Land use legacies and nitrogen fertilization affect methane emissions in the early years of rice field development

Methane (CH₄) emissions are critical to greenhouse gas (GHG) management in agriculture, especially in areas growing rice (Oryza sativa). However, studies on CH₄ emissions and the nitrogen (N) fertilization effect in new rice fields in subtropical regions are still scarce. In this study, we designed a split-plot field experiment in Jiangxi Province, southern China, to examine whether land-use legacies and N fertilization would influence CH₄ emissions. Using static chambers and gas chromatography, we measured CH₄ fluxes in a newly developed rice paddy and a 10-year-old rice paddy. We also measured climatic factors and soil chemical and physical properties to match the flux measurements. The results showed that annual CH₄ emissions in the new rice plots were significantly lower than in the old rice plots regardless of N fertilization. Annual CH₄ emissions increased with the land-use years of rice paddies, following the order of 1 year < 2 years < 3 years < 10 years. N fertilization significantly decreased CH₄ emissions by 36.9% in the first year after the new rice plots were developed, whereas it had no significant effects on CH₄ emissions in the old rice plots or the new rice plots in the second and third years. The results suggest that land-use legacies have significant effects on CH₄ emissions and may influence the N fertilization effect on CH₄ emissions in rice fields in subtropical regions. The findings suggest that land-use legacies should be considered in managing and estimating GHG emissions in rice-growing regions.     

黄河下游引黄灌区主要河系硝态氮及铵态氮安全评价

以德州引黄灌区为例,探讨灌区主要水系部分水化学特征及硝态氮(NO-3-N)与铵态氮(NH+4-N)污染状况。结果表明,pH、电导率(EC)、NO-3-N及NH+4-N含量存在明显的空间差异。NO-3-N受人类活动影响,灌区北部卫运河-漳卫新河以及德惠新河污染严重,呈带状分布;而马颊河及徒骇河受引黄水补给的影响范围较广,污染程度相对较低。NH+4-N污染除卫运河外,主要呈点源分布,各水系均有超标河段,而影响NH+4-N的可能因素更多,有待进一步深入研究。本研究为评价该区引黄灌渠对NO-3-N和NH+4-N的影响提供科学依据。     

Long-term monitoring of citrus orchard dynamics using time-series Landsat data: a case study in southern China

ABSTRACT

Citrus orchard planting is a typical land-use change process that can impact terrestrial ecosystem services both locally and globally. Long-term monitoring of citrus orchard dynamics is critical for understanding its change patterns as well as the potential driving factors. Satellite remote-sensing imagery has been a primary data source for this purpose. However, most previous studies with multi-year intervals only captured some, but not all detailed information on citrus orchard expansion. In this study, we developed a framework for mapping annual citrus orchard extent and track its long-term dynamics in Xunwu County, China, using the historical Landsat repository from 1990 to 2016. The results suggested that the average overall accuracy of original annual mapping was 87.73%, and its performance was significantly improved after the temporal filtering approach (91.46%). Several features (e.g. elevation, slope, normalized difference vegetation index) played more important roles in citrus orchard identification. With the achieved annual mapping layers, we found a rapid citrus orchard expansion trend during the study interval (i.e. from 22.18 to 697.21 km²). Moreover, this expansion process was unevenly distributed in time. Spatially, emerging citrus orchards were primarily transformed from forests and croplands and mainly distributed in areas with elevations from 200 to 500 m and slopes range from 5° to 20°. This study demonstrated the potential of mapping citrus orchard dynamics at a higher temporal frequency with remote-sensing time-series, which can contribute to providing reference for sustainable land-use policy.     

Modeling Effects of Land use and Vegetation Density on Soil Water Dynamics: Implications on Water Resource Management

Vegetation restoration is helpful in preventing soil erosion but aggravates water scarcity, thus resulting in soil desiccation on the wind–water erosion crisscross region (WWECR) of the Loess Plateau in Northwestern China. However, no guideline currently exists on the selection of plant species and density for restoration purposes. Based on the process model of soil water-carrying capacity for vegetation (SWCCV) originally developed in this region, this study validated the model under a broad range of weather regimes, soil types, and land uses. The SWCCV model was applied as a diagnostic tool to obtain insights into the separate effects of vegetation density and land use on soil water dynamics on the WWECR of the Loess Plateau. Results showed that the total water loss at semi-natural grasslands was close to rainfall while significantly decreasing runoffs, thus indicating that semi-natural grass was suitable for vegetation restoration on the WWECR. If Caragana korshinskii is planted for agronomic and economic benefits, a density of 3,400 trees ha^?1 will yield the most optimal soil water conservation benefits at study site. Our recommended land use and vegetation density were directive and instructional for vegetation restoration on the WWECR and that our modeling approach could be extended to applications in other arid and semi-arid regions.     

Particle swarm optimization based on dynamic niche technology with applications to conceptual design

Based on the standard particle swarm optimization (PSO) algorithm together with the widely used dynamic niche technology, this paper presents a new variation combined with the dynamic niche sharing technique on the basis of traditional PSO algorithm. We proposed a cooperative particle swarm optimization model with cooperative multi-population. Applications are given on creative conceptual architectural design.     

Fuzzy risk assessment modelling of East Kolkata Wetland Area: A remote sensing and GIS based approach

East Kolkata Wetland Area (EKWA), a Ramsar site, is suffering from long-term resource exploitation and land-use change. This paper presents a Fuzzy-based Risk Assessment Model (FRAM) using Remote Sensing and GIS tools to identify the areas with varying intensity of wetland conversion risk within EKWA. Model was developed through rule-based Fuzzy Inference System. ‘Wetlands conversion rate’, ‘Canal proximity’, ‘Road proximity’, ‘Population density’, ‘Population growth rate’, ‘Infrastructure status’, ‘Livelihood status’, and ‘Social status’ were the eight selected input indices for the model.The model was validated at two stages. For stage one, Fuzzy c-Means (FCM) classified satellite data for 2012 and 2014 were compared to detect the landuse change. In stage two, model relevance over thirteen study sites was studied through questionnaire-based field survey. It was found that FRAM has efficiently modelled and mapped the various levels of wetland risk zones of EKWA.