showed that the ecological quality in the middle regions of the Zoige Plateau was slightly better than in the surrounding marginal areas. For the whole area of Zoige Plateau, the average ecological environment quality index was 54.92, 53.99, 56.17, 57.88, 63.44, 56.93, 59.43, and 59.76 in 1987, 1992, 1997, 2001, 2006, 2013, 2016 and 2020, respectively, which indicated that eco-environmental quality witnessed several fluctuations during the study period but showed a generally increasing trend. Among five climate factors, the temperature was the dominant climate factor affecting the ecological environment quality (q value: 0.11-0.19), sunshine duration (0.03-0.17), wind speed (0.03-0.11), and precipitation (0.03-0.08) were the main climate drivers, while the explanatory power of relative humidity to ecological environment quality was relatively small. Such various climate factors impacting the ecological environment quality demonstrated distinct spatial non-stationary and the range of driving impact varied with time. Temperature, sunshine duration, wind speed, and relative humidity promoted ecological environment quality in most regions (regression coefficients > 0), while precipitation mainly had a negative inhibitory impact (regression coefficients < 0). Meanwhile, the greater impacts of these five climate factors were concentrated in high-elevation regions of the south and west or the northern areas. The appropriate enhancement of climate warming and air humidity was beneficial to the improvement of the ecological environment, but the excessive precipitation would result in landslides and exhibit inhibition of vegetation growth. Therefore, selecting cold-tolerant herbs and shrubs, and strengthening climate monitoring and early warning systems (such as drought and excessive precipitation) are essential for ecological restoration.

Antibiotics, Multidrug-Resistant Bacteria, and Antibiotic Resistance Genes: Indicators of Contamination in Mangroves?

Multidrug-resistant bacteria and antibiotic resistance genes can be monitored as indicators of contamination in several environments. Mangroves are among the most productive ecosystems, and although they can be resilient to the action of climate phenomena, their equilibrium can be affected by anthropogenic activities. Regarding the presence and persistence of multidrug-resistant bacteria in mangroves, it is common to think that this ecosystem can function as a reservoir, which can disperse the antibiotic resistance capacity to human pathogens, or serve as a filter to eliminate drug-resistant genes. The possible impact of anthropogenic activities carried out near mangroves is reviewed, including wastewater treatment, food production systems, leisure, and tourism. Adverse effects of antibiotic resistance genes or multidrug-resistant bacteria, considered as emerging contaminants, have not been reported yet in mangroves. On the contrary, mangrove ecosystems can be a natural way to eliminate antibiotics, antibiotic-resistant bacteria, and even antibiotic-resistant genes from the environment. Although mangroves’ role in decreasing antibiotics and antibiotic resistance genes from the environment is being proposed, the mechanisms by which these plants reduce these emerging contaminants have not been elucidated and need further studies. Additionally, further evaluation is needed on the effects of antibiotics and antibiotic-resistant bacteria in mangroves to generate an analysis of the human contribution to the degradation of this specific ecosystem as well as to define if these contaminants can be used as indicators of contamination in mangrove ecosystems.

Monitoring Urban Green Infrastructure Changes and Impact on Habitat Connectivity Using High-Resolution Satellite Data

In recent decades, the City of Stockholm, Sweden, has grown substantially and is now the largest city in Scandinavia. Recent urban growth is placing pressure on green areas within and around the city. In order to protect biodiversity and ecosystem services, green infrastructure is part of Stockholm municipal planning. This research quantifies land-cover change in the City of Stockholm between 2003 and 2018 and examines what impact urban growth has had on its green infrastructure. Two 2018 WorldView-2 images and three 2003 QuickBird-2 images were used to produce classifications of 11 land-cover types using object-based image analysis and a support vector machine algorithm with spectral, geometric and texture features. The classification accuracies reached over 90% and the results were used in calculations and comparisons to determine the impact of urban growth in Stockholm between 2003 and 2018, including the generation of land-cover change statistics in relation to administrative boundaries and green infrastructure. For components of the green infrastructure, i.e., habitat networks for selected sensitive species, habitat network analysis for the European crested tit (Lophophanes cristatus) and common toad (Bufo bufo) was performed. Between 2003 and 2018, urban areas increased by approximately 4% while green areas decreased by 2% in comparison with their 2003 areal amounts. The most significant urban growth occurred through expansion of the transport network, paved surfaces and construction areas which increased by 12%, mainly at the expense of grassland and coniferous forest. Examination of urban growth within the green infrastructure indicated that most land area was lost in dispersal zones (28 ha) while the highest percent change was within habitat for species of conservation concern (14%). The habitat network analysis revealed that overall connectivity decreased slightly through patch fragmentation and areal loss mainly caused by road expansion on the outskirts of the city. The habitat network analysis also revealed which habitat areas are well-connected and which are most vulnerable. These results can assist policymakers and planners in their efforts to ensure sustainable urban development including sustaining biodiversity in the City of Stockholm.

Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition

Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy. This study aims to analyze the effect of different composition methods, as well as different input images, on the classification results. We use Landsat 8 surface reflectance (L8sr) data with eight different combination strategies to produce and evaluate land cover maps for a study area in Mongolia. We implemented the experiment on the GEE platform with a widely applied algorithm, the Random Forest (RF) classifier. Our results show that all the eight datasets produced moderately to highly accurate land cover maps, with overall accuracy over 84.31%. Among the eight datasets, two time series datasets of summer scenes (images from 1 June to 30 September) produced the highest accuracy (89.80% and 89.70%), followed by the median composite of the same input images (88.74%). The difference between these three classifications was not significant based on the McNemar test (p > 0.05). However, significant difference (p < 0.05) was observed for all other pairs involving one of these three datasets. The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data. The spatial consistency among the classification results was relatively low compared to the general high accuracy, showing that the selection of the dataset used in any classification on GEE is an important and crucial step, because the input images for the composition play an essential role in land cover classification, particularly with snowy, cloudy and expansive areas like Mongolia.

A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions

Dense time-series with coarse spatial resolution (DTCS) and sparse time-series with fine spatial resolution (STFS) data often provide complementary information. To make full use of this complementarity, this paper presents a novel spatiotemporal fusion model, the spatial time-series geostatistical deconvolution/fusion model (STGDFM), to generate synthesized dense time-series with fine spatial resolution (DTFS) data. Attributes from the DTCS and STFS data are decomposed into trend and residual components, and the spatiotemporal distributions of these components are predicted through novel schemes. The novelty of STGDFM lies in its ability to (1) consider temporal trend information using land-cover-specific temporal profiles from an entire DTCS dataset, (2) reflect local details of the STFS data using resolution matrix representation, and (3) use residual correction to account for temporary variations or abrupt changes that cannot be modeled from the trend components. The potential of STGDFM is evaluated by conducting extensive experiments that focus on different environments; spatially degraded datasets and real Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images are employed. The prediction performance of STGDFM is compared with those of a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM). Experimental results indicate that STGDFM delivers the best prediction performance with respect to prediction errors and preservation of spatial structures as it captures temporal change information on the prediction date. The superiority of STGDFM is significant when the difference between pair dates and prediction dates increases. These results indicate that STGDFM can be effectively applied to predict DTFS data that are essential for various environmental monitoring tasks.

Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine

Mapping Earth’s surface and its rapid changes with remotely sensed data is a crucial task to understand the impact of an increasingly urban world population on the environment. However, the impressive amount of available Earth observation data is only marginally exploited in common classifications. In this study, we use the computational power of Google Earth Engine and Google Cloud Platform to generate an oversized feature set in which we explore feature importance and analyze the influence of dimensionality reduction methods to object-based land cover classification with Support Vector Machines. We propose a methodology to extract the most relevant features and optimize an SVM classifier hyperparameters to achieve higher classification accuracy. The proposed approach is evaluated in two different urban study areas of Stockholm and Beijing. Despite different training set sizes in the two study sites, the averaged feature importance ranking showed similar results for the top-ranking features. In particular, Sentinel-2 NDVI, NDWI, and Sentinel-1 VV temporal means are the highest ranked features and the experiment results strongly indicated that the fusion of these features improved the separability between urban land cover and land use classes. Overall classification accuracies of 94% and 93% were achieved in Stockholm and Beijing study sites, respectively. The test demonstrated the viability of the methodology in a cloud-computing environment to incorporate dimensionality reduction as a key step in the land cover classification process, which we consider essential for the exploitation of the growing Earth observation big data. To encourage further research and development of reliable workflows, we share our datasets and publish the developed Google Earth Engine and Python scripts as free and open-source software.