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Felix Rembold - One of the best experts on this subject based on the ideXlab platform.

  • Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets
    Remote Sensing, 2013
    Co-Authors: Clement Atzberger, Felix Rembold
    Abstract:

    For large areas, it is difficult to assess the spatial distribution and inter-annual variation of Crop Acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale Crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel Crop Acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI). Neural networks can learn the relation between temporal NDVI signatures and the sought Crop Acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach, a study region in central Italy (Tuscany) was selected. The task consisted of mapping the spatial distribution of winter Crops abundances within 1 km AVHRR pixels between 1988 and 2001. Reference Crop Acreage information for network training and validation was derived from high resolution Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+) images and official agricultural statistics. Encouraging

  • Portability of neural nets modelling regional winter Crop Acreages using AVHRR time series
    European Journal of Remote Sensing, 2012
    Co-Authors: Clement Atzberger, Felix Rembold
    Abstract:

    AbstractTime series of coarse resolution imagery offer the advantage of free global coverage but have to deal with mixed pixels. The study uses neural nets as modelling tool for sub-pixel Crop Acreage estimation. Nets are trained with reference Crop Acreage information derived from 30 m Landsat images and CORINE LC map for interpreting changes in the shapes of coarse resolution AVHRR NDVI profiles. Using official AGRIT statistics for Tuscany (Italy) as reference information, the network portability across years was evaluated. Using 3 images acquired before 2002 nets were trained. Subsequent application to 2002–2009 data explained roughly half of the inter-annual variance.

  • The use of MODIS data to derive Acreage estimations for larger fields : A case study in the south-western Rostov region of Russia
    International Journal of Applied Earth Observation and Geoinformation, 2008
    Co-Authors: Steffen Fritz, Javier Gallego, Michel Massart, Igor Savin, Felix Rembold
    Abstract:

    Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating Crop Acreage over larger areas. Remotely sensed data allow in some cases the estimation of Crop Acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterised by large agricultural fields of around 75 ha on average. This paper presents a methodology to estimate Crop Acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality Crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross-checking the MODIS classification a Landsat ETM time series for four different dates in the season of 2002 was acquired and classified. We attempted to distinguish five different Crop types and achieved satisfactory and good results for winter Crops. Three hundred and sixty fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior.

L. Iglesias Martinez - One of the best experts on this subject based on the ideXlab platform.

A. T. Jeyaseelan - One of the best experts on this subject based on the ideXlab platform.

  • Modeling of groundwater draft based on satellite-derived Crop Acreage estimation over an arid region of northwest India
    Hydrogeology Journal, 2016
    Co-Authors: Bidyut Kumar Bhadra, Sanjay Kumar, Rakesh Paliwal, A. T. Jeyaseelan
    Abstract:

