Estimating agricultural production in marginal and food insecure areas in Kenya using very high resolution remotely sensed imagery

Regularly monitoring the amount of food produced in food insecure, isolated, subsistence farming areas can be used to help identify households or communities who may be in need of additional food resources. Measuring seasonal food production in developing countries, particularly at a sub-national level, is complicated by lack of data. In this study we use high resolution remotely sensed data to calculate cultivated area in two different growing areas, during two different seasons in Kenya. The results of the research support the usefulness of this approach for agricultural monitoring in the developing world and suggest that monitoring cultivated area requires attention to the specific growing characteristics of an area.     

辽西大黑山生态修复区土地利用格局变化及驱动力

生态修复区土地利用与覆盖变化是区域环境变化研究的重要内容,可为水土保持生态修复措施的调控及效益定量评价提供依据。以辽西大黑山生态修复区2000年、2005年和2010年的Landsat TM遥感影像为基础数据,基于Erdas Imagine遥感软件和ArcGIS技术平台,利用土地利用动态度、土地利用程度综合指数和转移矩阵等指标,对研究区土地利用格局的动态变化进行了定量分析,并探讨了其变化的驱动力因素。结果表明:1.10 a间,耕地保持面积最大,其次是林地和草地;耕地和未利用土地占总面积比例分别减少了28.43%和44.32%,林地、草地、城镇村及工矿交通、水域及水利设施用地占总面积比例分别增加了83.68%、10.75%、21.07%和162.74%。2.2000—2005年,林地的土地利用变化幅度最大,为12.42%,其次为未利用土地和草地;2005—2010年,水域及水利设施用地的土地利用变化幅度最大,为24.82%,其次是草地、未利用土地和耕地。3.研究区土地利用程度和开发强度处于中等偏上水平,2000—2005年土地利用处于发展期,2005—2010年土地利用处于调整期;土地利用类型的转入和转出主要发生在耕地、林地和草地之间。4.水土保持生态修复和退耕还林等生态建设项目的实施,可以较大程度地改变微地貌、减少地表坡度、提高植被盖度,在短时间使土地利用的空间分布发生较大变化,是其土地利用格局变化的主要驱动力因素。     

复杂地形区陆面资料对WRF模式模拟性能的影响

本文利用WRF（Weather Research and Forecasting）模式耦合Noah陆面过程模式,对比研究了使用不同精度陆面资料:WRF默认陆面资料、中国1 km分辨率数字高程模型数据集、2006年MODIS（MODerate-resolution Imaging Spectroradiometer）土地利用和植被覆盖度资料,WRF模式对兰州地区冬季气象场模拟结果的差异。结果表明,近地面气温对陆面资料的精度非常敏感,而风场对陆面资料的精度不敏感,WRF模式对气温的模拟效果好于对风场模拟。采用高精度且时效性好的陆面资料后,WRF模拟的近地面气温准确率提高了15.8%,模拟的夜间气温改进幅度较白天大。陆面资料可影响整个边界层温度场分布,准确的陆面资料对提高WRF模式模拟近地面乃至整个边界层气象场至关重要。尽管风速模拟误差较大,但总体上WRF模式能较准确地模拟出研究区的风场演变特征。使用新的陆面资料后WRF模拟的风速误差略有减小,风向误差略有增加。干旱半干旱区冬季数值模拟需要注意土壤湿度初值和模式初始积分时刻对模拟结果的影响。     

Combining CLUE-S and SWAT models to forecast land use change and non-point source pollution impact at a watershed scale in Liaoning Province,China

Non-point source （NPS） pollution has become a major source of water pollution. A combination of models would provide the necessary direction and approaches designed to control NPS pollution through land use planning. In this study, NPS pollution load was simulated in urban planning, historic trends and ecological protection land use scenarios based on the Conversion of Land Use and its Effect at Small regional extent （CLUE-S） and Soil and Water Assessment Tool （SWAT） models applied to Hunhe-Taizi River Watershed, Liaoning Province, China. Total nitrogen （TN） and total phosphorus （TP） were chosen as NPS pollution indices. The results of models validation showed that CLUE-S and SWAT models were suitable in the study area. NPS pollution mainly came from dry farmland, paddy, rural and urban areas. The spatial distribution of TN and TP exhibited the same trend in 57 sub-catchments. The TN and TP had the highest NPS pollution load in the western and central plains, which concentrated the urban area and farm land. The NPS pollution load would increase in the urban planning and historic trends scenarios, and would be even higher in the urban planning scenario. How- ever, the NPS pollution load decreased in the ecological protection scenario. The differences observed in the three scenarios indicated that land use had a degree of impact on NPS pollution, which showed that scientific and ecologically sound construction could effec- tively reduce the NPS pollution load in a watershed. This study provides a scientific method for conducting NPS pollution research at the watershed scale, a scientific basis for non-point source pollution control, and a reference for related policy making.     

