Paradigm and ecological implication of changing agricultural land-use: A case study from Govind Wildlife Sanctuary, Central Himalaya, India

The paper scrutinizes that the changes in any sub-system(i.e.agriculture,livestock and forest) have direct impact on biophysical and social processes in village ecosystem of the central Himalayan region.In view of this,we studied the changes in spatial patterns of agricultural land use and dependency of agroecosystem on forest and animal husbandry over a period of two decades.Based on data analysis it was found that the cultivation of some traditional crops has either been abandoned in the area or declined by 25%-85% due to introduction of cash crops viz.,potato,kidney bean and apple farming with acreage increased up to 51%-72% in the last three decades.Livestock population of different categories has declined drastically by 17%-75%,and has resulted shortage of farmyard manure,deterioration of soil quality and fertility which leads to un-sustainability of agriculture system.The changes in agrobiodiversity have led to the dramatic increase in soil loss and runoff from the croplands together with the increase pressure on forests.The economic evaluation of each crop showed higher monetary benefit from cash crops as compared to traditional crops.Among all the evaluated crops,the monetary output/input ratio was found highest(3.04) for kidney bean and lowest(1.26) for paddy.Changes in land use and management have improved household income but at the cost of forest degradation,less productive animal husbandry and loss of agrodiversity in the region.Therefore,there is an urgent need to bring desirable changes in agricultural policy,research,land use and efficient management of the resources for maintaining sustainability in agro and Himalayan forest ecosystem.     

A Modeling Study of Climate Change and Its Implication for Agriculture in China Part II: The Implication of Climate Change for Agriculture in China

The potential CO2-induced impacts on the geographical shifts of wheat growth zones in China were studied from seven GCMs outputs. The wheat growth regions may move northward and westward under the condition of a doubling CO2 climate. The wheat cultivation features and variety types may also assume significant changes. Climatic warming would have a positive influence in Northeast China, but high temperature stress may be produced in some regions of central and southern China. Higher mean air temperatures during wheat growth, particularly during the reproductive stages, may increase the need for earlier-maturing and more heat-tolerant cultivars.     

淮安一次雾霾过程的污染物变化特征及来源分析

2012年6月9-11日淮安及周边地区出现了一次严重雾霾天气,PM2.5、NOx、CO、SO2等大气污染物浓度急剧升高.本文综合利用地面空气污染监测资料、气象观测资料,分析了该次雾霾过程中污染物的变化特征和影响因素,并对污染物的来源进行了探讨.结果发现：在这次连续雾霾过程中,SO2的质量浓度都在0.049 mg/m3以下;NOx、PM2.5、CO的质量浓度与能见度呈反相关,并在整个雾霾过程中出现两个峰值,均出现在能见度低于1 000m的雾过程中;在雾霾天气过程中,中低层一直维持一个逆温层结,其中四个时次中逆温层底在975 hPa,逆温层顶在925 hPa附近,这种稳定的大气垂直结构不利于大气湍流、水汽的垂直交换以及污染物的垂直扩散,为雾霾的长时间维持创造了良好的热力条件.整个雾霾过程中,近地层始终维持偏东风向,能见度在风速下降的时候随之下降,在风速增大的时候随之增大.应用轨迹模式分析表明,有利的天气条件和大面积秸秆燃烧的结合是造成这次淮河下游连续雾霾过程发生的主要原因.     

Images difference of ASAR data for rice crop mapping in Fuzhou,China

This paper introduces ENVISAT ASAR data application on rice field mapping in the Fuzhou area, using multi-temporal ASAR dual polarization data acquired in 2005. The procedure for ASAR data processing here includes data calibration, image registration, speckle reduction and conversion of data format from amplitude to dB for backscatter. The backscatter of rice increases with the rice growing stages, which was much different from other land covers. Based on image difference techniques, 6 schemes were designed with ASAR different temporal and polarization data for rice field mapping. Difference images between images in the early period of rice crop and growing or ripening period, are more suitable for rice extraction than those difference images between different polarizations in the same date. The most accurate result of late rice extraction was achieved based on the difference of HH polarization data acquired in October and August. Therefore, for rice field mapping, the temporal information is more important than polarization information. The data during the early growing season of rice is very important for high accuracy rice mapping.     

