Crop residue burning in China (2019-2021): Spatiotemporal patterns, environmental impact, and emission dynamics.

Crop residue burning (CRB) is a major contributor to air pollution in China. Current fire detection methods, however, are limited by either temporal resolution or accuracy, hindering the analysis of CRB's diurnal characteristics. Here we explore the diurnal spatiotemporal patterns and environmental impacts of CRB in China from 2019 to 2021 using the recently released NSMC-Himawari-8 hourly fire product. Our analysis identifies a decreasing directionality in CRB distribution in the Northeast and a notable southward shift of the CRB center, especially in winter, averaging an annual southward movement of 7.5°. Additionally, we observe a pronounced skewed distribution in daily CRB, predominantly between 17:00 and 20:00. Notably, nighttime CRB in China for the years 2019, 2020, and 2021 accounted for 51.9%, 48.5%, and 38.0% respectively, underscoring its significant environmental impact. The study further quantifies the hourly emissions from CRB in China over this period, with total emissions of CO, PM10, and PM2.5 amounting to 12,236, 2,530, and 2,258 Gg, respectively. Our findings also reveal variable lag effects of CRB on regional air quality and pollutants across different seasons, with the strongest impacts in spring and more immediate effects in late autumn. This research provides valuable insights for the regulation and control of diurnal CRB before and after large-scale agricultural activities in China, as well as the associated haze and other pollution weather conditions it causes.

Spatial distribution and main drivers of soil selenium in Taihu Lake Basin, Southeast China.

Selenium (Se) is an essential micronutrient that is both hazardous and beneficial to living organisms. However, few studies have examined soil Se distribution and its driving mechanisms on a large basin scale. Thus, multivariate statistics, geostatistics, boosted regression trees, and structural equation models were used to investigate the spatial distribution, driving factors, and multivariate interactions of soil Se based on 1753 topsoil samples (0-20 cm) from the Taihu Lake Basin. The results indicated that the soil Se concentration ranged from 0.12 to 57.26 mg kg-1, with a mean value of 0.90 mg kg-1. Overall, the spatial pattern of soil Se gradually decreased from south to north with approximately 1.06% of the soil contaminated with Se. Moisture index (MI), soil moisture (SM), and ≥ 0 â„ƒ accumulative temperature (AAT0) were the main determinants of soil Se accumulation. Additionally, the substantial effect of SM∩AAT0 on soil Se concentrations demonstrated that climate-soil interactions largely governed the spatial pattern of soil Se. The Se-enriched and Se-contaminated soils occurred mainly in regions with high precipitation, MI, SM, AAT0, and soil organic matter. This study provides a theoretical basis and practical guidance for the remediation of soil Se contamination and the sustainable development of Se-enriched agriculture.

Plant exosomes fused with engineered mesenchymal stem cell-derived nanovesicles for synergistic therapy of autoimmune skin disorders.

Existing therapeutics for autoimmune diseases remain problematic due to low efficacy, severe side effects, and difficulties to reach target tissues. Herein, we design multifunctional fusion nanovesicles that can target lesions for the treatment of autoimmune skin diseases. The grapefruit-derived exosome-like nanovesicles (GEVs) with anti-inflammatory and antioxidant effects are first encapsulated with CX5461, an immunosuppressant with anti-proliferative properties to form GEV@CX5461. In order to enhance therapeutic efficiency and safety, GEV@CX5461 are then fused with CCR6+ nanovesicles derived from membranes of engineered gingiva-derived mesenchymal stem cells (GMSCs). The resulting FV@CX5461 not only maintain the bioactivity of GEVs, CX5461, and GMSC membranes but also home to inflamed tissues rich in chemokine CCL20 through the chemotaxis function of CCR6 on FVs. Moreover, FV@CX5461 reduce the secretion of inflammatory factors, calm down Th17 cell activation, and induce Treg cell infiltration. Finally, impressive therapeutic efficiency in both psoriasis and atopic dermatitis disease models is demonstrated using FV@CX5461 to reshape the unbalanced immune microenvironment. A nanotherapeutic drug delivery strategy is developed using fusion nanovesicles derived from plant and animal cells with high clinical potential.

