Investigating The Efficiency Of The VPR And COFFE Area Models In Predicting The Layout Area Of FPGA Lookup Tables

A single tile in a mesh-based FPGA includes both the routing block and the logic block. The area estimate of a tile in an FPGA is used to determine the physical length of an FPGA’s routing segments. An estimate of the physical length of the routing segments is needed in order to accurately assess the performance of a proposed FPGA architecture. The VPR (Versatile Place and Route) and the COFFE (Circuit Optimization for FPGA Exploration) tools are widely used meshbased FPGA exploration environments. These tools map, place, and route benchmark circuits on FPGA architectures. Subsequently, based on area and delay measurements, the best architectural parameters of an FPGA are decided. The area models of the VPR and COFEE tools take only transistor size as input to estimate the area of a circuit. Realistically, the layout area of a circuit depends on both the transistor size and the number of metal layers that are available to route the circuit. This work measures the effect of the number of metal layers that are available for routing on FPGA layout area through a series of carefully laid out 4-LUTs (4-input Lookup Tables). Based on measured results, a correction factor for the COFFE area equation is determined. The correction factor is a function of both the transistor drive strength and the number of metal layers that are available for routing. Consequently, a new area estimation equation, that is based on the COFFE area model, is determined. The proposed area equation takes into consideration the effect of both the transistor drive strength and the number of metal layers that are available for routing on layout area. The area prediction error of the proposed area equation is significantly less than the area prediction errors of the VPR and COFFE area models.

Investigating The Efficiency Of The VPR And COFFE Area Models In Predicting The Layout Area Of FPGA Lookup Tables

A single tile in a mesh-based FPGA includes both the routing block and the logic block. The area estimate of a tile in an FPGA is used to determine the physical length of an FPGA’s routing segments. An estimate of the physical length of the routing segments is needed in order to accurately assess the performance of a proposed FPGA architecture. The VPR (Versatile Place and Route) and the COFFE (Circuit Optimization for FPGA Exploration) tools are widely used meshbased FPGA exploration environments. These tools map, place, and route benchmark circuits on FPGA architectures. Subsequently, based on area and delay measurements, the best architectural parameters of an FPGA are decided. The area models of the VPR and COFEE tools take only transistor size as input to estimate the area of a circuit. Realistically, the layout area of a circuit depends on both the transistor size and the number of metal layers that are available to route the circuit. This work measures the effect of the number of metal layers that are available for routing on FPGA layout area through a series of carefully laid out 4-LUTs (4-input Lookup Tables). Based on measured results, a correction factor for the COFFE area equation is determined. The correction factor is a function of both the transistor drive strength and the number of metal layers that are available for routing. Consequently, a new area estimation equation, that is based on the COFFE area model, is determined. The proposed area equation takes into consideration the effect of both the transistor drive strength and the number of metal layers that are available for routing on layout area. The area prediction error of the proposed area equation is significantly less than the area prediction errors of the VPR and COFFE area models.

Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning

Many nations are challenged by landscape fires. A confident knowledge of the area and distribution of burning is crucial to monitor these fires and to assess how they might best be reduced. Given the differences that arise using different detection approaches, and the uncertainties surrounding burned-area estimates, their relative merits require evaluation. Here we propose, illustrate, and examine one promising approach for Indonesia where recurring forest and peatland fires have become an international crisis. Drawing on Sentinel-2 satellite time-series analysis, we present and validate new 2019 burned-area estimates for Indonesia. The corresponding burned-area map is available at https://doi.org/10.5281/zenodo.4551243 (Gaveau et al., 2021a). We show that >3.11 million hectares (Mha) burned in 2019. This burned-area extent is double the Landsat-derived official estimate of 1.64 Mha from the Indonesian Ministry of Environment and Forestry and 50 % more that the MODIS MCD64A1 burned-area estimate of 2.03 Mha. Though we observed proportionally less peatland burning (31 % vs. 39 % and 40 % for the official and MCD64A1 products, respectively), in absolute terms we still observed a greater area of peatland affected (0.96 Mha) than the official estimate (0.64 Mha). This new burned-area dataset has greater reliability than these alternatives, attaining a user accuracy of 97.9 % (CI: 97.1 %–98.8 %) compared to 95.1 % (CI: 93.5 %–96.7 %) and 76 % (CI: 73.3 %–78.7 %), respectively. It omits fewer burned areas, particularly smaller- (<100 ha) to intermediate-sized (100–1000 ha) burns, attaining a producer accuracy of 75.6 % (CI: 68.3 %–83.0 %) compared to 49.5 % (CI: 42.5 %–56.6 %) and 53.1 % (CI: 45.8 %–60.5 %), respectively. The frequency–area distribution of the Sentinel-2 burn scars follows the apparent fractal-like power law or Pareto pattern often reported in other fire studies, suggesting good detection over several magnitudes of scale. Our relatively accurate estimates have important implications for carbon-emission calculations from forest and peatland fires in Indonesia.

