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This paper presents a new method for real-time automatic detection of nonforested and eroded areas in tropical rain forests. It is based on simple image algebra between color components, which enhances the contrast between brown and green areas. A successive segmentation through multiple thresholds, based on newly proposed indices of “brown color excess” and the “nonforest detection index,” leads to a binary map that clearly identifies forested and nonforested areas. Experimental tests, performed and compared with other recommended methods based on: region growing, active contours, and clustering, outperformed in detection accuracy ( Fm=96.4 %) and processing times ( Tp = 0.082 s). The method presented copes well with detecting regional irregularities and reduces frequent issues of nondetection, as well as false positives caused by intensity changes, shadows, and/or partial occlusions. The low processing times achieved with the proposed method allow real-time applications for low-cost unmanned aerial vehicle and unmanned aircraft systems with conventional camera equipment. [ABSTRACT FROM PUBLISHER]

In this paper, a simple algorithm is proposed to perform the nowcasting of rainfall in the tropical region. The algorithm applies global positioning system-derived precipitable water vapor (PWV) values and its second derivative for the short-term prediction of rainfall. The proposed algorithm incorporates the seasonal dependency of PWV values for the prediction of a rain event in the coming 5 min based on the past 30 min of PWV data. This proposed algorithm is based on the statistical study of four-year PWV and rainfall data from a station in Singapore and is validated using two-year independent data for the same station. The results show that the algorithm can achieve an average true detection rate and a false alarm rate of 87.7% and 38.6%, respectively. To analyze the applicability of the proposed algorithm, further validations are done using one-year data from one independent station from Singapore and two-year data from one station from Brazil. It is shown that the proposed algorithm performs well for both the independent stations. For the station from Brazil, the average true detection and false alarm rates are around 84.7% and 37%, respectively. All these observations suggest that the proposed algorithm is reliable and works well with a good detection rate. [ABSTRACT FROM AUTHOR]

A new method using water vapor pressure (WVP) from a radiosonde profile to determine the cloud vertical structure for the tropical region is proposed in this paper. This includes both the cloud base height and the cloud occurrences at different levels in the atmosphere. Our study shows that the presence of clouds depends on the following criterion: the measured WVP is larger than the critical WVP at the same level. The applicable level is found to be within the range of 300–12 000 m. The estimated cloud vertical structure using the proposed method is compared with the Salonen and Uppala (SU) model, the ceilometer data, and two kinds of meteorological observation data, namely, SYNOP and METAR. The proposed model shows a higher accuracy level of prediction of the cloud vertical structure as compared with the existing SU model. [ABSTRACT FROM AUTHOR]

A nowcasting technique has been proposed to estimate the impending rain accumulation using ground-based radiometric measurements at Kolkata (22.65°N, 88.45°E), a tropical location. It has been observed that the normalized variation of brightness temperature (BT) at 31 GHz along with the standard deviation of BT at 22 GHz and instability indices, namely, lifting index, have shown definite changes before rain events. A combination of these three parameters can be effective in predicting rain events both qualitatively and quantitatively. Accordingly, a prediction model is developed and tested on several intense rain events during the period 2014–2015. The model is found to perform reasonably well in predicting intense rain about 70–75 min in advance with an efficiency of 80%. [ABSTRACT FROM AUTHOR]