Spatial Distribution and Potential Sources of Five Heavy Metals and One Metalloid in the Soils of Xiamen City, China.

In our study, we collected 146 surface soil samples in Xiamen City and measured the concentrations of five heavy metals (Cr, Cu, Pb, Ni, and Zn) and one metalloid (As). Multivariate statistics, geostatistics and Random Forest methods were applied to identify the potential sources and spatial variation of the six elements. The results revealed that As, Cr, and Ni originated mainly from industrial activities, and higher concentrations were found in developed areas. The amounts of Cu, Pb, and Zn in soils were mainly predetermined by soil parent material and agricultural activities. Besides, the atmospheric deposition rather than industrial activities substantially influenced the accumulation of Pb in the soils near the boundary between Tong'an and Quanzhou City, because there were few industries but many forests in this area. Because of the connections of the six elements with factor values of factor analysis, appropriate and accessible covariates could be used in co-kriging to increase the accuracies of interpolation of heavy metal and metalloid concentrations relative to that in ordinary kriging.

An application of remote sensing data in mapping landscape-level forest biomass for monitoring the effectiveness of forest policies in northeastern China.

Monitoring the dynamics of forest biomass at various spatial scales is important for better understanding the terrestrial carbon cycle as well as improving the effectiveness of forest policies and forest management activities. In this article, field data and Landsat image data acquired in 1999 and 2007 were utilized to quantify spatiotemporal changes of forest biomass for Dongsheng Forestry Farm in Changbai Mountain region of northeastern China. We found that Landsat TM band 4 and Difference Vegetation Index with a 3 × 3 window size were the best predictors associated with forest biomass estimations in the study area. The inverse regression model with Landsat TM band 4 predictor was found to be the best model. The total forest biomass in the study area decreased slightly from 2.77 × 10(6) Mg in 1999 to 2.73 × 10(6) Mg in 2007, which agreed closely with field-based model estimates. The area of forested land increased from 17.9 × 10(3) ha in 1999 to 18.1 × 10(3) ha in 2007. The stabilization of forest biomass and the slight increase of forested land occurred in the period following implementations of national forest policies in China in 1999. The pattern of changes in both forest biomass and biomass density was altered due to different management regimes adopted in light of those policies. This study reveals the usefulness of the remote sensing-based approach for detecting and monitoring quantitative changes in forest biomass at a landscape scale.

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou.

The rapid expansion of different types of urban land continues to erode natural and semi-natural ecological space and causes irreversible ecological damage to rapidly industrialized and urbanized areas. This work considers Quanzhou, a typical industrial and trade city in southeastern China as the research area and uses a Markov chain integrated into the patch-generating land use simulation (PLUS) model to simulate the urban expansion of Quanzhou from 2005 to 2018. The PLUS model uses the random forest algorithm to determine the contribution of driving factors and simulate the organic and spontaneous growth process based on the seed generation mechanism of multi-class random patches. Next, leveraging the importance of ecosystem services and ecological sensitivity as indicators of evaluation endpoints, we explore the temporal and spatial evolution of ecological risks from 2018 to 2031 under the scenarios of business as usual (BAU), industrial priority, and urban transformation scenarios. The evaluation endpoints cover water conservation service, soil conservation service, biodiversity maintenance service, soil erosion sensitivity, riverside sensitivity, and soil fertility. The ecological risk studied in this work involves the way in which different types of construction land expansion can possibly affect the ecosystem. The ecological risk index is divided into five levels. The results show that during the calibration simulation period from 2005 to 2018 the overall accuracy and Kappa coefficient reached 91.77% and 0.878, respectively. When the percent-of-seeds (PoS) parameter of random patch seeds equals 0.0001, the figure of merit of the simulated urban construction land improves by 3.9% compared with the logistic-based cellular automata model (Logistic-CA) considering organic growth. When PoS = 0.02, the figure of merit of the simulated industrial and mining land is 6.5% higher than that of the Logistic-CA model. The spatial reconstruction of multiple types of construction land under different urban development goals shows significant spatial differentiation on the district and county scale. In the industrial-priority scenario, the area of industrial and mining land is increased by 20% compared with the BAU scenario, but the high-level risk area is 42.5% larger than in the BAU scenario. Comparing the spatial distribution of risks under the BAU scenario, the urban transition scenario is mainly manifested as the expansion of medium-level risk areas around Quanzhou Bay and the southern region. In the future, the study area should appropriately reduce the agglomeration scale of urban development and increase the policy efforts to guide the development of industrial land to the southeast.

