Agricultural drought assessment at disaggregated level using AWiFS/WiFS data of Indian Remote Sensing satellites

Spatial differences in drought proneness and intensity of drought caused by differences in cropping patterns and crop growing environments within a district indicate the need for agricultural drought assessment at disaggregated level. The objective of this study is to use moderate resolution satellite images for detailed assessment of the agricultural drought situation at different administrative units (blocks) within a district. Monthly time composite NDVI images derived from moderate resolution AWiFS (60 m) and WiFS (180 m) images from Indian Remote Sensing satellites were analysed along with ground data on rainfall and crop sown areas for the kharif seasons (June – November) of 2002 (drought year), 2004 (early season drought) and 2005 (good monsoon year). The impact of the 2002 meteorological drought on crop area in different blocks of the district was assessed. The amplitude of crop condition variability in a severe drought year (2002) and a good year (2005) was used to map the degree of vulnerability of different blocks in the district to agricultural drought. The impact of early season deficit rainfall in 2004 on the agricultural situation and subsequent recovery of the agricultural situation was clearly shown. Agricultural drought assessment at disaggregated level using moderate resolution images is useful for prioritizing the problem areas within a district to undertake, in season drought management plans, such as alternate cropping strategies, as well as for end of the season drought relief management actions. The availability of ground data on rainfall, cropping pattern, crop calendar, irrigation, soil type etc., is very crucial in order to interpret the seasonal NDVI patterns at disaggregated level for drought assessment. The SWIR band of AWiFS sensor is a potential data source for assessing surface drought at the beginning of the season.     

GEE环境下的玉米低温冷害损失快速评估

大范围、及时、准确的灾害损失评估与制图对防灾减灾、农业保险和粮食安全等至关重要。针对传统灾害损失评估方法空间尺度单一、泛化能力差、时效性低,可操作性弱等问题,本文建立了一种遥感产品耦合作物模型的多尺度的灾害损失评估方法MDLA （a Multiscale Disaster Loss Assessment）。该方法利用作物模型的多情景模拟产生大量的灾害样本,结合对应日期的遥感指标构建灾害脆弱性模型,依托Google Earth Engine（GEE）平台将其应用到高分辨率遥感影像和格点灾害指标进行逐象元评估。以鄂伦春自治旗玉米为例,基于精细校准的CERES-Maize模型的模拟,利用两个生长季窗口的LAI和冷积温（CDD）建立统计模型来刻画低温对最终产量的影响,结合Sentinel-2数据逐格点计算完成高精度损失制图。结果显示,校准后的CERES-Maize模拟物候和产量的NRMSE 分别为3.3%和8.9%。冷害情景模拟结果表明不同类型和生育期的低温冷害对玉米产量的影响不尽相同,其中生长峰值期（出苗—吐丝和吐丝—灌浆）最为敏感。回代检验显示,MDLA方法估算精度为11.4%,与历史冷害年份的实际损失相吻合。经评估,鄂伦春2018-08-09的冷害导致玉米减产23.7%,受灾面积1.86×10⁴ ha,其中高海拔地区损失较重（减产率>25%）,低温冷害对该区玉米生产构成了严重的威胁。与现有的统计回归、作物模型模拟以及同化等技术相比,其优势在于：（1）结合遥感观测和作物模型模拟技术能更好地刻画了灾害对产量的影响过程;（2）利用GEE平台快速处理海量遥感数据,提高了灾害损失评估的时效性;（3）不受地面实测数据的限制,易操作,可实现动态、多尺度（象元、田块、村,县等）的损失评估,这为防灾减损、维持粮食丰产稳产提供了保障,也为农业保险的业务化运行提供了思路。     

Local-scale agricultural drought monitoring with satellite-based multi-sensor time-series

ABSTRACT

Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel 1) data. Several indicators were derived such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), Tasseled cap indices and Sentinel-1 based backscattering intensity and relative surface moisture. We used logistic regression to evaluate the drought-induced variability of remotely sensed parameters estimated for different phases of crop growth. The parameters with the highest prediction rate were further used to estimate thresholds for drought/non-drought classification. The models were evaluated using the area under the receiver operating characteristic curve and validated with in-situ data. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions. Growing season maximum NDVI and NDMI (70–75%) and SAR derived metrics (60%) reflect specifically the impact of agricultural drought. These metrics also depict stress affected areas with a larger spatial extent. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86% and 71%, respectively. The developed approach can be further used to assess crop condition and to support decision-making in areas which are more susceptible and vulnerable to drought.     