Causal inference from cross-sectional earth system data with geographical convergent cross mapping.

Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems. The generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect.

Sunglint reflection facilitates performance of spaceborne UV sensor in oil spill detection.

Ultraviolet Imager (UVI) onboard Haiyang-1C/D (HY-1C/D) satellites has been providing ultraviolet (UV) data to detect marine oil spills since 2018. Although the scale effect of UV remote sensing has been preliminarily interpreted, the application characteristics of spaceborne UV sensors with medium spatial resolution in oil spill detection deserve further investigation, especially the role of sunglint in the process of detection. In this study, the performance of the UVI is thoroughly assessed by the following aspects: image features of oils under sunglint, sunglint requirement for spaceborne UV detection of oils, and the stability of the UVI signal. The results indicate that in UVI images, it is sunglint reflection that determines the image features of spilled oils, and the appearance of sunglint can strengthen the contrast between oils and seawater. Besides, the required sunglint strength in spaceborne UV detection has been deduced to be 10-3 - 10-4 sr-1, which is higher than that in the VNIR wavelengths. Moreover, uncertainties in the UVI signal can meet the demand to discriminate between oils and seawater. The above results can confirm the capability of the UVI and the critical role of sunglint in spaceborne UV detection of marine oil spills, and provide new reference for spaceborne UV remote sensing.

Evaluating Urban Vitality of Street Blocks Based on Multi-Source Geographic Big Data: A Case Study of Shenzhen.

Urban vitality is the comprehensive form of regional development quality, sustainability, and attractiveness. Urban vitality of various regions within the cities has difference, and the quantitative evaluation of urban vitality within the cities can help guide to future city constructions. Evaluation of urban vitality needs the combination of multi-source data. Existing studies have developed index method and estimation models mainly based on geographic big data to evaluate urban vitality. This study aims to combine remote sensing data with geographic big data to evaluate urban vitality of Shenzhen at street block scale and build the estimation model by random forest method. Indexes and random forest model were built, and some further analyses were conducted. The results were: (1) urban vitality in Shenzhen was high in the coastal areas, business areas, and new towns; (2) compared to indexes, the estimation model had advantages of more accurate results, combination of various data, and the ability to analyze feature contributions; and (3) taxi trajectory, nighttime light, and housing rental data had the strongest influence on urban vitality.

[Optimization of urban ecological spatial structure based on network analysis: A case study of Changzhou City, China]. / 基于网络分析的城市生态空间结构优化­­ä»¥å¸¸å·žå¸‚为例.

The rapid development of economy and society leads to the rapid expansion of cities, resulting in the atrophy of urban ecological space and the decline of ecological function, as well as a serious threat to urban ecological security. It is of great significance for the sustainable development of a city to systematically analyze the structure of urban ecological space and put forward targeted protection and optimization measures. Taking Changzhou City as the research area and considering the natural ecological function and social service function of urban ecological space, we constructed two ecological networks, the "source-corridor" ecological network based on natural ecology and the "supply-demand" ecological network based on human ecology. For the "source-corridor" ecological network, quantitative analysis was mainly carried out from the importance of nodes, network connectivity and stability. For the "supply-demand" ecological network, quantitative analysis was mainly carried out from the importance of nodes, supply-demand equilibrium and stability. The results showed that the levels of connectivity and stability of the "source-corridor" ecological network in the main urban area of Changzhou were not high, the stability level of the "supply-demand" ecological network was general, and there was spatial mismatch between service supply and demands. From the perspective of connectivity and stability improvement, an optimization scheme of "source-corridor" ecological network with 12 additional source nodes and 57 corridors was proposed. From the perspective of supply-demand balance and stability improvement, an optimization scheme of "supply-demand" ecological network with 22 new supply nodes was proposed. Compared with the original "source-corridor" ecological network, the connectivity level of the optimized network was improved by 10%, and the network stability was improved by 0.05. Compared with the initial "supply-demand" ecological network, the service level of the optimized network was improved by 4%, and the network stability was improved by 0.10. Finally, we integrated the two ecological networks, and formulated the implementation plan of protection and management for both the current protected patches and the new ecological nodes.