Quantifying Spatiotemporal Changes in Human Activities Induced by COVID-19 Pandemic Using Daily Nighttime Light Data.

The COVID-19 pandemic caused drastic changes in human activities and nighttime light (NTL) at various scales, providing a unique opportunity for exploring the pattern of the extreme responses of human community. This study used daily NTL data to examine the spatial variations and temporal dynamics of human activities under the influence of COVID-19, taking Chinese mainland as the study area. The results suggest that the change in the intensity of NTL is not correlated to the number of confirmed cases, but reflects the changes in human activities and the intensity of epidemic prevention and control measures within a region. During the outbreak period, the major provincial capitals and urban agglomerations were affected by COVID-19 more than smaller cities. During the recovery, different regions showed different recovery processes. The cities in West and Northeast China recovered steadily while the recovery in coastal cities showed relatively greater fluctuations due to an increase in imported cases. Wuhan, the most seriously affected city in China, did not recover until the end of March. Nevertheless, as of 31 March, the overall NTL across China had recovered to an 89.5% level of the same period in the previous year. The high consistency between the big data of travel intensity and NTL further proved the validity of the results of this study. These findings imply that daily NTL data are effective for rapidly monitoring the dynamic changes in human activities, and can help evaluate the effects of control measures on human activities during major public health events.

Automatic Identification of Individual Primates with Deep Learning Techniques.

The difficulty of obtaining reliable individual identification of animals has limited researcher's ability to obtain quantitative data to address important ecological, behavioral, and conservation questions. Traditional marking methods placed animals at undue risk. Machine learning approaches for identifying species through analysis of animal images has been proved to be successful. But for many questions, there needs a tool to identify not only species but also individuals. Here, we introduce a system developed specifically for automated face detection and individual identification with deep learning methods using both videos and still-framed images that can be reliably used for multiple species. The system was trained and tested with a dataset containing 102,399 images of 1,040 individuals across 41 primate species whose individual identity was known and 6,562 images of 91 individuals across four carnivore species. For primates, the system correctly identified individuals 94.1% of the time and could process 31 facial images per second.

China's classification-based forest management: procedures, problems, and prospects.

China's new Classification-Based Forest Management (CFM) is a two-class system, including Commodity Forest (CoF) and Ecological Welfare Forest (EWF) lands, so named according to differences in their distinct functions and services. The purposes of CFM are to improve forestry economic systems, strengthen resource management in a market economy, ease the conflicts between wood demands and public welfare, and meet the diversified needs for forest services in China. The formative process of China's CFM has involved a series of trials and revisions. China's central government accelerated the reform of CFM in the year 2000 and completed the final version in 2003. CFM was implemented at the provincial level with the aid of subsidies from the central government. About a quarter of the forestland in China was approved as National EWF lands by the State Forestry Administration in 2006 and 2007. Logging is prohibited on National EWF lands, and their landowners or managers receive subsidies of about 70 RMB (US$10) per hectare from the central government. CFM represents a new forestry strategy in China and its implementation inevitably faces challenges in promoting the understanding of forest ecological services, generalizing nationwide criteria for identifying EWF and CoF lands, setting up forest-specific compensation mechanisms for ecological benefits, enhancing the knowledge of administrators and the general public about CFM, and sustaining EWF lands under China's current forestland tenure system. CFM does, however, offer a viable pathway toward sustainable forest management in China.

Distribution and Health Risk Assessment of Trace Metals in Soils in the Golden Triangle of Southern Fujian Province, China.