农业遥感研究应用进展与展望

得益于中国自主遥感卫星、无人机遥感和物联网等技术的发展,中国农业遥感研究与应用在过去20年取得了显著进步,中国农业遥感信息获取呈现出天地网一体化的趋势;农业定量遥感在关键参数遥感反演技术方法与应用方面取得进展;作物面积、长势、产量、灾害遥感监测的理论与技术方法取得突破,农业遥感技术应用领域不断拓展。本文从农业遥感信息获取、农业定量遥感、农业灾害遥感、作物遥感识别与制图、作物长势遥感监测与产量预测、农业土地资源遥感等方面对中国农业遥感科研与应用进行了总结综述。     

A simple assessment approach for winter wheat loss risk impacted by water stress

In this study, an empirical assessment approach for the risk of crop loss due to water stress was developed and used to evaluate the risk of winter wheat loss in China, the United States, Germany, France and the United Kingdom. We combined statistical and remote sensing data on crop yields with climate data and cropland distribution to model the effect of water stress from 1982 to 2011. The average value of winter wheat loss due to water stress for the three European countries was about ?931 kg/ha, which was higher than that in China (?570 kg/ha) and the United States (?367 kg/ha). Our study has important implications for the operational assessment of crop loss risk at a country or regional scale. Future studies should focus on using higher spatial resolution remote sensing data, combining actual evapotranspiration to estimate water stress, improving the method for downscaling of statistical crop yield data and establishing more sophisticated zoning methods.     

基于时间序列叶面积指数稀疏表示的作物种植区域提取

以华北平原黄河以北地区为研究区域,以时间序列叶面积指数LAI(Leaf Area Index)傅里叶变换的谐波特征作为不同作物识别的数据源,利用稀疏表示的分类方法识别2007年—2016年冬小麦、春玉米、夏玉米等主要农作物种植区域。首先利用上包络线Savitzky-Golay滤波分别对2007年—2016年的时间序列MODIS LAI曲线进行重构,进而对重构的年时间序列LAI进行傅里叶变换,以0—5级谐波振幅、1—5级谐波相位作为作物识别的依据,基于各类地物的训练样本,通过在线字典学习算法构建稀疏表示方法的判别字典,对每个待测样本利用正交匹配追踪算法求解稀疏系数,从而计算对应于各类地物的重构误差,根据最小重构误差判定待测样本的作物类型,并对作物识别结果的位置精度进行验证。结果表明,2007年—2016年作物识别的总体精度为77.97%,Kappa系数为0.74,表明本文提出的方法可以用于研究区域主要作物种植区域的提取。     

Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information

The accurate and timely information of crop area is vital for crop production and food security. In this study, the Enhanced Vegetation Index (EVI) data from MODerate resolution Imaging Spectroradiometer (MODIS) integrated crop phenological information was used to estimate the maize cultivated area over a large scale in Northeast China. The fine spatial resolution China’s Environment Satellite (HJ-1 satellite) images and the support vector machine (SVM) algorithm were employed to discriminate distribution of maize in the reference area. The mean MODIS–EVI time series curve of maize was extracted in the reference area by using multiple periods MODIS–EVI data. By analysing the temporal shift of crop calendars from northern to southern parts in Northeast China, the lag value was derived from phenological data of twenty-one agro-meteorological stations; here integrating with the mean MODIS–EVI time series image of maize, a standard MODIS–EVI time series image of maize was obtained in the whole study area. By calculating mean absolute distances (MAD) map between standard MODIS–EVI image and mean MODIS–EVI time series images, and setting appropriate thresholds in three provinces, the maize cultivated area was extracted in Northeast China. The results showed that the overall classification accuracy of maize cultivated area was approximately 79%. At the county level, the MODIS-derived maize cultivated area and statistical data were well correlated (R² = 0.82, RMSE = 283.98) over whole Northeast China. It demonstrated that MODIS–EVI time series data integrated with crop phenological information can be used to improve the extraction accuracy of crop cultivated area over a large scale.     