    地下水超采用于农作物灌溉对印度拉贾斯坦邦干旱地区自然资源的克持续性造成了压力。2004–2005年到2011–2012年季风前(5月到6月)、季风后(10月到11月)及灌溉后(2月到3月)期间进行的研究区地下水位水文地质研究显示,地下水位稳定下降速率为1.28至1.68 米/年,主要是由于地下水超采用于灌溉造成的。由于研究区内地下水观测井网密度很低,因此,地下水开采评价及地下水资源的管理成为一项艰巨的任务。为了克服这种状况,根据2003–2004年卫星导出农作物面积和观测的地下水开采量之间的经验关系,建立了线性地下水开采模型。在三年长的间隔(2005–2006年,2008–2009年和2011–2012年)期间,利用通过排泄因子方法估算的地下水开采量对模型进行了10年的验证。而且,通过随机采样的村庄观测的抽水资料对估算的开采量进行了验证(2011–2012年)。结果显示,建立的线性地下水开采模型对印度西北部干旱地区缺乏地下水观测井的情况下估算地下水开采量提供了很好的替代选择。 A sobre-exploração das águas subterrâneas para cultivos agrícolas causa estres na sustentabilidade dos recursos naturais na região árida do estado do Rajastão, Índia. Estudo hidrogeológico dos níveis da água subterrânea na área de estudo durante as estações pré-monção (maio a junho), pós-monção (outubro a novembro) e pós-irrigação (fevereiro a março) de 2004–2005 a 2011–2012 mostrou um declínio constante dos níveis a uma taxa de 1.28 a 1.68 m/ano, causado principalmente pela excessiva abstração para irrigação. Devido à baixa densidade da rede de poços de observação na área de estudo, a avaliação da abstração das águas subterrâneas e, assim, a gestão dos recursos hídricos subterrâneos, torna-se uma tarefa difícil. Para reverter a situação, um modelo linear de abstração (LGDM) foi desenvolvido baseado na relação empírica entre superfície cultivadas estimadas via satélite e do bombeamento de águas subterrâneas observado para o ano de 2003–2004. O modelo foi validado por uma década, durante três intervalos (2005–2006, 2008–2009 e 2011–2012) utilizando a abstração estimada a partir do método de fator de descarga. Os resultados sugerem que o desenvolvimento do modelo LGDM proporciona uma boa alternativa para a estimativa da abstração baseadas em produtos de satélites para áreas cultivadas na ausência de poços de monitoramento em regiões áridas no nordeste da Índia. La sobreexplotación del agua subterránea para los cultivos agrícolas pone tensión a la sostenibilidad de los recursos naturales en la región árida del estado de Rajasthan, India. Un estudio hidrogeológico de niveles freáticos de la zona de estudio durante el pre-monzón (mayo a junio), post-monzón (de octubre a noviembre) y post-riego (febrero-marzo) en las temporadas 2004–2005 al 2011–2012 muestra una disminución constante de niveles de aguas subterráneas, a razón de 1.28 a 1.68 m/año, debido principalmente a un proyecto excesiva extracción de agua subterránea para riego. Debido a la baja densidad de la red de pozos de observación de agua subterránea en el área de estudio, la evaluación de la extracción de agua subterránea, y por lo tanto la gestión de recursos de agua subterránea se convierte en una tarea difícil. Para superar la situación, se desarrolló un modelo lineal de extracción de agua subterránea (LGDM) en base a la relación empírica entre la superficie de cultivo procedente de satélites y la extracción de agua subterránea observada para el año 2003–2004. El modelo ha sido validado para una década, durante tres intervalos de un año de duración (2005–2006, 2008–2009 y 2011–2012) utilizando la extracción de agua subterránea, estimada a través de un método de factor de descarga. Además, la extracción estimada fue validada por los datos de bombeo observados en poblados muestreados al azar (2011–2012). Los resultados sugieren que el modelo desarrollado LGDM ofrece una buena alternativa a la estimación de la extracción de agua subterránea en base a la superficie de cultivo a partir de satélites en ausencia de pozos de observación de agua subterránea en las regiones áridas del noroeste de la India. La surexploitation des eaux souterraines pour l’agriculture met à mal la durabilité des ressources naturelles dans la région aride de l’état du Rajasthan, Inde. L’étude hydrogéologique des niveaux piézométriques de la région étudiée durant les saisons de pré-mousson (mai à juin), post-mousson (octobre à novembre) et post-irrigation (février à mars) de 2004–2005 à 2011–2012 montre une décroissance régulière des niveaux piézométriques à un taux de 1.28 à 1.68 m/an, principalement due à des prélèvements excessifs d’eau souterraine pour l’irrigation. Du fait à la faible densité du réseau de puits d’observation des eaux souterraines dans la région d’étude, l’estimation des prélèvements d’eau souterraine et, par conséquent, la gestion de la ressource en eau souterraine est une tâche difficile. Pour surmonter cette situation, un modèle linéaire de prélèvement d’eau souterraine (LGDM) a été développé sur la base d’une relation empirique entre les surfaces cultivées obtenues par satellite et les prélèvements d’eau souterraine observés au cours de l’année 2003–2004. Le modèle a été validé pour une décennie, pendant des intervalles de trois ans (2005–2006, 2008–2009 et 2011–2012) en utilisant un prélèvement d’eau souterraine estimé à partir d’une méthode de facteur de débit. De plus, le prélèvement estimé a été validé à partir de données de pompage observées à partir de villages tirés au hasard (2011–2012). Les résultats suggèrent que le modèle LGDM développé fournit une bonne alternative à l’estimation des prélèvements d’eau souterraine basée sur les surfaces de cultures estimées par satellite, en l’absence d’observations hydrogéologiques dans les régions arides du nord-ouest de l’Inde. Over-exploitation of groundwater for agricultural Crops puts stress on the sustainability of natural resources in the arid region of Rajasthan state, India. Hydrogeological study of groundwater levels of the study area during the pre-monsoon (May to June), post-monsoon (October to November) and post-irrigation (February to March) seasons of 2004–2005 to 2011–2012 shows a steady decline of groundwater levels at the rate of 1.28–1.68 m/year, mainly due to excessive groundwater draft for irrigation. Due to the low density of the groundwater observation-well network in the study area, assessment of groundwater draft, and thus groundwater resource management, becomes a difficult task. To overcome the situation, a linear groundwater draft model (LGDM) has been developed based on the empirical relationship between satellite-derived Crop Acreage and the observed groundwater draft for the year 2003–2004. The model has been validated for a decade, during three year-long intervals (2005–2006, 2008–2009 and 2011–2012) using groundwater draft, estimated through a discharge factor method. Further, the estimated draft was validated through observed pumping data from random sampled villages (2011–2012). The results suggest that the developed LGDM model provides a good alternative to the estimation of groundwater draft based on satellite-based Crop area in the absence of groundwater observation wells in arid regions of northwest India.

