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Abstract:The occurrence and extent of intense harmful algal blooms (HABs) have increased in inland waters during recent decades. Standard monitor networks, based on infrequent sampling from a few fixed observation stations, are not providing enough information on the extent and intensity of the blooms. Remote sensing has great potential to provide the spatial and temporal coverage needed. Several sensors have been designed to study water properties (AVHRR, SeaBAM, and SeaWIFS), but most lack adequate spatial resolution for monitoring algal blooms in small and medium-sized lakes. Over the last decade, satellite data with 250-m spatial resolution have become available with MODIS. In the present study, three models inspired by published approaches (Kahru, Gitelson, and Floating Algae Index (FAI)) and a new approach named APPEL (APProach by ELimination) were adapted to the specific conditions of southern Quebec and used to estimate chlorophyll-a concentration (Chl-a) using MODIS data. Calibration and validation were provided from in situ Chl-a measured in four lakes over 9 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) and concurrent MODIS imagery. MODIS bands 3 to 7, originally at 500-m spatial resolution, were downscaled to 250 m. The APPEL, FAI, and Kahru models yielded satisfactory results and enabled estimation of Chl-a for heavy blooming conditions (Chl-a > 50 mg•m −3 ), with coefficients of determination reaching 0.95, 0.94, and 0.93, respectively. The model inspired from Gitelson did not provide good estimations compared to the others (R 2 = 0.77).However, the performance of all models decreased when Chl-a was below 50 mg•m −3 . OPEN ACCESSRemote Sens. 2012, 4 2374

Abstract:The purpose of this study is to assess the performance of an adaptive model (AM) in estimating chlorophyll-a concentration (Chl-a) in optically complex inland waters. Chl-a modeling using remote sensing data is usually based on a single model that generally follows an exponential function. The estimates produced by such models are relatively accurate at high Chl-a concentrations, but accuracy drops at low concentrations. Our objective was to develop an approach combining spectral response classification and three semi-empirical algorithms. The AM discriminates between three blooming classes (waters poorly, moderately, and highly loaded in Chl-a), with discrimination thresholds set using the classification and regression tree (CART) technique. The calibration of three specific estimators for each class was achieved using a multivariate stepwise regression. Compared to published models (Floating Algae Index, Kahru model, and APProach by ELimination) using the same data set, the AM provided better Chl-a concentration estimates (R 2 of 0.96, relative RMSE of 23%, relative Bias of −2%, and a relative NASH criterion of 0.9). Moreover, the AM achieved an overall success rate of 67% in the estimation of blooming classes (corresponding to low, moderate, and high Chl-a concentration classes). This was done using an independent data set collected from 22 inland water bodies for the period 2007-2010 and for which the only information available was the blooming class.

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Surveys and farmer interviews were conducted to identify key pests attacking frafra potato. The sweetpotato butterfly, Acraea acerata, was the most frequently encountered pest, its larvae causing much of the defoliation observed on farmers' fields. White flies ( Bemisia tabacci), leafhoppers, termites, grasshoppers/crickets and millipedes were also associated with the crop. However, farmers took no conscious pest control actions, due mainly to lack of technical know-how. Fresh tuber yields of the three types grown widely in the area ranged from 3-6.2 tons/ha, with the Red and Black types significantly out yielding the White during the two seasons. Controlling both foliar and soil pests increased tuber yields by 23 to 64% over the control.