Monitoring Horizontal and Vertical Cropping Pattern and Dynamics in Bihar over a Decade (2001–2012) Based on Time-Series Satellite Data

Increasing population and natural disasters like drought, flood, cyclone etc., has impacted global agriculture area and hence continuously modifying cropping pattern and associated statistics. The present study analysed agriculture dynamics over one of the densely populated and disaster prone state (Bihar) in India and derived vital statistics (single, double and triple cropping area, and monthly, seasonal, annual and long term status at the state and district level) for the years 2001–2012. The study used time-series MODIS vegetation index (EVI; MOD13A2, 1 km, 16 day, 2001–2012), MODIS annual Land Cover product (MCD12Q1, 500 m, 2001–2012) and Global Land Cover map (Scasia_V4, 1 km, 2000; Globcover_V2.2, 300 m, 2005/2006 and V2.3, 2009, 300 m), and extracted horizontal (i.e., area change) and vertical (i.e., cropping intensification) agriculture change pattern. The results were inter-compared, and validated using government reports as well as with high spatial resolution data (IRS-LISS III 23.5 m). From 2001–2006 to 2007–2012, the net horizontal and vertical change in agriculture area is +145.24 and +907.82 km², respectively, and net change in seasonal crop area (winter, summer and monsoon) is +959.21, +1009.84 and ?1061.64 km², respectively. The districts which are located along the eastern part of Ganges experienced maximum positive changes and the districts along Gandak river in the north-western part of the study area experienced maximum negative changes. Overall, the study has quantified and revealed interesting space–time agriculture change patterns over 12 years including impacts caused by droughts and floods in the study area.     

A combined deficit index for regional agricultural drought assessment over semi-arid tract of India using geostationary meteorological satellite data

The untimely onset and uneven distribution of south-west monsoon rainfall lead to agricultural drought causing reduction in food-grain production with high vulnerability over semi-arid tract (SAT) of India. A combined deficit index (CDI) has been developed from tri-monthly sum of deficit in antecedent rainfall and deficit in monthly vegetation vigor with a lag period of one month between the two. The formulation of CDI used a core biophysical (e.g., NDVI) and a hydro-meteorological (e.g., rainfall) variables derived using observation from Indian geostationary satellites. The CDI was tested and evaluated in two drought years (2009 and 2012) within a span of five years (2009–2013) over SAT. The index was found to have good correlation (0.49–0.68) with standardized precipitation index (SPI) computed from rain-gauge measurements but showed lower correlation with anomaly in monthly land surface temperature (LST). Significant correlations were found between CDI and reduction in agricultural carbon productivity (0.67–0.83), evapotranspiration (0.64–0.73), agricultural grain yield (0.70–0.85). Inconsistent correlation between CDI and ET reduction was noticed in 2012 in contrast to consistent correlation between CDI and reduction in carbon productivity both in 2009 and 2012. The comparison of CDI-based drought-affected area with those from existing operational approach showed 75% overlapping regions though class-to-class matching was only 40–45%. The results demonstrated that CDI is a potential indicator for assessment of late-season regional agricultural drought based on lag-response between water supply and crop vigor.     

2001—2019年长江中下游农业干旱遥感监测及植被敏感性分析

长江中下游地区是中国最重要的粮食产区之一,近年来,由于极端天气影响,长江中下游地区的农业生产时常受到干旱灾害威胁。利用植被条件指数(vegetation condition index, VCI)、温度条件指数(temperature condition index, TCI)及植被健康指数(vegetation health index, VHI)对2001—2019年长江中下游地区农业干旱的时空演变情况进行监测,探究长江中下游地区VCI、TCI在VHI指数中的最优权重比例,挖掘不同植被对干旱的敏感性差异,同时基于气候变化背景分析长江中下游六省一市的干旱趋势。结果表明,VCI和TCI指数能够分别反映地区植被生长异常和热量异常;当VCI和TCI的权重分配比为7∶3时,VHI指数能够结合两种指数的特点,在长江中下游地区农业干旱监测上更有优势;不同植被对干旱的敏感性不同,在长江中下游地区,农作物对干旱的敏感性最高,森林最低,草地介于二者之间;在气候变化背景下,近20年来,长江中下游地区呈现逐渐湿润的趋势,干旱风险逐步降低,其中湖北、湖南、安徽、江西和浙江等地湿润趋势明显,而江苏和上海地区湿润趋势较弱,在极端气候下仍存在一定的干旱风险。相关结果能够为长江中下游地区各省市旱情预警及抗旱措施制定、区域农业生产管理提供参考。     