In recent years, intensified industrialization and rapid urbanization have accelerated the accumulation of trace metals in topsoils of the Golden Triangle of Southern Fujian Province in China. Trace metals can cause adverse impacts on ecosystems and human health. In order to assess the ecological and human health risks of trace metals in the Golden Triangle region and to determine the distribution and degree of pollution of trace metals, 456 soil samples were collected from 28 districts. The concentrations of six metals (As, Cr, Cu, Ni, Pb, and Zn) were analyzed to assess ecological risk using the geoaccumulation index (Igeo) and the potential ecological risk index (RI). The United States Environmental Protection Agency (USEPA) model was applied to calculate health risk. The average soil concentrations of the six elements are ranked as follows: As < Ni < Cu < Cr < Pb < Zn. Inverse distance weighting (IDW) interpolation maps showed that Cr, Cu, Ni, and Zn are enriched in the soils of developed areas, while As and Pb are enriched in the soils of undeveloped areas. The Igeo showed that the levels of metals in most soil samples are below polluting levels. Similarly, RI values indicated that trace metals pose low potential ecological risk in the region's soils. The Hazard Quotient (HQ) ranked the mean total noncarcinogenic risk of the six metals, for both children and adults, as follows: As > Pb > Cr > Ni > Cu >Zn. The mean carcinogenic risk (CR) of the metals in the region's soils are ranked as follows: Cr > As > Ni. The Hazard Index (HI) values indicated that 3.7% of soils contained unsafe levels of toxic metals for children and total carcinogenic risk (TCR) values indicated that 23.3% of soils contained unsafe levels, indicating that children are facing both noncarcinogenic and carcinogenic risks from trace metals. Principal component analysis (PCA) and matrix cluster analysis were used to identify pollution sources and classified trace metals and soil samples into two and five groups, respectively. The five groups represented the effects of different land use types, including agricultural area, residential and public area, industrial area, forest, and industrial area and roadside, based on the contents of trace metals in soils. Industrial, agricultural and traffic activities attribute to the enrichment of Cr, Cu, Ni, Pb, and Zn in the region's soils. Moreover, the accumulation of As and Pb are also attributed to atmospheric deposition. These results can contribute to a better understanding of pollution, ecological risks, and human health risks from trace metals on large regional scales like the Golden Triangle of Southern Fujian Province.

Assessment of Regional Ecosystem Health-A Case Study of the Golden Triangle of Southern Fujian Province, China.

Intensifying urbanization and rapid population growth in Fujian Province, China, has caused pollution of air and water resources; this has adversely impacted ecosystems and human health. China has recently begun pursuing a massive infrastructure and economic development strategy called the Belt and Road Initiative, which could potentially cause further environmental damage. Evaluations of ecosystem health are therefore a first step towards identifying the potential impacts from the development and planning sustainable development strategies in the Golden Triangle of Southern Fujian. To this end, our study analyzed landscape patterns and evaluated ecosystem health in this region. We used an index system method to develop a pressure⁻state⁻response (PSR) model for assessing the region’s ecosystem health. We found that: (1) the landscape type with the greatest area in the study region is cultivated land and there were no areas that were undisturbed by human activity; (2) the overall ecological health of the region is good, but there is distinct variation across the region. This study incorporates the landscape pattern into an evaluation of ecosystem health. Using counties as evaluation units, we provide a general evaluation index for this scale. The methods reported here can be used in complex ecological environments to inform sustainable management decisions.

Forest ecosystem services of changbai mountain in china.

The forest ecosystem of the Changbai Mountain is the most typical upland temperate forest ecosystem in eastern Asia. It is also of the most primitive vegetation type that came into being through the natural succession of soil and vegetation following volcanic eruption. The forest ecosystem has great importance for maintaining the structures and functions of the watershed ecosystems of the Songhua River, the Yalu River and the Tumen River. We combined physical assessment method (PAM) with the value assessment method (VAM) to evaluate the forest ecosystem services of the northern slope of the, including eco-tourism, forest by-products, timber, soil and water conservation, air purification, and the recycling of nutritive elements. We also assessed the integrated forest ecosystem service and analyzed its dynamics. The service value provided by the Changbai Mountain forest ecosystem amounts up to RMB 3.38 x 10(12) yuan, of which, water conservation is 66%, water conservation and air purification together make up 80%, while the timber value is only 7%. Therefore, developing the ecosystem services besides timber is the best way to exert the integrated value of the forest ecosystem services of Changbai Mountain.

