Anthropogenic activities dominated tropical forest carbon balance in two contrary ways over the Greater Mekong Subregion in the 21st century.

The tropical forest carbon (C) balance threatened by extensive socio-economic development in the Greater Mekong Subregion (GMS) in Asia is a notable data gap and remains contentious. Here we generated a long-term spatially quantified assessment of changes in forests and C stocks from 1999 to 2019 at a spatial resolution of 30 m, based on multiple streams of state-of-the-art high-resolution satellite imagery and in situ observations. Our results show that (i) about 0.54 million square kilometers (21.0% of the region) experienced forest cover transitions with a net increase in forest cover by 4.3% (0.11 million square kilometers, equivalent to 0.31 petagram of C [Pg C] stocks); (ii) forest losses mainly in Cambodia, Thailand, and in the south of Vietnam, were also counteracted by forest gains in China due mainly to afforestation; and (iii) at the national level during the study period an increase in both C stocks and C sequestration (net C gain of 0.087 Pg C) in China from new plantation, offset anthropogenetic emissions (net C loss of 0.074 Pg C) mainly in Cambodia and Thailand from deforestation. Political, social, and economic factors significantly influenced forest cover change and C sequestration in the GMS, positively in China while negatively in other countries, especially in Cambodia and Thailand. These findings have implications on national strategies for climate change mitigation and adaptation in other hotspots of tropical forests.

Assessment of changes in environmental factors in a tourism-oriented Island.

Tourism development has influenced industrial structure changes and has become a major driving force for China's new urbanization. However, the development will negatively impact natural resources and the ecological environment and will become an essential driving factor for land use change. Therefore, understanding the impact of tourism urbanization is crucial for sustainable local development. This study selected the Dachangshan Island in the Changhai County, Dalian, China, as the study area, because it is the only coastal island-type border county in China. During the study period, changes in local environmental factors were analyzed based on land use data, Landsat 5 and Landsat 8 data of 2009, 2014, and 2019. The results showed that: (1) the overall land surface temperature (LST) in the research region shows an increasing trend; the LST in 2014 and 2019 increased by 6.10 and 5.94 °C, respectively, compared with 2009. With respect to specific land types, impervious surfaces maintained a high land surface temperature (25.44, 32.38, and 31.86); however, surface temperatures for cropland, forest, grassland, and water bodies remained stable. (2) The land use land cover (LULC) change analysis from 2009-2019 indicates that impervious surfaces and cropland increased by 0.5653 km2 and 0.9941 km2, while the areas of forest, grassland, and water bodies decreased. The results also showed that forests (-1.3703 km2) are most affected by urbanization. (3) The results of the landscape index calculation showed that the variation at the patch scale is different for different LULC types. The patch density of impervious surfaces decreased, but the aggregation index increased over time, while the patch density of the forest increased continuously. At the landscape scale, overall patch type and distribution remained stable. The purpose of this study is to explore the environmental changes of islands and provide a reference for the sustainable development of islands.

Changes in area and number of nature reserves in China.

Nature reserves (NR) are the cornerstone of biodiversity conservation. Over the past 60 years, the rapid expansion of NRs in China, one of the world's megadiverse countries, has played a critical role in slowing biodiversity loss. We examined the changes in the number and area of China's NRs from 1956 to 2014 and analyzed the effect of economic development on the expansion of China's NRs from 2005 to 2014 with linear models. Despite a continuing increase in the number of NRs, the total area of China's NRs decreased by 3% from 2007 to 2014. This loss resulted from downsizing and degazettement of existing NRs and a slowdown in the establishment of new ones. Nature reserves in regions with rapid economic development exhibited a greater decrease in area, suggesting that downsizing and degazettement of NRs are closely related to the intensifying competition between economic growth and conservation. For example, boundary adjustments to national NRs, the most strictly protected NRs, along the coast of China's Yellow Sea, a global biodiversity hotspot with a fast-growing economy, resulted in the loss of one-third of the total area. One of the most important ecosystems in these NRs, tidal wetlands, decreased by 27.8% because of boundary adjustments and by 25.2% because of land reclamation. Our results suggest conservation achievement, in terms of both area and quality, are declining at least in some regions in the Chinese NR estate. Although the designation of protected areas that are primarily managed for sustainable use has increased rapidly in recent years in China, we propose that NRs with biodiversity conservation as their main function should not be replaced or weakened.