Mapping irrigated agriculture in complex landscapes using SPOT6 imagery and object-based image analysis – A case study in the Central Rift Valley,Ethiopia –

Irrigation infrastructure development for smallholder farmers in developing countries increasingly gains attention in the light of domestic food security and poverty alleviation. However, these complex landscapes with small cultivated plots pose a challenge with regard to mapping and monitoring irrigated agriculture. This study presents an object-based approach to map irrigated agriculture in an area in the Central Rift Valley in Ethiopia using SPOT6 imagery. The study is a proof-of-concept that the use of shape, texture, neighbour and location information next to spectral information is beneficial for the classification of irrigated agriculture. The underlying assumption is that the application of irrigation has a positive effect on crop growth throughout the field, following the field's borders, which is detectable in an object-based approach. The type of agricultural system was also mapped, distinguishing smallholder farming and modern large-scale agriculture. Irrigated agriculture was mapped with an overall accuracy of 94% and a kappa coefficient of 0.85. Producer's and user's accuracies were on average 90.6% and 84.2% respectively. The distinction between smallholder farming and large-scale agriculture was identified with an overall accuracy of 95% and a kappa coefficient of 0.88. The classifications were performed at the field level, since the segmentation was able to adequately delineate individual fields. The additional use of object features proved essential for the identification of cropland plots, irrigation period and type of agricultural system. This method is independent of expert knowledge on crop phenology and absolute spectral values. The proposed method is useful for the assessment of spatio-temporal dynamics of irrigated (smallholder) agriculture in complex landscapes and yields a basis for land and water managers on agricultural water use.     

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia’s cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation (R² > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships (R² < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation (R²) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.     

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

ABSTRACT

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.     

全球农情遥感速报系统20年

面向国家粮食安全的重大战略需求,1998年中国科学院建立了"中国农情遥感速报系统"(CropWatch),持续运行20年后,现已发展成为"全球农情遥感速报系统"(CropWatch)。本文重点论述了2013年建立参与式全球农情遥感监测云平台(CropWatch-Cloud)以来,所采用的农情监测体系、可定制的农情监测云平台理念以及CropWatch-Cloud在国内外的应用推广情况,介绍了技术方法与农情信息服务方式的创新与进步带来的国际影响力的提升。系统总结了全球农情遥感速报系统发展的农情监测指标、农情预警能力、作物长势综合监测方法以及众源数据支持的作物面积监测方法,论文进一步阐述了CropWatch未来的发展方向,借助众源地理信息、大数据技术等的发展,打通从地块—村—镇—县—市—省—国家—全球的体系化全链条监测,满足从农户到政府决策部门对农情信息的差异化需求。     

基于ArcGIS的中国粮食播种面积空间格局变化及其影响因素分析

粮食播种面积的变化及空间分布特征对农业的可持续发展以及粮食安全有着重要影响。本文采用GIS空间分析方法,以1949—2013年中国粮食播种面积变化为研究对象进行相关探讨。研究结果表明:中国粮食播种面积在近十年来重心北移十分明显;粮食作物中的豆类和薯类播种面积在1997—2013年呈现典型聚类分布特征;耕地减少对粮食播种面积的作用逐渐减弱,粮作比和复种指数成为影响粮食播种面积增长的主要因子;粮作比主要受农田基础设施建设、人口城镇化、农户种植习惯影响,与之呈现显著相关关系。     

Land suitability analysis for cereal production in Himachal Pradesh (India) using Geographical Information System

