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Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the OPEN ACCESSRemote Sens. 2015, 7 14483proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km 2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ≈ 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map.

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RESUMOVisando a fornecer subsídios para programas de manejo de plantas daninhas em culturas agrícolas, foi conduzido um experimento de campo em Botucatu, SP. O objetivo foi determinar, através do procedimento estatístico de análise de regressão, o período critico para prevenção da interferên-cia (PCPI) de plantas daninhas de folha larga na produtividade da cultura de soja. Foi utilizado o delineamento experimental em blocos casualizados, com 3 repetições. A cultura foi mantida na presença das plantas daninhas de folha larga por diferentes períodos. O período crítico determinado foi de 21 a 30 dias após a emergência da cultura, segundo ajuste dos dados de produtividade ao modelo "Broken-Stick". No entanto, o período critico determinado indica que o controle das plantas daninhas pode ser realizado, uma única vez, através do uso de método momentâneo, sem efeito residual. Palavras-chave:Modelos de regressão, controle, manejo, ecologia, Glycine max. ABSTRACT Determining the critical period of weeds interference on soybean yield: use of broken-stick modelThis research is in support of weed management programs. A field experiment was carried out in Botucatu (São Paulo, Brazil), with the objective to determine the critical period of broadleaf weed interference on soybean, using statistical procedures of regression analysis. A randomized block design was used with 3 replications. The crop was kept weed infested for different periods. The critical period of INTRODUÇÃOOs programas de manejo de plantas daninhas são importantes no sentido da racionalização do seu controle em culturas agricolas. Para a implementação desses programas é weed interference determined by the "Broken-Stick" model was between 21-30 days after crop emergence. Broadleaf weeds in soybean crops can be controled with single use of remedial methods without residual effect.Additional index words: Regression models, control, weed management, ecology, Glycine max.imprescindível estudar os periodos de convivência possível entre as plantas daninhas e a planta cultivada. Segundo Pitelli & Durigan (1984), o período anterior a interferência (PAI) é aquele em que, a partir da emergência da cultura, esta pode conviver com as plantas daninhas sem 1 Recebido para publicação em 04/04/94 e na forma revisada em 17/06/94. Trabalho realizado com suporte financeiro da FAPESP.

-The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classifi cation method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classifi cation was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fi eldwork data, TM/Landsat-5 and CCD/ CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classifi cation by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classifi cation overestimated in 21.31% of the reference values. In regions where soybean fi elds were less prevalent, the classifi er overestimated 132.37% in the acreage of the reference. The overall classifi cation accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classifi cation of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less effi cient, overestimating soybean areas.Index terms: Glycine max, accuracy, agricultural statistics, classifi cation, remote sensing, thematic map. Estimativa de áreas de soja usando superfícies espectro-temporais derivadas de imagens MODIS em Mato Grosso, BrasilResumo -O objetivo deste trabalho foi avaliar a aplicação do método de classifi cação por superfícies de resposta espectro-temporal (STRS) em imagens do sensor Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) para estimar áreas de plantio de soja no Estado de Mato Grosso, Brasil. A classifi cação foi realizada usando o algoritmo de máxima verossimilhança (MLA) adaptado ao algoritmo STRS. Trinta segmentos de 30x30 km foram escolhidos ao longo das principais regiões agrícolas do estado, com dados da safra de verão de 2005/2006 (outubro a março), e mapeados com base em dados de campo e de imagens orbitais TM/Landsat-5 e CCD/CBERS-2. Cinco classes temáticas foram consideradas: Soja, Floresta, Cerrado, Pastagem e Solos Expostos. A classifi cação pelo método das STRS foi feita com base em uma área interseccionada por um subconjunto de segmentos de 30x30 km. O STRS superestimou os valores de referência em 21,31% em regiões com predomínio da cultura da soja e em 132,37% em regiões nas quais a soja era menos predominante. A exatidão global da classifi cação foi de 80%. As imagens MODIS e o algoritmo STRS mostraram-se promissores para a classifi cação da soja em regiões com predominância de grandes fazendas. Entretanto, os resultados para áreas fragmentadas em fazendas menores foram menos efi cientes, superestimando as áreas de soja.Termos para indexação: Glycine max, acurácia, estatísticas agrícolas, classifi cação, sensoriamento r...

Abstract:The potential of optical remote sensing data to identify, map and monitor croplands is well recognized. However, clouds strongly limit the usefulness of optical imagery for these applications. This paper aims at assessing cloud cover conditions over four states in the tropical and sub-tropical Center-South region of Brazil to guide the development of an appropriate agricultural monitoring system based on Landsat-like imagery. Cloudiness was assessed during overlapping four months periods to match the typical length of crop cycles in the study area. The percentage of clear sky occurrence was computed from the 1 km resolution MODIS Cloud Mask product (MOD35) considering 14 years of data between July 2000 and June 2014. Results showed high seasonality of cloud occurrence within the crop year with strong variations across the study area. The maximum seasonality was observed for the two states in the northern part of the study area (i.e., the ones closer to the Equator line), which also presented the lowest averaged values (15%) of clear sky occurrence during the main (summer) cropping period (November to February). In these locations, optical data faces severe constraints for mapping summer crops. On the other hand, relatively favorable conditions were found in the southern part of the study region. In the South, clear sky values of around 45% were found and no significant clear sky seasonality was observed. Results underpin the challenges to implement an operational crop monitoring system based solely on optical remote sensing imagery in tropical and sub-tropical regions, in particular if short-cycle crops have to be monitored during the cloudy summer months. To cope with cloudiness issues, we recommend the use of new systems with higher repetition rates such as Sentinel-2. For local studies, Unmanned Aircraft Vehicles (UAVs) might be used to augment the observing capability. Multi-sensor approaches combining optical and microwave data can be another option. In cases where wall-to-wall maps are not mandatory, statistical sampling approaches might also be a suitable alternative for obtaining useful crop area information.

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We developed a computer program for life table analysis using the open source, free software programming environment R. It is useful to quantify chronic nonlethal effects of treatments on arthropod populations by summarizing information on their survival and fertility in key population parameters referred to as fertility life table parameters. Statistical inference on fertility life table parameters is not trivial because it requires the use of computationally intensive methods for variance estimation. Our codes present some advantages with respect to a previous program developed in Statistical Analysis System. Additional multiple comparison tests were incorporated for the analysis of qualitative factors; a module for regression analysis was implemented, thus, allowing analysis of quantitative factors such as temperature or agrochemical doses; availability is granted for users, once it was developed using an open source, free software programming environment. To illustrate the descriptive and inferential analysis implemented in lifetable.R, we present and discuss two examples: 1) a study quantifying the influence of the proteinase inhibitor berenil on the eucalyptus defoliator Thyrinteina arnobia (Stoll) and 2) a study investigating the influence of temperature on demographic parameters of a predaceous ladybird, Hippodamia variegata (Goeze).