Effects of Substitution Ratios of Zinc-Substituted Hydroxyapatite on Adsorption and Desorption Behaviors of Bone Morphogenetic Protein-2.

Understanding interactions between bone morphogenetic proteins (BMPs) and biomaterials is of great significance in preserving the structure and bioactivity of BMPs when utilized in clinical applications. Currently, bone morphogenetic protein-2 (BMP-2) is one of the most important growth factors in bone tissue engineering; however, atomistic interactions between BMP-2 and zinc-substituted hydroxyapatite (Zn-HAP, commonly used in artificial bone implants) have not been well clarified until now. Thus, in this work, the interaction energies, binding/debinding states, and molecular structures of BMP-2 upon a series of Zn-HAP surfaces (Zn-HAPs, 1 at%, 2.5 at%, 5 at%, and 10 at% substitution) were investigated by hybrid molecular dynamics (MD) and steered molecular dynamics (SMD) simulations. Meanwhile, cellular studies including alkaline phosphatase (ALP) activity and reverse transcription-polymerase chain reaction (RT-PCR) assay were performed to verify the theoretical modeling findings. It was found that, compared to pure HAP, Zn-HAPs exhibited a higher binding affinity of BMP-2 at the adsorption process; meanwhile, the detachment of BMP-2 upon Zn-HAPs was more difficult at the desorption process. In addition, molecular structures of BMP-2 could be well stabilized upon Zn-HAPs, especially for Zn10-HAP (with a 10 at% substitution), which showed both the higher stability of cystine-knots and less change in the secondary structures of BMP-2 than those upon HAP. Cellular studies confirmed that higher ALP activity and osteogenic marker gene expression were achieved upon BMP-2/Zn-HAPs than those upon BMP-2/HAP. These findings verified that Zn-HAPs favor the adsorption of BMP-2 and leverage the bioactivity of BMP-2. Together, this work clarified the interaction mechanisms between BMP-2 and Zn-HAPs at the atom level, which could provide new molecular-level insights into the design of BMP-2-loaded biomaterials for bone tissue engineering.

Direct and indirect impacts of urbanization on vegetation growth across the world's cities.

Urban environments, regarded as "harbingers" of future global change, may exert positive or negative impacts on urban vegetation growth. Because of limited ground-based experiments, the responses of vegetation to urbanization and its associated controlling factors at the global scale remain poorly understood. Here, we use satellite observations from 2001 to 2018 to quantify direct and indirect impacts of urbanization on vegetation growth in 672 worldwide cities. After controlling for the negative direct impact of urbanization on vegetation growth, we find a widespread positive indirect effect that has been increasing over time. These indirect effects depend on urban development intensity, population density, and background climate, with more pronounced positive effects in cities with cold and arid environments. We further show that vegetation responses to urbanization are modulated by a cities' developmental status. Our findings have important implications for understanding urbanization-induced impacts on vegetation and future sustainable urban development.

Causation inference in complicated atmospheric environment.

Reliable attribution is crucial for understanding various climate change issues. However, complicated inner-interactions between various factors make causation inference in atmospheric environment highly challenging. Taking PM2.5-Meteorology causation, which involves a large number of non-Linear and uncertain interactions between many meteorological factors and PM2.5, as a case, we examined the performance of a series of mainstream statistical models, including Correlation Analysis (CA), Partial Correlation Analysis (PCA), Structural Equation Model (SEM), Convergent Cross Mapping (CCM), Partial Cross Mapping (PCM) and Geographical Detector (GD). From a coarse perspective, the Top 3 major meteorological factors for PM2.5 in 190 cities across China extracted using different models were generally consistent. From a strict perspective, the extracted dominant meteorological factor for PM2.5 demonstrated large model variations and shared a limited consistence. Such models as SEM and PCM, which are capable of further separating direct and indirect causation in simple systems, performed poorly to identify the direct and indirect PM2.5-Meteorology causation. The notable inconsistence denied the feasibility of employing multiple models for better causation inference in atmospheric environment. Instead, the sole use of CCM, which is advantageous in dealing with non-linear causation and removing disturbing factors, is a preferable strategy for causation inference in complicated ecosystems. Meanwhile, given the multi-direction, uncertain interactions between many variables, we should be more cautious and less ambitious on the separation of direct and indirect causation. For better causation inference in the complicated atmospheric environment, the combination of statistical models and atmospheric models, and the further exploration of Deep Neural Network can be promising strategies.