  • Modeling of groundwater draft based on satellite-derived Crop Acreage estimation over an arid region of northwest India
    Hydrogeology Journal, 2016
    Co-Authors: Bidyut Kumar Bhadra, Sanjay Kumar, Rakesh Paliwal, A. T. Jeyaseelan
    Abstract:

    Over-exploitation of groundwater for agricultural Crops puts stress on the sustainability of natural resources in the arid region of Rajasthan state, India. Hydrogeological study of groundwater levels of the study area during the pre-monsoon (May to June), post-monsoon (October to November) and post-irrigation (February to March) seasons of 2004–2005 to 2011–2012 shows a steady decline of groundwater levels at the rate of 1.28–1.68 m/year, mainly due to excessive groundwater draft for irrigation. Due to the low density of the groundwater observation-well network in the study area, assessment of groundwater draft, and thus groundwater resource management, becomes a difficult task. To overcome the situation, a linear groundwater draft model (LGDM) has been developed based on the empirical relationship between satellite-derived Crop Acreage and the observed groundwater draft for the year 2003–2004. The model has been validated for a decade, during three year-long intervals (2005–2006, 2008–2009 and 2011–2012) using groundwater draft, estimated through a discharge factor method. Further, the estimated draft was validated through observed pumping data from random sampled villages (2011–2012). The results suggest that the developed LGDM model provides a good alternative to the estimation of groundwater draft based on satellite-based Crop area in the absence of groundwater observation wells in arid regions of northwest India.

L. Ambrosio Flores - One of the best experts on this subject based on the ideXlab platform.

Zhongxin Chen - One of the best experts on this subject based on the ideXlab platform.

  • An optimized two-stage spatial sampling scheme for winter wheat Acreage estimation using remotely sensed imagery
    International Journal of Remote Sensing, 2018
    Co-Authors: Di Wang, Qingbo Zhou, Peng Yang, Zhongxin Chen
    Abstract:

    ABSTRACTTimely and reliable information on Crop Acreage is essential for formulating grain production policies and ensuring national food security. The combination of available satellite-based remo...

  • Design of a spatial sampling scheme considering the spatial autocorrelation of Crop Acreage included in the sampling units
    Journal of Integrative Agriculture, 2018
    Co-Authors: Di Wang, Qingbo Zhou, Peng Yang, Zhongxin Chen
    Abstract:

    Abstract Information on Crop Acreage is important for formulating national food polices and economic planning. Spatial sampling, a combination of traditional sampling methods and remote sensing and geographic information system (GIS) technology, provides an efficient way to estimate Crop Acreage at the regional scale. Traditional sampling methods require that the sampling units should be independent of each other, but in practice there is often spatial autocorrelation among Crop Acreage contained in the sampling units. In this study, using Dehui County in Jilin Province, China, as the study area, we used a thematic Crop map derived from Systeme Probatoire d’Observation de la Terre (SPOT-5) imagery, cultivated land plots and digital elevation model data to explore the spatial autocorrelation characteristics among maize and rice Acreage included in sampling units of different sizes, and analyzed the effects of different stratification criteria on the level of spatial autocorrelation of the two Crop Acreages within the sampling units. Moran's I, a global spatial autocorrelation index, was used to evaluate the spatial autocorrelation among the two Crop Acreages in this study. The results showed that although the spatial autocorrelation level among maize and rice Acreages within the sampling units generally decreased with increasing sampling unit size, there was still a significant spatial autocorrelation among the two Crop Acreages included in the sampling units (Moran's I varied from 0.49 to 0.89), irrespective of the sampling unit size. When the sampling unit size was less than 3 000 m, the stratification design that used Crop planting intensity (CPI) as the stratification criterion, with a stratum number of 5 and a stratum interval of 20% decreased the spatial autocorrelation level to almost zero for the maize and rice area included in sampling units within each stratum. Therefore, the traditional sampling methods can be used to estimate the two Crop Acreages. Compared with CPI, there was still a strong spatial correlation among the two Crop Acreages included in the sampling units belonging to each stratum when cultivated land fragmentation and ground slope were used as stratification criterion. As far as the selection of stratification criteria and sampling unit size is concerned, this study provides a basis for formulating a reasonable spatial sampling scheme to estimate Crop Acreage.