Certainties and uncertainties of land cover statistics in China.

Vegetation or land cover maps have been made directly or indirectly available for the entire territory of China. The certainties and uncertainties of land cover statistics were analyzed by comparing three data sources: China's Vegetation Map, IGPB DISCover, and University of Maryland Product. Great similarities in the statistics of 7 aggregated land cover types were found among the three data sources,particularly between the two global land cover maps. The per-pixel agreement between any 2 of 3 maps was between 38.0%-51.4%; the per-pixel agreement among all three maps was only 27.1%. Certainties were found in regions where vegetation types are typical and human land use practice is relatively homogenous; the uncertainties occurred to either vegetation transition zones or regions where land cover types and land use practice are relatively diversified. Systematic and multidisciplinary efforts are necessary to promote accurate mapping of nationwide land cover types in China. Intensive efforts should be made in regions where uncertainties of land cover information are found.

[Ecological classification system of forest landscape in eastern mountainous region of Liaoning Province].

Based on Digital Elevation Models (DEM) and satellite SPOT-5 data, and by using the spatial analysis function in Geographic Information System, a hierachical Ecological Classification System of forest landscape was developed for the eastern mountainous region of Liaoning Province, and the two lowest layers in the hierachical framework, Ecological Land Types (ELTs) and Ecological Land Type Phases (ELTPs), were mapped. The results indicated that there were 5 ELTs and 34 ELTPs. The boundaries of ELTs, which presented the potential vegetation distribution and potential forestry ecosystem productivity, were determined by environmental conditions quantified by DEM. ELTPs were classified by overlaying ELTs with forest vegetation data layers which were obtained from remotely sensed data, forest inventory data, and ground data. The ELTPs represented the divisions of land in terms of both natural and human-induced forest conditions, and therefore, were reliable units for forest inventories and management. ELTPs could function as conventional forest inventory sub-compartments. By this means, forestry departments could adjust forest management planning and forest management measures from the viewpoint of forest landscape scale to realize forest ecosystem management.

[Accuracy evaluation of land vegetative cover index by RS detection].

The accuracy of GIS data is important in GIS application. In this paper, a fictitious case and an example were used to discuss the relationship between accuracy of land vegetative cover index and accuracy of image classification, and this relationship was explained by mathematics. The accuracy of land vegetative cover index rested with the accuracy of image classification, but had no direct relations with total classification accuracy. User accuracy and producer accuracy had more direct control on the accuracy of land vegetative cover index than total classification accuracy; and hence, they could not be ignored in the image classification. To improve the accuracy of land vegetative cover index, user accuracy and producer accuracy of the same land type should be consistent to the greatest extent.

[Advances in research on ecological land classification].

Land classification is the base of land evaluation, land planning and land management, and plays an important role in the sustainable development of forestry resource. Ecological land classification (ELC) is the main approuch and direction of land classification. Along with the development of landscape ecology and 3S techniques, developing of ecological classification system (ECS) becomes the keystone of ELC. The definition, history, basic characteristics of ELC, the theory of ECS and its' prospects were systematically reviewed in this paper. The prospects were also described for the development of ECS methodology and the application of ELC. The general direction of ecological land research, which was a multi-factors and multi-levels syntheses based on the multi-objects administration of resource (forest, land and waters), was the combination of quantificational and qualitative research. It is necessary to carry out relevant researches in China.

[Ecological distribution of mycorrihizal fungi associated with oak in Dandong district of Liaoning Province].

With plot cruising and rout investigation methods, 36 species of ectomycorrhizal fungi associated with oaks were found in natural secondary oak forest in Dandong district of Liaoning Province. They were subordinated to 7 familyies, 13 genera. Studies on the relationship of the ecological distribution of mycorrhizal fungi with tree species, tree age and seasons showed that 29 species mycorrhizal fugi preferred to grow with Q. mongolica, The species of Russulla were associated with a wider tree ages of oak, and the species of Amanita were most likely to grow with old tree. July and August were the occurrence peak of mycorrhizal fungi. 75% species occurred in July, and 100% species occurred in August. The relative density of the occurrence number was the highest in mid-August (19.5%) and last-August (18.4%).