遥感定量反演华北平原地区冬小麦含水量

针对现有植被水分反演算法在华北平原地区适用性差、反演精度低、不能实施有效监测的问题,该文基于地面实测冬小麦植被含水量(VWC)数据,基于归一化型和比值型植被水分指数这两种常见的指数类型,提出调节植被水分指数以削弱土壤背景的影响,使用多个波段反射率数据反演VWC,提高拟合精度80%以上,发展适用于华北平原的农作物水分含量反演模型。拟合冬小麦植被含水量的决定系数为0.51,均方根误差为0.95(kg·m^-2)。结果表明:调节植被水分指数能够削弱土壤背景影响,大幅度提高植被水分反演精度;同一种指数计算形式中,在水汽吸收谷内,基于更长波段反射率的植被水分指数反演精度更高;归一化型和比值型植被水分指数在反演精度方面无明显优劣,归一化型植被水分指数反演精度。     

受蚜虫危害与干旱胁迫的冬小麦高光谱判别

从高光谱遥感角度判别冬小麦旱害和蚜虫危害,可进一步提高遥感监测灾害的准确性.在麦长管蚜的自然危害下,通过控制其生育期水分条件形成的不同程度的干旱胁迫,监测了灌浆末期冬小麦冠层反射率对蚜虫危害和干旱胁迫的反应;并经一阶微分数据变换,筛选出识别蚜虫虫害和干旱胁迫响应最敏感的光谱波段.实验结果表明:受蚜虫危害和干旱胁迫后,灌浆末期冬小麦在近红外波段的光谱特征变化比在可见光波段的显著,可见光和近红外波段是识别蚜虫危害和干旱胁迫最敏感的谱段.经一阶微分数据变换发现,自然降水处理(灌水量相当于需水量的＜40％)下的冬小麦光谱曲线的“红边”斜率最小;受蚜虫危害以及灌水量分别相当于需水量的＞70％,60％～ 70％,50％～ 60％和40％～ 50％水分处理下的“红边”斜率依次变大;受蚜虫危害冬小麦光谱曲线的“红边”位置波长最短(698 nm),其他不同水分处理结果随着干旱胁迫的加重向波长短的方向发生“蓝移”.因此,“红边”参数也可以作为判别冬小麦蚜虫危害和干旱胁迫的重要参数.     

Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R² = 0.65, whereas VWSI showed overestimated values with mean RD 4%.     

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.     

Monitoring winter wheat growth in North China by combining a crop model and remote sensing data

Both of crop growth simulation models and remote sensing method have a high potential in crop growth monitoring and yield prediction. However, crop models have limitations in regional application and remote sensing in describing the growth process. Therefore, many researchers try to combine those two approaches for estimating the regional crop yields. In this paper, the WOFOST model was adjusted and regionalized for winter wheat in North China and coupled through the LAI to the SAIL–PROSPECT model in order to simulate soil adjusted vegetation index (SAVI). Using the optimization software (FSEOPT), the crop model was then re-initialized by minimizing the differences between simulated and synthesized SAVI from remote sensing data to monitor winter wheat growth at the potential production level. Initial conditions, which strongly impact phenological development and growth, and which are hardly known at the regional scale (such as emergence date or biomass at turn-green stage), were chosen to be re-initialized. It was shown that re-initializing emergence date by using remote sensing data brought simulated anthesis and maturity date closer to measured values than without remote sensing data. Also the re-initialization of regional biomass weight at turn-green stage led that the spatial distribution of simulated weight of storage organ was more consistent to official yields. This approach has some potential to aid in scaling local simulation of crop phenological development and growth to the regional scale but requires further validation.     

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.     

遥感技术用于大型单体滑坡风险评估与管理

本文以平武县文家坝滑坡为研究对象,从地质、地貌、水文3方面分析了该滑坡形成的条件;以遥感地质学相关理论为指导,在地质资料、地形图、CPS数据的支撑下,经过实地验证,建立了滑坡解译标志;对震后SPOT5影像进行解译,提取出滑坡周界、滑坡要素等信息,估算出滑坡规模,分析了滑坡的发育特征;确定滑坡的坡度、岩性、地震因素、水文...     