Global simulation of fine resolution land use/cover change and estimation of aboveground biomass carbon under the shared socioeconomic pathways.

Land use change driven by human activities plays a critical role in the terrestrial carbon budget through habitat loss and vegetation change. Despite the projections of the global population and economic growth under the framework of the Shared Socioeconomic Pathways (SSPs), little is known of land use/cover change (LUCC) at a fine spatial resolution and how carbon pools respond to LUCC under different SSPs. This study projected the future global LUCC with 1 km spatial resolution and a 10-year time step from 2010 to 2100 and then explored its direct impacts on aboveground biomass carbon (AGB) under SSPs. Scenario SSP3 yields the highest global cropland expansion, among which approximately 48% and 46% is expected to be located in the current forest land and grassland, respectively. Scenario SSP1 has the largest forest expansion and is mainly converted from grassland (54%) and cropland (30%). Due to the spatial change in land use/cover, global AGB loss is expected to reach approximately 3.422 Pg C in 2100 under scenario SSP3 and increases by approximately 0.587 Pg C under scenario SSP1. Africa is expected to lose 30% of AGB under the scenario SSP3. Aboveground biomass in Asia will fix 0.774 Pg C to reverse the AGB loss in 2100 under scenario SSP1. The global carbon loss estimated by the land use products with 10 km and 25 km resolution are less than that with 1 km by 1.5% (ranging from -11.2% in Africa to +34.0% in Oceania) and 2.9% (ranging from -11.8% in Africa to +24.0% in Oceania), respectively. These findings suggest that sufficient spatial details in the existing SSP scenario projections could reduce the uncertainties of AGB assessment, and reasonable land use development and management is a key measure to mitigate the negative impacts of LUCC on the biomass carbon pool.

Satellite-derived bathymetry using Landsat-8 and Sentinel-2A images: assessment of atmospheric correction algorithms and depth derivation models in shallow waters.

Satellite-derived bathymetry (SDB) has an extensive prospect in nearshore bathymetry for its high efficiency and low costs. Atmospheric correction and bathymetric modeling are critical processes in SDB, and examining the performance of related algorithms and models will contribute to the formulation of reliable bathymetry strategies. This study explored the effectiveness of three general atmospheric correction algorithms, namely Second Simulation of a Satellite Signal in the Solar Spectrum (6S), Atmospheric correction for OLI 'lite' (ACOLITE), and QUick Atmospheric Correction (QUAC), in depth retrieval from Landsat-8 and Sentinel-2A images using different SDB models over Ganquan Island and Oahu Island. The bathymetric Light Detection and Ranging (LiDAR) data was used for SDB model training and accuracy verification. The results indicated that the three atmospheric correction algorithms could provide effective corrections for SDB. For the SDB models except log-transformed band ratio model (LBR) and support vector machine (SVM), the impact of different atmospheric corrections on bathymetry was basically the same. Furthermore, we assessed the performance of six different SDB models: Lyzenga's model (LM), generalized additive model (GAM), LBR, SVM, multilayer perceptron (MLP), and random forest (RF). The bathymetric accuracy, consistency of bathymetric maps and generalization ability were considered for the assessment. Given sufficient training data, the accuracy of the machine learning models (SVM, MLP, RF) was generally superior to that of the empirical inversion models (LM, GAM, LBR), with the root mean square error (RMSE) varied between 0.735 m to 1.177 m. MLP achieved the best accuracy and consistency. When the depth was deeper than 15 m, the bathymetry error of all the SDB models increased sharply, and LM, LBR and SVM reached the upper limit of depth retrieval capability at 20-25 m. In addition, LM and LBR were demonstrated to have better adaptability in heterogeneous environment without training data.