Soil respiration mapped by exclusively use of MODIS data for forest landscapes of Saskatchewan,Canada

Soil respiration (R_s) is of great importance to the global carbon balance. Remote sensing of R_s is challenging because of (1) the lack of long-term R_s data for model development and (2) limited knowledge of using satellite-based products to estimate R_s. Using 8-years (2002–2009) of continuous R_s measurements with nonsteady-state automated chamber systems at a Canadian boreal black spruce stand (SK-OBS), we found that R_s was strongly correlated with the product of the normalized difference vegetation index (NDVI) and the nighttime land surface temperature (LSTn) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The coefficients of the linear regression equation of this correlation between R_s and NDVI × LSTn could be further calibrated using the MODIS leaf area index (LAI) product, resulting in an algorithm that is driven solely by remote sensing observations. Modeled R_s closely tracked the seasonal patterns of measured R_s and explained 74–92% of the variance in R_s with a root mean square error (RMSE) less than 1.0 g C/m²/d. Further validation of the model from SK-OBS site at another two independent sites (SK-OA and SK-OJP, old aspen and old jack pine, respectively) showed that the algorithm can produce good estimates of R_s with an overall R² of 0.78 (p < 0.001) for data of these two sites. Consequently, we mapped R_s of forest landscapes of Saskatchewan using entirely MODIS observations for 2003 and spatial and temporal patterns of R_s were well modeled. These results point to a strong relationship between the soil respiratory process and canopy photosynthesis as indicated from the greenness index (i.e., NDVI), thereby implying the potential of remote sensing data for detecting variations in R_s. A combination of both biological and environmental variables estimated from remote sensing in this analysis may be valuable in future investigations of spatial and temporal characteristics of R_s.     

Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data

Land cover classification of finer resolution remote sensing data is always difficult to acquire high-frequency time series data which contains temporal features for improving classification accuracy. This paper proposed a method of land cover classification with finer resolution remote sensing data integrating temporal features extracted from time series coarser resolution data. The coarser resolution vegetation index data is first fused with finer resolution data to obtain time series finer resolution data. Temporal features are extracted from the fused data and added to improve classification accuracy. The result indicates that temporal features extracted from coarser resolution data have significant effect on improving classification accuracy of finer resolution data, especially for vegetation types. The overall classification accuracy is significantly improved approximately 4% from 90.4% to 94.6% and 89.0% to 93.7% for using Landsat 8 and Landsat 5 data, respectively. The user and producer accuracies for all land cover types have been improved.     

Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation

The urban heat island (UHI) refers to the phenomenon of higher atmospheric and surface temperatures occurring in urban areas than in the surrounding rural areas. Mitigation of the UHI effects via the configuration of green spaces and sustainable design of urban environments has become an issue of increasing concern under changing climate. In this paper, the effects of the composition and configuration of green space on land surface temperatures (LST) were explored using landscape metrics including percentage of landscape (PLAND), edge density (ED) and patch density (PD). An oasis city of Aksu in Northwestern China was used as a case study. The metrics were calculated by moving window method based on a green space map derived from Landsat Thematic Mapper (TM) imagery, and LST data were retrieved from Landsat TM thermal band. A normalized mutual information measure was employed to investigate the relationship between LST and the spatial pattern of green space. The results showed that while the PLAND is the most important variable that elicits LST dynamics, spatial configuration of green space also has significant effect on LST. Though, the highest normalized mutual information measure was with the PLAND (0.71), it was found that the ED and PD combination is the most deterministic factors of LST than the unique effects of a single variable or the joint effects of PLAND and PD or PLAND and ED. Normalized mutual information measure estimations between LST and PLAND and ED, PLAND and PD and ED and PD were 0.7679, 0.7650 and 0.7832, respectively. A combination of the three factors PLAND, PD and ED explained much of the variance of LST with a normalized mutual information measure of 0.8694. Results from this study can expand our understanding of the relationship between LST and street trees and vegetation, and provide insights for sustainable urban planning and management under changing climate.     

Assessing integration of intensity,polarimetric scattering,interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification

Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type.     

Food choices,health and environment: Effects of cutting Europe's meat and dairy intake

Western diets are characterised by a high intake of meat, dairy products and eggs, causing an intake of saturated fat and red meat in quantities that exceed dietary recommendations. The associated livestock production requires large areas of land and lead to high nitrogen and greenhouse gas emission levels. Although several studies have examined the potential impact of dietary changes on greenhouse gas emissions and land use, those on health, the agricultural system and other environmental aspects (such as nitrogen emissions) have only been studied to a limited extent. By using biophysical models and methods, we examined the large-scale consequences in the European Union of replacing 25–50% of animal-derived foods with plant-based foods on a dietary energy basis, assuming corresponding changes in production. We tested the effects of these alternative diets and found that halving the consumption of meat, dairy products and eggs in the European Union would achieve a 40% reduction in nitrogen emissions, 25–40% reduction in greenhouse gas emissions and 23% per capita less use of cropland for food production. In addition, the dietary changes would also lower health risks. The European Union would become a net exporter of cereals, while the use of soymeal would be reduced by 75%. The nitrogen use efficiency (NUE) of the food system would increase from the current 18% to between 41% and 47%, depending on choices made regarding land use. As agriculture is the major source of nitrogen pollution, this is expected to result in a significant improvement in both air and water quality in the EU. The resulting 40% reduction in the intake of saturated fat would lead to a reduction in cardiovascular mortality. These diet-led changes in food production patterns would have a large economic impact on livestock farmers and associated supply-chain actors, such as the feed industry and meat-processing sector.     

渤海西岸全新世气候演化与海陆变迁

应用第四纪地质学和海相生物等化石数量统计,以天津马棚口海岸带BQ1孔化石鉴定与统计结果,对古气候、古事件进行对比研究。通过岩芯编录,在岩性层中划分出4个海相层、5个过渡层、9个海面波动。结合研究区广泛分布的贝壳堤和牡蛎礁,利用其大量的测年资料,重现了6道海岸线;根据海相化石个体统计数,画出全新世气候演化曲线,与孢粉和物候所画的古气候演化曲线大体一致。研究历代海陆变迁,必然涉及到历史上长期争论不休的西汉大海侵等问题,谭其骧（1965）圈定的大海侵范围、海岸线分布以及考古结果几乎都能联系在一起,因此,大海侵之谜终于尘埃落定。考古界对河北平原自东汉—金的空白也有了初步答案,同时,发现中国朝代兴衰与气候冷暖有明显的对应,从而建立了气候分期。