Coupling of remote sensing data and environmental-related parameters for dengue transmission risk assessment in Subang Jaya,Malaysia

In Malaysia, the endemic level of dengue fever (DF) has already changed morbidity indicators, and the magnitude of these incidences in the last few years has surpassed the incidences of all other diseases of compulsory notification. The reasons for the dramatic emergence of DF are complex and not well understood. There are many factors that contribute to the epidemiological conditions that favour viral transmission by the main mosquito vector. This study, therefore, is filling this gap by analysing the impact of dengue incidence at a local (Subang Jaya) scale using environmental factors. Meteorological data and land-use pattern were consolidated using geographic information system (GIS) and its components as an analytical tool. We have shown that weather variables (relative humidity, temperature and precipitation) have significant correlation with DF incidence with seasonal variation. Besides land-use pattern, DF incidence shows the higher distribution in the residential area, followed by commercial and industrial area. This is due to the higher population density in residential area as well as favourable places for the breeding of dengue-carrying Aedes mosquitos created by humans in the residential area, especially one-storey houses. The analysis on the trends of DF incidence towards various housing types indicate that most of the victims’ houses fall into interconnection houses and mixed houses types compared to the independent houses area. The outcome driven from this analysis suggested that each character of the environmental factors has their own risk towards dengue incidence. In line with that, it is possible to develop a dynamic model of DF transmission using the knowledge produced by this comprehensive time series data and the results provided by the different analyses.     

基于遥感和GIS的水土流失因子信息提取与分级定标研究--以攀枝花市为例

以攀枝花市为例,主要选用美国陆地卫星2003年的Landsat-7 ETM+遥感数据、1∶10万地形图,运用ERDAS/IMAG INE遥感图像处理软件及M apG IS软件提取对水土流失影响较大的植被覆盖度、地形坡度、沟谷密度、土地利用类型等因子;根据中华人民共和国行业标准《土壤侵蚀分类分级标准》(SL 190-96),建立攀枝花市水土流失强度面蚀分级指标,为进一步水土流失监测研究提供科学依据。     

Assessing the supplementary irrigation for improving crop productivity in water stress region using spatial hydrological model

Water stress during crop cultivation due to inconsistent rainfall is a common phenomenon in maize growing area of Shanmuganadi watershed, located in the semi-arid region of southern peninsular India. The objective is to estimate the supplementary irrigation required to improve the crop productivity during water stress period. Spatial hydrological model, Soil and Water Assessment Tool, has been applied to simulate the watershed hydrology and crop growth for rabi season (October–February) considering the rainfed and irrigated scenarios. The average water stress days of rainfed maize was 60 days with yield of 1.6 t/ha. Irrigated maize with supplementary irrigation of 93–126 mm was resulted in improved yield of 3.8 t/ha with 28 water stress days. The results also suggest that supplemental irrigation can be obtained from groundwater reserves and by adopting early sowing strategy can provide opportunities for improving water productivity in rainfed farming.     

印度洋海啸灾害遥感监测与评估——以印度尼西亚亚齐省为例

介绍了运用新一代中分辨率小卫星数据———英国灾害监测小卫星(DMC,DisasterMonitoringConstellation)数据源,以印度尼西亚亚齐省为例,采用遥感数据作为信息源,对2004-12-26印尼苏门答腊岛西北海域发生的里氏9·0级的强烈地震所引发的印度洋海啸灾害进行了监测评估。所获得的小卫星图像预处理后,通过对图像中受损失地物的光谱信息分析、受损失信息与环境背景信息的对比分析,进而建立其判读标志;并在此基础上进行损失程度分级判读、统计分析等,实现了从小卫星图像上对海啸灾情损失的遥感快速监测评估。其监测评估结果为中国开展国际援助提供了客观依据,其技术方法为海啸灾害及其它灾害的遥感快速监测评价提供了技术思路,也为中国即将发射运行的灾害和环境监测预报小卫星星座及中国DMC小卫星的应用提供必要的经验及技术支持。     

A Geographic Information System (GIS)-based approach for relating anthropogenic impact and environmental stress: a case study within the African Albertine Rift

This study introduces a potentially cost-effective methodology to assess habitat quality remotely using anthropogenic variables derived from both Geographic Information System (GIS) and remote sensing. We related anthropogenic impact to organism-level response across the Albertine Rift of East-Africa, which was measured using body condition (BC) in Lophuromys aquilus, the dark-coloured brush-furred rat. We chose seven variables to measure anthropogenic impact, ranging from land cover/land use to nighttime light radiance. Using a principal component analysis, we extracted four components that describe human impact and correlated each with BC. We documented that BC tended to be higher in more disturbed areas. We recommend this GIS-based methodology to relate anthropogenic impact to organismal-level response in the Albertine Rift region, and we provide broad guidelines for its application to assess habitat quality for species of conservation concern.     