Incorporating ecosystem services to assess progress towards sustainable development goals: A case study of the Yangtze River Economic Belt, China.

To promote coordinated development of the global economy, society, and environment, The 2030 Agenda for Sustainable Development of the United Nations (UN) specifies 17 sustainable development goals (SDGs) with 169 nested targets. Studies have shown that a healthy ecological environment and stable ecosystem services (ESs) provide an important foundation for achieving the SDGs. Due to the lack of quantitative methods for assessing the contribution of ESs to SDGs, very few empirical studies have been conducted to examine how knowledge of ESs can guide regional sustainable development. From the perspective of an ES cascade, we proposed a systematic approach for quantifying progress towards environment-related SDGs in terms of ES indicators, based on the Yangtze River Economic Belt (YREB). The scores of multiple SDGs and the SDG Index increased significantly across the YREB from 2000 to 2015, mainly attributed to the improvement of food and water provisioning services. Significant spatiotemporal variation in sustainable development levels was observed between provinces within the YREB. For example, the sustainable development levels in the eastern YREB clearly trailed those in the central and western YREB, and the SDG scores and SDG Index scores declined gradually. Among 11 provincial regions in the YREB, the SDG Index scores of nine provincial regions increased gradually, whereas those of Zhejiang and Shanghai declined. Strengthening the assessment of the ES value for generating more comprehensive ES indicators and applying an integrated ES-based approach for monitoring and comparing the process of local and global sustainable development, are promising areas for future research.

Spatial analysis and evaluation of medical resource allocation in China based on geographic big data.

BACKGROUND: Spatial allocation of medical resources is closely related to people's health. Thus, it is important to evaluate the abundance of medical resources regionally and explore the spatial heterogeneity of medical resource allocation. METHODS: Using medical geographic big data, this study analyzed 369 Chinese cities and constructed a medical resource evaluation model based on the grading of medical institutions using the Delphi method. It evaluated China's medical resources at three levels (economic sectors, economic zones, and provinces) and discussed their spatial clustering patterns. Geographically weighted regression was used to explore the correlations between the evaluation results and population and gross domestic product (GDP). RESULTS: The spatial heterogeneity of medical resource allocation in China was significant, and the following general regularities were observed: 1) The abundance and balance of medical resources were typically better in the east than in the west, and in coastal areas compared to inland ones. 2) The average primacy ratio of medical resources in Chinese cities by province was 2.30. The spatial distribution of medical resources in the provinces was unbalanced, showing high concentrations in the primate cities. 3) The allocation of medical resources at the provincial level in China was summarized as following a single-growth pole pattern supplemented by bipolar circular allocation and balanced allocation patterns. The agglomeration patterns of medical resources in typical cities were categorized into single-center and balanced development patterns. GDP was highly correlated to the medical evaluation results, while demographic factors showed, low correlations. Large cities and their surrounding areas exhibited obvious response characteristics. CONCLUSIONS: These findings provide policy-relevant guidance for improving the spatial imbalance of medical resources, strengthening regional public health systems, and promoting government coordination efforts for medical resource allocation at different levels to improve the overall functioning of the medical and health service system and bolster its balanced and synergistic development.

Assessing potential ecosystem service dynamics driven by urbanization in the Yangtze River Economic Belt, China.