Analyzing the impact of thermal stress on vegetation health and agricultural drought – a case study from Gujarat,India

Although poor precipitation due to delayed arrival and/or early retreat of the southwest monsoon is considered the chief architect of drought in India, heat waves may also play a crucial role in the intensification of droughts. In the Indian subcontinent, occurrence of heat waves during the pre-monsoon and high air-temperature in the subsequent monsoon season imparts thermal stress on vegetation causing degradation of vegetation health (VH). In the present study, various vegetation indices and land-use/land-cover data derived from multi-sensor satellite have been used to assess VH and agricultural drought in Gujarat during 1981–2010. This Geographical Information Systems-based study has also used heat wave and temperature data to analyze the adverse effects of high temperature on VH. The time series of Vegetation Condition Index and Temperature Condition Index (TCI) has shown that the combined influence of moisture-stress and thermal stress determines the occurrence and severity of drought, which is reflected in the Vegetation Health Index (VHI). A strong correlation among aboveground air-temperature, the TCI and the VHI indicates definite influence of thermal stress on VH. Further, a systematic variation and strong resemblance between temperature, crop yield, TCI and VHI has established the impact of thermal stress on agricultural productivity.     

THE VARIATION FEATURES OF AGROCLIMATIC RESOURCES AND CROP YIELD OF CHINA IN RECENT 40 YEARS

ＴＨＥＶＡＲＩＡＴＩＯＮＦＥＡＴＵＲＥＳＯＦＡＧＲＯＣＬＩＭＡＴＩＣＲＥＳＯＵＲＣＥＳＡＮＤＣＲＯＰＹＩＥＬＤＯＦＣＨＩＮＡＩＮＲＥＣＥＮＴ４０ＹＥＡＲＳＧａｏＳｕｈｕａ（高素华）ａｎｄＺｈａｎｇＹｕ（张宇）ＲｅｓｅａｒｃｈＣｅｎｔｅｒｆｏｒＡｇｒｉ...     

甘肃武威市作物气候─经济优势分析及种植结构调整方案

通过建立增产性能、稳产性能、重要性能、经济性能、抗旱性能 5个评价指标的隶属函数 ,运用模糊数学方法计算了武威市主要种植作物的气候经济优势 ,得出了在武威市目前社会、自然和经济条件下最具优势的 4种种植作物依次为玉米、甜菜、油菜和小麦。根据气候经济优势度值对现有种植结构提出了调整方案     

基于时序遥感数据的农作物种植制度研究进展与展望

及时准确掌握农作物种植制度时空分布信息,对于确保国家粮食安全与农业结构合理具有重要意义。随着时序遥感影像质量的不断提高,基于时序遥感数据的农作物种植制度研究备受关注。本文从研究框架、遥感特征参数以及数据产品等角度,分析了基于时序遥感数据的农作物种植制度最新研究进展。研究发现:(1)前农作物种植制度研究框架,主要包括耕地复种指数和农作物制图等相关内容,其问题在于需要高质量耕地分布数据支撑以及易将热带亚热带湿润区撂荒地误判为农作物等;(2)于红边和短波红外的新型多维度光谱指数,有助于更好地揭示农作物生长发育过程,大尺度农作物时序遥感制图取得了系列研究成果,但需要应对不同作物光谱差异细微、同种作物在不同区域和年份存在明显类内异质性的挑战;(3)尺度中高分辨率耕地复种指数产品不断丰富,但其时效性和时空连续性有待加强;(4)欧美少数国家外,目前农作物分布数据产品覆盖的作物类型有限,我国大尺度农作物种植制度数据产品欠缺,特别是复杂多熟制农业区。随着多源遥感数据时空谱分辨率的不断提高以及云计算平台性能的不断发展,我们对以下方面进行了研究展望:(1)新研究框架,建立直接提取耕作区、农作物种植模式的农作...     

Crop type mapping using LiDAR,Sentinel-2 and aerial imagery with machine learning algorithms

LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field - often informed by data collected during ground and aerial surveys. However, manual digitizing and labeling is time-consuming, expensive and subject to human error. Automated remote sensing methods is a cost-effective alternative, with machine learning gaining popularity for classifying crop types. This study evaluated the use of LiDAR data, Sentinel-2 imagery, aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area. Different combinations of the three datasets were evaluated along with ten machine learning. The classification results were interpreted by comparing overall accuracies, kappa, standard deviation and f-score. It was found that LiDAR data successfully differentiated between different crop types, with XGBoost providing the highest overall accuracy of 87.8％. Furthermore, the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data, with LiDAR obtaining a mean overall accuracy of 84.3％ and Sentinel-2 a mean overall accuracy of 83.6％. However, the combination of all three datasets proved to be the most effective at differentiating between the crop types, with RF providing the highest overall accuracy of 94.4％. These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.     

Crop classification using MODIS NDVI data denoised by wavelet: A case study in Hebei Plain, China

Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-...