Image time series processing for agriculture monitoring

Eerens, Herman; Haesen, Dominique; Rembold, Felix; Urbano, Ferdinando; Toté, C.; Bydekerke, Lieven

doi:10.1016/j.envsoft.2013.10.021

Cited by 87 publications

(54 citation statements)

References 25 publications

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“…Its main advantages are represented by the data derived from spectral signatures of vegetation and possibilities to combine meteorological data for crop monitoring (EERENS et al, 2014). Currently, a large range of studies involving remote sensing with energy purposes has been conducted with sugarcane, gathering qualitative aspects, referring to biophysical parameters of the plant (SERRANO et al, 2002), and quantitative aspects mainly related to production estimation (PICOLI et al, 2009).…”

Section: Geotechnologies In Agro-energy Planningmentioning

confidence: 99%

Identification, assessment and ordering of concepts for agro-energy planning and possibilities of spatial analysis

et al. 2015

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ABSTRACT:The present study aims to present the main concepts of the sugarcane straw to energy planning. Throughout the study, the subject is contextualized highlighting broader aspects of sustainability, which is considered the main driver towards agro-energy modernization. Concerning sugarcane straw, we first evaluated its availability regarding technical and economic aspects, and then it summarized the straw production chain for energy supply purposes. As a proposal to support agro-energy planning, it is presented some spatial tools that have been barely used in the Brazilian energy planning context so far. Therefore, working on straw to electricity associated with supply chain basis, we developed a conceptual model to spatially assess this bioenergy system. Using the model proposed, it is described the whole supply chain at state level, which accounted the potential of a single mill to explore straw, as well as main costs associated with straw acquisition, investments on the straw recovery routes and electricity transmission. Bearing these concepts in mind, it is fully believed that spatial analysis can bring important information for agro-energy action plans.KEYWORDS: sugarcane, straw, agro-energy planning, geographic information system. IDENTIFICAÇÃO, AVALIAÇÃO E ORDENAMENTO DE CONCEITOS PARA O PLANEJAMENTO AGROENERGÉTICO E AS POSSIBILIDADES DA ANÁLISE ESPACIALRESUMO: O presente artigo busca apresentar os principais conceitos envolvendo um planejamento energético específico da palha da cana-de-açúcar. Ao longo do trabalho, procurou-se contextualizar o tema por meio de questões de sustentabilidade, considerada um dos principais indutores em direção à modernização da agroenergia. Considerando a palha da cana de açúcar, avaliou-se primeiramente sua disponibilidade envolvendo aspetos técnicos e econômicos, e posteriormente caracterizou-se a cadeia de produção da palha para energia. Como proposta para auxílio ao planejamento agroenergetico, apresentou-se algumas ferramentas espaciais que até então pouco têm sido utilizadas para planejamento energético no Brasil. Deste modo, trabalhando o potencial de palha para eletricidade associado à sua cadeia de produção, desenvolveu-se um modelo conceitual para se avaliar espacialmente esse sistema bioenergético. Utilizando o modelo proposto, foi possível descrever toda a cadeia de produção da palha para energia em um nível estadual, envolvendo o potencial que cada usina pode explorar, bem como os principais custos associados à sua aquisição, investimentos relacionados à rota de recuperação e em linhas de transmissão. Por meio dos conceitos abordados, acredita-se que análise espacial pode trazer importantes informações para planos de ação no âmbito agroenergético. PALAVRAS-CHAVE:cana-de-açúcar, palha, planejamento energético, sistema de informação geográfica.Identification, assessment and ordering of concepts for agro-energy planning and possibilities of sp atial analysisEng.