High-resolution global map of smallholder and industrial closed-canopy oil palm plantations

Oil seed crops, especially oil palm, are among the most rapidly expanding agricultural land uses, and their expansion is known to cause significant environmental damage. Accordingly, these crops often feature in public and policy debates which are hampered or biased by a lack of accurate information on environmental impacts. In particular, the lack of accurate global crop maps remains a concern. Recent advances in deep-learning and remotely sensed data access make it possible to address this gap. We present a map of closed-canopy oil palm (Elaeis guineensis) plantations by typology (industrial versus smallholder plantations) at the global scale and with unprecedented detail (10 m resolution) for the year 2019. The DeepLabv3+ model, a convolutional neural network (CNN) for semantic segmentation, was trained to classify Sentinel-1 and Sentinel-2 images onto an oil palm land cover map. The characteristic backscatter response of closed-canopy oil palm stands in Sentinel-1 and the ability of CNN to learn spatial patterns, such as the harvest road networks, allowed the distinction between industrial and smallholder plantations globally (overall accuracy =98.52±0.20 %), outperforming the accuracy of existing regional oil palm datasets that used conventional machine-learning algorithms. The user's accuracy, reflecting commission error, in industrial and smallholders was 88.22 ± 2.73 % and 76.56 ± 4.53 %, and the producer's accuracy, reflecting omission error, was 75.78 ± 3.55 % and 86.92 ± 5.12 %, respectively. The global oil palm layer reveals that closed-canopy oil palm plantations are found in 49 countries, covering a mapped area of 19.60 Mha; the area estimate was 21.00 ± 0.42 Mha (72.7 % industrial and 27.3 % smallholder plantations). Southeast Asia ranks as the main producing region with an oil palm area estimate of 18.69 ± 0.33 Mha or 89 % of global closed-canopy plantations. Our analysis confirms significant regional variation in the ratio of industrial versus smallholder growers, but it also confirms that, from a typical land development perspective, large areas of legally defined smallholder oil palm resemble industrial-scale plantings. Since our study identified only closed-canopy oil palm stands, our area estimate was lower than the harvested area reported by the Food and Agriculture Organization (FAO), particularly in West Africa, due to the omission of young and sparse oil palm stands, oil palm in nonhomogeneous settings, and semi-wild oil palm plantations. An accurate global map of planted oil palm can help to shape the ongoing debate about the environmental impacts of oil seed crop expansion, especially if other crops can be mapped to the same level of accuracy. As our model can be regularly rerun as new images become available, it can be used to monitor the expansion of the crop in monocultural settings. The global oil palm layer for the second half of 2019 at a spatial resolution of 10 m can be found at https://doi.org/10.5281/zenodo.4473715 (Descals et al., 2021).

Regression models for prediction of leaf area in purple ipe [Tabebuia impetiginosa (Mart.)]

Besides its medicinal and ornamental use, Tabebuia impetiginosa is also very economically important. The achievement of accurate and easy-to-perform tools to determine its leaf area is fundamental for understanding the interaction between the plant and the environment. The objective of this work was to obtain regression equations by using several models that use allometric measurements of the fifth leaflet and to select the most accurate one to determine the leaf area of composite leaves of Tabebuia impetiginosa Mart. in a non-destructive way. By using the dimensions of the fifth leaflet such as - length (LFL in cm), maximum width (WFL in cm) and the product between LFL and WFL (LWFL) of leaf limb, the equations were estimated for linear, quadratic, potential and exponential linear models. The results showed that the determination of leaf area could be performed with excellent precision for leaves of different sizes of this species, using the product of the measurements of length and width of the fifth leaflet. The equation that best expresses the leaf area estimate of the composite leaf of Tabebuia impetiginosa is ELACL = 8.7772 + 2.3840 (LWFL).

Leaf blade area estimate of Digitaria pentzii under different cutting Heights

Pasture studies require information on leaf area, as it is one of the main parameters for evaluation of plant growth. Thus, the objective of this study was to estimate the leaf blade area of pangolão grass (Digitaria pentzii Stent.) using non-destructive methods by regression model analysis. The experimental design consisted of randomized blocks, with three cutting heights (10, 15, and 20 cm) and four replications. Three hundred leaf blades of pangolão grass were randomly collected, and their respective lengths (L) and widths (W) determined using a digital caliper. The leaf blade area of pangolão grass was estimated by the gravimetric method, using linear and power regression models to explain the leaf blade area as a function of the product of L and maximum W. The real leaf blade area presented an average value of 18.64 cm2, ranging from 4.29 to 45.95 cm2. The leaf blade area of pangolão grass, regardless of cutting height, was estimated with greater accuracy by the power model. The power model, Ŷ=LW1.007, can be used to estimate the leaf blade area of pangolão grass based on leaf blade L and W values.

Rectangular Spiral Inspired Approach for Estimating Area of Solar Photovoltaic Plants in India

In pursuit of achieving its clean energy goals, India is moving aggressively towards establishing large solar plants. Land being a finite resource in a densely populated country like India, an approach for planning for these plants aiming towards better utilisation of available land resource is certainly of interest. There is merit in looking at a design approach which aides planning by offering high packing density leading to reduced land utilisation. The objective of this paper is to propose a method which can provide the rational area estimate of a plant factoring sizing considerations from electrical, maintenance, and shading aspects. The idea for the plant design is inspired by the number pattern illustrated in the Ulam spiral. From the perspective of planning, the proposed approach aims to provide an area estimate which could set the boundary conditions in term of realistic potential estimation and minimum land area required. Further, the paper provides insights with respect to land area requirements across the latitudinal spread for India. Also, it provides an estimate of the solar power potential for ground mounted utility scale plants in India.