Deep Learning Regression Approaches Applied to Estimate Tillering in Tropical Forages Using Mobile Phone Images.

We assessed the performance of Convolutional Neural Network (CNN)-based approaches using mobile phone images to estimate regrowth density in tropical forages. We generated a dataset composed of 1124 labeled images with 2 mobile phones 7 days after the harvest of the forage plants. Six architectures were evaluated, including AlexNet, ResNet (18, 34, and 50 layers), ResNeXt101, and DarkNet. The best regression model showed a mean absolute error of 7.70 and a correlation of 0.89. Our findings suggest that our proposal using deep learning on mobile phone images can successfully be used to estimate regrowth density in forages.

Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method.

Mapping utility poles using side-view images acquired with car-mounted cameras is a time-consuming task, mainly in larger areas due to the need for street-by-street surveying. Aerial images cover larger areas and can be feasible alternatives although the detection and mapping of the utility poles in urban environments using top-view images is challenging. Thus, we propose the use of Adaptive Training Sample Selection (ATSS) for detecting utility poles in urban areas since it is a novel method and has not yet investigated in remote sensing applications. Here, we compared ATSS with Faster Region-based Convolutional Neural Networks (Faster R-CNN) and Focal Loss for Dense Object Detection (RetinaNet ), currently used in remote sensing applications, to assess the performance of the proposed methodology. We used 99,473 patches of 256 × 256 pixels with ground sample distance (GSD) of 10 cm. The patches were divided into training, validation and test datasets in approximate proportions of 60%, 20% and 20%, respectively. As the utility pole labels are point coordinates and the object detection methods require a bounding box, we assessed the influence of the bounding box size on the ATSS method by varying the dimensions from 30×30 to 70×70 pixels. For the proposal task, our findings show that ATSS is, on average, 5% more accurate than Faster R-CNN and RetinaNet. For a bounding box size of 40×40, we achieved Average Precision with intersection over union of 50% (AP50) of 0.913 for ATSS, 0.875 for Faster R-CNN and 0.874 for RetinaNet. Regarding the influence of the bounding box size on ATSS, our results indicate that the AP50 is about 6.5% higher for 60×60 compared to 30×30. For AP75, this margin reaches 23.1% in favor of the 60×60 bounding box size. In terms of computational costs, all the methods tested remain at the same level, with an average processing time around of 0.048 s per patch. Our findings show that ATSS outperforms other methodologies and is suitable for developing operation tools that can automatically detect and map utility poles.

Water provisioning improvement through payment for ecosystem services.

We assess whether a Payments for Ecosystem Services (PES) programme met its objectives of reducing soil erosion and yielding water in an environmental protected area, the Guariroba River Basin, Midwestern Brazil. We measured rainfall and water discharge throughout 2012 and 2016. During the same period, soil and water conservation practices were performed in the basin, such as: building level terraces and riparian vegetation recovery. We separated streamflow into baseflow and direct runoff, then we evaluted the baseflow index that indicated that groundwater significantly contributes to total flow. Therefore, to investigate the effects on streamflow, we performed a trend analysis in the baseflow time series using the Mann-Kendall test. In addition, we analysed the efficiency of soil erosion regulation practices over time, considering the total payment and the trends found in the baseflow. Whereas precipitation records present a decreasing trend (1 mm month-1), baseflow tends to increase by 0.018 m3 s-1 in the same period. Our findings show that soil conservation practices performed in the basin increase baseflow and also provide a better resilience to endure extreme events such as drought based on an increase in forest areas and soil conservation practices such as level terrace.