Land suitability analysis is prerequisite for sustainable agriculture and it plays a pivotal role in the niche based agricultural planning in mountain regions. In this paper different parameters viz. climatic (precipitation and temperature), topographic (elevation), soil type and land cover/land use have been used in order to perform land suitability evaluation for cereals food-grain crops in Himachal Pradesh using Geographic Information System (GIS). The suitability analysis was performed by digital processing of geo-referenced data (elevation, climate, soil and landcover) and calculating potential production areas by combining different types of geographical data through decision rules framed for each crop in ArcView spatial analyst. Suitable areas have been delineated for cereal crops in the form of land suitability maps. In comparison to the actual area under cereal crops, the possibility of further expansion under each cereal crop was determined. These discriminated areas appear suitable for growing these crops and can be harnessed efficiently for achieving long term sustainability and food security.     

空间信息技术在农业保险中的应用研究

中国是农业灾害发生频繁且灾情严重的国家之一。农业保险已成为农民灾后恢复生产和灾区重建的重要资金来源,风险保障和经济补偿作用日益凸显。但是,农业保险面临着信息不对称和经营成本高等问题。而利用3S等空间信息技术,可将保险标的空间化,建立承保标的空间数据库,为承保和理赔工作提供空间数据和分析管理支持,实现＂按图承保＂和＂按图理赔＂,以空间信息技术支持的农业保险创新应用促进了农业保险模式的转变,从而有效地解决农业保险存在的信息不对称和经营成本高等问题,充分发挥农业保险支农惠农的社会管理职能,提升农业保险的风险管理水平,以更好地服务＂三农＂。     

Remote sensing based agricultural drought assessment in Palar basin of Tamil Nadu state,India

Agricultural drought has been a recurrent phenomenon in many parts of India. Remote sensing plays a vital role in real time monitoring of the agricultural drought conditions over large area, there by effectively supplementing the ground mechanism. Conventional drought monitoring is based on subjective data. The satellite based monitoring such as National Agricultural Drought Assessment and Monitoring System (NADAMS) is based on the crop condition, which is an integrated effect of soil, effective rainfall, weather, etc. Drought causes changes in the external appearance of vegetation, which can clearly be identified (by their changed spectral response) and judged using satellite sensors through the use of vegetation indices. These indices are functions of rate of growth of the plants and are sensitive to the changes of moisture stress in vegetation. The satellite based drought assessment methodology was developed based on relationship obtained between previous year’s Normalised Difference Vegetation Index (NDVI) profiles with corresponding agricultural performance available at district/block level. Palar basin, one of the major river basins in Tamil Nadu state was selected as the study area. The basin covers 3 districts, which contain 44 blocks. Wide Image Field Sensor (WiFS) of 188m spatial resolution from Indian Remote Sensing Satellite (IRS) data was used for the analysis. Satellite based vegetation index NDVI, was generated for Samba and Navarai seasons in the years 1998 and 1999. An attempt has been made to estimate the area under paddy. It was also observed that, there was reduction in the crop area as well as vigour in the vegetation in both Samba and Navarai seasons in 1999 when compared with 1998. Drought severity maps were prepared in GIS environment giving blockwise agricultural water deficiency status.     

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.     

社区减灾能力的熵权-灰靶评价方法研究——以苏州新区为例

社区是城市细胞和基层单位,其防灾减灾能力建设对构建城市安全体系有着极其重要的作用。本文借助于熵权-灰靶模型和GIS叠置分析技术进行社区减灾能力综合评价方法研究,首先从灾害风险评估能力、救援与保障能力等6个方面构建起包含30个二级指标的社区减灾能力评价指标体系。经过指标序列的影响空间和标准模式的构建、灰靶变换和靶心度分级,进行社区减灾能力分级评价。以苏州新区作为案例分析研究区,借助于ArcMap10.2软件得到该区域的社区减灾能力空间分布特征图。分析结果表明,研究区社区减灾能力总体上呈现东区较好,西区较弱,区域内社区减灾能力建设不平衡特征。研究区各街道和社区的灾害风险评估能力和灾害管理能力较好,工程防御能力总体分布不均衡,经济基础支撑能力、救援与保障能力、公众认知能力较差,说明社区综合减灾能力不足,今后应从单纯依赖减灾示范社区建设转变为加强社区综合减灾能力的内涵建设。