Ecosystem services (ESs) link natural and social processes and play an important role in sustaining ecological security, human well-being, and sustainable development. However, uncertainties in future socioeconomic land use drivers may result in very different land use dynamics and consequences for land-based ESs. In this study, land use transitions in the Yangtze River Economic Belt (YREB) were simulated in the short term (2018-2030), medium term (2030-2040), and long term (2040-2050) using the future land use simulation (FLUS) model based on the local shared socioeconomic pathways (SSPs). According to the projected land use types, six ESs were quantified and assessed regarding how they would evolve under particular land use changes. The results of land use simulations showed that the main features were urban sprawl and a decrease in cropland. In particular, intensive urban sprawl occurred around existing urban areas, and a large amount of cultivated land was converted into urban land. In the YREB, urban land will increase from 88,441 km2 in 2018 to 156,173-192,900 km2 in 2050, while the cropland area will decrease from 607,131 km2 in 2018 to 500,183-596,313 km2 in 2050. As a consequence of urban expansion, all ESs exhibited decreasing trends, except for several services under SSP1. Food production (FP), carbon storage (CS), water conservation (WC), soil retention (SR), air purification (AP), and habitat quality (HQ) will decline by 8.98-21.4%, 1.95-6.781%, 2.97-6.5%, 0.9-1.7%, 1.20-5.15%, and 6.11-12.86%, respectively. The ES integrative assessment indicated distinct provincial differences. Developed eastern provinces have higher populations and urbanization; however, these traits result in greater ES losses. We suggest that future land management should control the blind expansion of urban land and enhance the protection of cropland and natural habitats to reduce ES losses.

Estimating the impact of ground ozone concentrations on crop yields across China from 2014 to 2018: A multi-model comparison.

Ground level ozone exerts a strong impact on crop yields, yet how to properly quantify ozone induced yield losses in China remains challenging. To this end, we employed a series of O3-crop models to estimate ozone induced yield losses in China from 2014 to 2018. The outputs from all models suggested that the total Relative Yield Losses (RYL) of wheat in China from 2014 to 2018 was 18.4%-49.3% and the total RYL of rice was 6.2%-52.9%. Consequently, the total Crop Production Losses (CPL) of wheat and rice could reach 63.9-130.4 and 28.3-35.4 million tons, and the corresponding Total Economic Losses (TEL) could reach 20.5-44.7 and 11.0-15.3 billion dollars, stressing the great importance and urgency of national ozone management. Meanwhile, the estimation outputs highlighted the large variations between different regional O3-crop models when applying to large scales. Instead of applying one unified O3-crop models to all regions across China, we also explored the strategy of employing specific O3-crop models in corresponding (and neighboring) regions to estimate ozone induced yield loss in China. The comparison of two strategies suggested that the mean value from multiple models may still present an inconsistent over/underestimation trend for different crops. Therefore, it is a preferable strategy to employ corresponding O3-crop models in different regions for estimating the national crop losses caused by ozone pollution. However, the severe lack of regional O3-crop models in most regions across China makes a robust estimation of national yield losses highly challenging. Given the large variations between O3-crop interactions across regions, a systematic framework with massive regional O3-crop models should be properly designed and implemented.

Coupling urban 3-D information and circuit theory to advance the development of urban ecological networks.

Ongoing, rapid urban growth accompanied by habitat fragmentation and loss challenges biodiversity conservation and leads to decreases in ecosystem services. Application of the concept of ecological networks in the preservation and restoration of connections among isolated patches of natural areas is a powerful conservation strategy. However, previous approaches often failed to objectively consider the impacts of complex 3-D city environments on ecological niches. We used airborne lidar-derived information on the 3-D structure of the built environment and vegetation and detailed land use and cover data to characterize habitat quality, niche diversity, and human disturbance and to predict habitat connectivity among 38 identified habitat core areas (HCAs) in Nanjing, China. We used circuit theory and Linkage Mapper to create a landscape resistance layer, simulate habitat connectivity, and identify and prioritize important corridors. We mapped 64 links by using current flow centrality to evaluate each HCA's contribution and the links that facilitate intact connectivity. Values were highest for HCA links located in the west, south, and northeast of the study area, where natural forests with complex 3-D structures predominate. Two smaller HCA areas had high centrality scores relative to their extents, which means they could act as important stepping stones in connectivity planning. The mapped pinch-point regions had narrow and fragile links among the HCAs, suggesting they require special protection. The barriers with the highest impact scores were mainly located at the HCA connections to Purple Mountain and, based on these high scores, are more likely to indicate important locations that can be restored to improve potential connections. Our novel framework allowed us to sufficiently convey spatially explicit information to identify targets for habitat restoration and potential pathways for species movement and dispersal. Such information is critical for assessing existing or potential habitats and corridors and developing strategic plans to balance habitat conservation and other land uses based on scientifically informed connectivity planning and implementation.