Cambios en la Superficie y el Número de Reservas Naturales en China Resumen Las reservas naturales (RN) son la piedra angular de la conservación de la biodiversidad. Durante los últimos 60 años, la rápida expansión de las RN en China, uno de los países megadiversos, ha jugado un papel crítico en la reducción de la pérdida de biodiversidad. Examinamos los cambios en el número y superficie de las RN en China de 1956 a 2014 y analizamos el efecto del desarrollo económico en la expansión de las RN en China de 2005 a 2014 mediante modelos lineales. A pesar del incremento continuo en el número de RN, la superficie total de RN en China decreció en 3% de 2007 a 2014. Esta pérdida resultó de la reducción y cambio de registro de RN existentes y una desaceleración en el establecimiento de RN nuevas. Las reservas naturales en regiones con desarrollo económico rápido presentaron una mayor disminución en la superficie, lo que sugiere que la reducción y cambio de registro de RN están relacionados cercanamente con la intensificación de la competencia entre crecimiento económico y conservación. Por ejemplo, ajustes en los límites de RN nacionales, las RN más estrictamente protegidas, a lo largo de la costa del Mar Amarillo, un sitio de importancia para la biodiversidad global con una economía en rápido crecimiento, resultó en la pérdida de un tercio de la superficie total. Uno de los ecosistemas más importantes en estas RN, humedales mareales, decreció en 27.8% debido a ajustes en los límites y en 25.2% debido a la reclamación de tierras. Nuestros resultados sugieren que los logros de conservación, en términos tanto de área como de calidad, están declinando en las RN de China. Aunque la designación de áreas protegidas administradas primariamente para un uso sustentable ha incrementado rápidamente en años recientes en China, proponemos que las RN cuya principal función es la conservación de la biodiversidad no deben ser reemplazadas o debilitadas.

Spatial analysis of dengue fever and exploration of its environmental and socio-economic risk factors using ordinary least squares: A case study in five districts of Guangzhou City, China, 2014.

OBJECTIVE: Spatial patterns and environmental and socio-economic risk factors of dengue fever have been studied widely on a coarse scale; however, there are few such quantitative studies on a fine scale. There is a need to investigate these factors on a fine scale for dengue fever. METHODS: In this study, a dataset of dengue fever cases and environmental and socio-economic factors was constructed at 1-km spatial resolution, in particular 'land types' (LT), obtained from the first high resolution remote sensing satellite launched from China (GF-1 satellite), and 'land surface temperature', obtained from moderate resolution imaging spectroradiometer (MODIS) images. Spatial analysis methods, including point density, average nearest neighbor, spatial autocorrelation, and hot spot analysis, were used to analyze spatial patterns of dengue fever. Spearman rank correlation and ordinary least squares (OLS) were used to explore associated environmental and socio-economic risk factors of dengue fever in five districts of Guangzhou City, China in 2014. RESULTS: A total of 30553 dengue fever cases were reported in the districts of Baiyun, Haizhu, Yuexiu, Liwan, and Tianhe of Guangzhou, China in 2014. Dengue fever cases showed strong seasonal variation. The cases from August to October accounted for 96.3% of the total cases in 2014. The top three districts for dengue fever morbidity were Baiyun (1.32%), Liwan (0.62%), and Haizhu (0.60%). Strong spatial clusters of dengue fever cases were observed. Areas of high density for dengue fever were located at the district junctions. The dengue fever outbreak was significantly correlated with LT, normalized difference water index (NDWI), land surface temperature of daytime (LSTD), land surface temperature of nighttime (LSTN), population density (PD), and gross domestic product (GDP) (correlation coefficients of 0.483, 0.456, 0.612, 0.699, 0.705, and 0.205, respectively). The OLS equation was built with dengue fever cases as the dependent variable and LT, LSTN, and PD as explanatory variables. The residuals were not spatially autocorrelated. The adjusted R-squared was 0.320. CONCLUSIONS: The findings of spatio-temporal patterns and risk factors of dengue fever can provide scientific information for public health practitioners to formulate targeted, strategic plans and implement effective public health prevention and control measures.

A 50-m forest cover map in Southeast Asia from ALOS/PALSAR and its application on forest fragmentation assessment.

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity.

Improving risk models for avian influenza: the role of intensive poultry farming and flooded land during the 2004 Thailand epidemic.

Since 1996 when Highly Pathogenic Avian Influenza type H5N1 first emerged in southern China, numerous studies sought risk factors and produced risk maps based on environmental and anthropogenic predictors. However little attention has been paid to the link between the level of intensification of poultry production and the risk of outbreak. This study revised H5N1 risk mapping in Central and Western Thailand during the second wave of the 2004 epidemic. Production structure was quantified using a disaggregation methodology based on the number of poultry per holding. Population densities of extensively- and intensively-raised ducks and chickens were derived both at the sub-district and at the village levels. LandSat images were used to derive another previously neglected potential predictor of HPAI H5N1 risk: the proportion of water in the landscape resulting from floods. We used Monte Carlo simulation of Boosted Regression Trees models of predictor variables to characterize the risk of HPAI H5N1. Maps of mean risk and uncertainty were derived both at the sub-district and the village levels. The overall accuracy of Boosted Regression Trees models was comparable to that of logistic regression approaches. The proportion of area flooded made the highest contribution to predicting the risk of outbreak, followed by the densities of intensively-raised ducks, extensively-raised ducks and human population. Our results showed that as little as 15% of flooded land in villages is sufficient to reach the maximum level of risk associated with this variable. The spatial pattern of predicted risk is similar to previous work: areas at risk are mainly located along the flood plain of the Chao Phraya river and to the south-east of Bangkok. Using high-resolution village-level poultry census data, rather than sub-district data, the spatial accuracy of predictions was enhanced to highlight local variations in risk. Such maps provide useful information to guide intervention.