一种提取北部湾沿海地区水体信息的动态阈值方法

准确地获取水体分布信息与水体变化规律在流域综合治理等方面具有重要意义。本文基于高分六号(GF-6)影像与Landsat 8影像,选取北部湾海域作为测试研究区,在海洋水体指数(OWI)基础上,针对水体光谱特性,分析了水体像元OWI与归一化植被指数之间的相关性,基于两种指数动态平衡,提出了一个稳定性较高的动态阈值的水体提取方法,并进行了试验验证。试验结果表明,动态阈值方法能够稳定、准确地提取水体信息,避免了人为设定阈值存在的误差,并且本文提出的指数无论是针对国产高分影像还是其他光学影像,都具有较好的普适性,可应用于沿海海岸线的监测等方面。     

Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images

This paper presents a new drought assessment method by spatially and temporally integrating temperature vegetation dryness index (TVDI) with regional water stress index (RWSI) based on a synergistic approach. With the aid of LANDSAT TM/ETM data, we were able to retrieve the land-use and land-cover (LULC), vegetation indices (VIs), and land surface temperature (LST), leading to the derivation of three types of modified TVDI, including TVDI_SAVI, TVDI_ANDVI and TVDI_MSAVI, for drought assessment in a fast growing coastal area, Northern China. The categorical classification of four drought impact levels associated with the RWSI values enables us to refine the spatiotemporal relationship between the LST and the VIs. Holistic drought impact assessment between 1987 and 2000 was carried out by linking RWSI with TVDIs group wise. Research findings indicate that: (1) LST and VIs were negatively correlated in most cases of low, medium, and high vegetation cover except the case of high density vegetation cover in 2000 due to the effect of urban heat island (UHI) effect; (2) the shortage of water in 1987 was more salient than that that in 2000 based on all indices of TVDI and RWSI; and (3) TVDIs are more suitable for monitoring mild drought, normal and wet conditions when RWSI is smaller than 0.752; but they are not suitable for monitoring moderate and severe drought conditions.     

DEM精度检查中等高线回放的量化方法

目前数字高程模型(DEM)精度检查中广泛采用等高线回放方法,以目视检查方式进行,定性为主。基于集合论引进了一种量化方法,即层次等高线差异栅格分析法,用于描述等高线回放的平面位置变形量。实验证明此方法不仅可视化效果好,其量化指标有助于定位DEM误差,包括粗差;也适合对不同DEM内插方法精度评定的对比分析。     

A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree,Markov chain and cellular automata models in GIS

Urban development is a continuous and dynamic spatio-temporal phenomenon associated with economic developments and growing populations. To understand urban expansion, it is important to establish models that can simulate urbanization process and its deriving factors behaviours, monitor deriving forces interactions and predict spatio-temporally probable future urban growth patterns explicitly. In this research, therefore, we presented a hybrid model that integrates the chi-squared automatic integration detection decision tree (CHAID-DT), Markov chain (MC) and cellular automata (CA) models to analyse, simulate and predict future urban expansions in Tripoli, Libya in 2020 and 2025. First, CHAID-DT model was applied to investigate the contributions of urban factors to the expansion process, to explore their interactions and to provide future urban probability map; second, MC model was employed to estimate the future demand of urban land; third, CA model was used to allocate estimated urban land quantity on the probability map to present future projected land use map. Three satellite images of the study area were obtained from the periods of 1984, 2002 and 2010 to extract land use maps and urban expansion data. We validated the model with two methods, namely, receiver operating characteristic and the kappa statistic index of agreement. Results confirmed that the proposed hybrid model could be employed in urban expansion modelling. The applied hybrid model overcame the individual shortcomings of each model and explicitly described urban expansion dynamics, as well as the spatio-temporal patterns involved.