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Section: Geotechnologies In Agro-energy Planningmentioning

confidence: 99%

Identification, assessment and ordering of concepts for agro-energy planning and possibilities of spatial analysis

et al. 2015

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“…counties, constituencies of Kenya) and/or livelihood zones. All data are imported at NDMA into SPIRITS (Eerens et al, 2014) for production of seasonal graphs, etc. Additional web-tools were developed by BOKU for educational purposes (BOKU, 2015).…”

Section: Anomaliesmentioning

confidence: 99%

Satellite-based drought monitoring in Kenya in an operational setting

Klisch

Atzberger

Luminari

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The University of Natural Resources and Life Sciences (BOKU) in Vienna (Austria) in cooperation with the National Drought Management Authority (NDMA) in Nairobi (Kenya) has setup an operational processing chain for mapping drought occurrence and strength for the territory of Kenya using the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI at 250 m ground resolution from 2000 onwards. The processing chain employs a modified Whittaker smoother providing consistent NDVI "Mondayimages" in near real-time (NRT) at a 7-daily updating interval. The approach constrains temporally extrapolated NDVI values based on reasonable temporal NDVI paths. Contrary to other competing approaches, the processing chain provides a modelled uncertainty range for each pixel and time step. The uncertainties are calculated by a hindcast analysis of the NRT products against an "optimum" filtering. To detect droughts, the vegetation condition index (VCI) is calculated at pixel level and is spatially aggregated to administrative units. Starting from weekly temporal resolution, the indicator is also aggregated for 1-and 3-monthly intervals considering available uncertainty information. Analysts at NDMA use the spatially/temporally aggregated VCI and basic image products for their monthly bulletins. Based on the provided bio-physical indicators as well as a number of socio-economic indicators, contingency funds are released by NDMA to sustain counties in drought conditions. The paper shows the successful application of the products within NDMA by providing a retrospective analysis applied to droughts in 2006, 2009 and 2011. Some comparisons with alternative products (e.g. FEWS NET, the Famine Early Warning Systems Network) highlight main differences.

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“…Specific and detailed information of the PROBA-V mission was previously described [25][26][27]. The PROBA-V 100-and 300-m S1 (daily synthesis product) top of canopy (TOC) reflectance products were downloaded from VITO's product distribution portal [28] in the HDF5 file format and then converted to the ENVI file format using SPIRITS (Software for the Processing and Interpretation of Remotely sensed Image Time Series [29][30][31].…”

Section: Field Measured Datamentioning

confidence: 99%

Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products

Zheng

Zhang

et al. 2016

Remote Sensing

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Abstract:Monitoring crop areas and yields is crucial for food security and agriculture management across the world. In this paper, we mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation (PROBA-V) products in the North China Plain (NCP). First, the daily 100-m land surface reflectance was generated by fusing the PROBA-V 100-m and 300-m S1 products. Our results show that the blended data exhibited high correlations with the referenced data (0.71 ≤ R 2 ≤ 0.94 for the red band, 0.50 ≤ R 2 ≤ 0.95 for the near-infrared band, and 0.88 ≤ R 2 ≤ 0.97 for the shortwave infrared band). The time-series Normalized Difference Vegetation Index (NDVI) derived from the synthetic reflectance was then clustered for winter wheat identification. The overall classification accuracy was between 78% and 87%, with a kappa coefficient above 0.57, which was 10%-20% higher than the classification accuracy using the 300-m data. Finally, a light use efficiency model was employed to estimate the biomass and yield. The estimation results were closely related to the field-measured biomass and yield, with high R 2 and low root mean square errors (RMSE) (0.864 ≤ R 2 ≤ 0.871 and 168 ≤ RMSE ≤ 191 g/m 2 for biomass; and 0.631 ≤ R 2 ≤ 0.663 and 41.8 ≤ RMSE ≤ 62.8 g/m 2 for yield). This paper shows the strong potential of using PROBA-V 100-m data to enhance the spatial resolution of PROBA-V 300-m data and because the proposed framework in this study was based only on the relatively high spatio-temporal resolution PROBA-V data and achieved favorable results, it provides a novel approach for crop areas and yields estimation utilizing the relatively new data set.

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Image time series processing for agriculture monitoring

Cited by 87 publications

References 25 publications

Identification, assessment and ordering of concepts for agro-energy planning and possibilities of spatial analysis

Identification, assessment and ordering of concepts for agro-energy planning and possibilities of spatial analysis

Satellite-based drought monitoring in Kenya in an operational setting

Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products

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