Acoplamiento de la Información Urbana en 3-D y la Teoría de Circuitos para Avanzar el Desarrollo de las Redes Ecológicas Urbanas Resumen El rápido crecimiento urbano en curso acompañado por la fragmentación y pérdida de hábitats obstaculizan la conservación de la biodiversidad y llevan a una disminución de los servicios ambientales. La aplicación del concepto de redes ecológicas en la preservación y restauración de las conexiones entre los fragmentos aislados de áreas naturales es una estrategia poderosa para la conservación. Sin embargo, las estrategias previas con frecuencia han fallado al no considerar de manera objetiva los impactos del ambiente complejo y tridimensional que tienen las ciudades sobre los nichos ecológicos. Usamos información derivada de lidar aéreos sobre la estructura tridimensional del ambiente construido y de la vegetación y detallamos la información sobre el uso y la cobertura del suelo para caracterizar la calidad del hábitat, la diversidad de nichos y la perturbación humana y así predecir la conectividad de hábitat entre 38 áreas nucleares de hábitat (ANHs) en Nanjing, China. Usamos la teoría de circuitos y el programa Linkage Mapper para crear una capa de resistencia de paisaje, simular la conectividad de hábitat e identificar y priorizar los corredores importantes. Mapeamos 64 conexiones mediante la centralidad del flujo de corriente para evaluar la contribución de cada ANH y las conexiones que facilitan la conectividad intacta. Los valores más altos fueron para las conexiones de ANH ubicadas en el oeste, sur y norte del área de estudio, en donde predominan los bosques naturales con estructuras tridimensionales complejas. Dos áreas más pequeñas de ANH tuvieron puntajes altos de centralidad en relación con sus extensiones, lo que significa que podrían fungir como pasos intermedios importantes en la planeación de la conectividad. Las regiones mapeadas de los puntos de fijación tuvieron conexiones estrechas y frágiles entre las ANHs, lo que sugiere que requieren de protección especial. Las barreras con los puntajes más elevados de impacto estuvieron localizadas principalmente en las conexiones entre las ANH y la Tierra de las Montañas Púrpuras. Con base en estos puntajes elevados hay mayor probabilidad de que indiquen localidades importantes que pueden ser restauradas para mejorar el potencial de las conexiones. Nuestro novedoso marco de trabajo nos permitió transmitir adecuadamente la información espacialmente explícita para identificar los objetivos de la restauración de hábitat y los caminos potenciales para el movimiento y la dispersión de las especies. Tal información es crítica para el análisis de los hábitats y corredores existentes o potenciales y para el desarrollo de planes estratégicos para equilibrar la conservación del hábitat y otros usos de suelo con base en la planeación e implementación de la conectividad científicamente informada.

Hierarchical Classification of Urban ALS Data by Using Geometry and Intensity Information.

Airborne laser scanning (ALS) can acquire both geometry and intensity information of geo-objects, which is important in mapping a large-scale three-dimensional (3D) urban environment. However, the intensity information recorded by ALS will be changed due to the flight height and atmospheric attenuation, which decreases the robustness of the trained supervised classifier. This paper proposes a hierarchical classification method by separately using geometry and intensity information of urban ALS data. The method uses supervised learning for stable geometry information and unsupervised learning for fluctuating intensity information. The experiment results show that the proposed method can utilize the intensity information effectively, based on three aspects, as below. (1) The proposed method improves the accuracy of classification result by using intensity. (2) When the ALS data to be classified are acquired under the same conditions as the training data, the performance of the proposed method is as good as the supervised learning method. (3) When the ALS data to be classified are acquired under different conditions from the training data, the performance of the proposed method is better than the supervised learning method. Therefore, the classification model derived from the proposed method can be transferred to other ALS data whose intensity is inconsistent with the training data. Furthermore, the proposed method can contribute to the hierarchical use of some other ALS information, such as multi-spectral information.