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Plant synthetic biology holds great promise for engineering plants to meet future demands. Genetic circuits are being designed, built and tested in plants to demonstrate the proof of concept. However, developing these components in monocots, which the world relies on for grain, lags behind dicot models, such as Arabidopsis thaliana and Nicotiana benthamiana. Here, we show the successful adaptation of a ligand‐inducible sensor to activate an endogenous anthocyanin pathway in the C4 monocot model Setaria viridis. We identify two transcription factors that can be expressed as a single transcript that are sufficient to induce endogenous anthocyanin production in S. viridis protoplasts and whole plants in a constitutive or ligand‐inducible manner. We also test multiple ligands to overcome physical barriers to ligand uptake, identifying triamcinolone acetonide (TA) as a highly potent inducer of this system. Using hyperspectral imaging and a discriminative target characterisation method in a near‐remote configuration, we can non‐destructively detect anthocyanin production in leaves in response to ligands. This work demonstrates the use of inducible expression systems in monocots to manipulate endogenous pigmentation production for remote detection. Applying inducible anthocyanin production coupled with sensitive detection algorithms could enable crop plants to report on the status of field contamination or detect undesirable chemicals impacting agriculture, ushering in an era of agriculture‐based sensor systems.

Deforestation and forest degradation are major drivers of global environmental change and tropical forests are subjected to unprecedented pressures from both. For most tropical zones, deforestation rates are averaged across entire countries, often without highlighting regional differentiation. There are also very few estimates of forest degradation, either averaged or localized for the tropics. We quantified regional and country‐wide changes in deforestation and forest degradation rates for Madagascar from Landsat temporal data (in two intervals, 1994–2002 and 2002–2014). To our knowledge, this is the first country‐wide estimate of forest degradation for Madagascar. We also performed an intensity analysis to categorize the magnitude and speed of transitions between forest, vegetation matrix, cultivated land and exposed surface. We found significant regional heterogeneity in deforestation and forest degradation. Deforestation rates decreased annually in lowland evergreen moist forest by −0.24% and in all other vegetation zones. Forest degradation rates had annual increases in the same period in lowland evergreen moist forest (0.09%), littoral forest (0.06%) but decreased in medium altitude moist evergreen forest (−0.25%), dry deciduous forest (−0.23%) and scelrophyllous woodland (−0.61%) in the same period. Despite these regional differences, higher rates of deforestation and forest degradation were consistently driven by rapid and large‐sized conversions of largely intact forest to cultivated lands and exposed surfaces, most of which occurred between 1994 and 2002. These results suggest that while targeted conservation projects may have reduced forest degradation rates in some areas (e.g. medium altitude moist evergreen forest), the drivers of land cover change remain intense in relatively neglected regions. We advocate a more balanced approach to future conservation initiatives, one recognizing that deforestation and forest degradation, particularly in tropical Africa, are often driven by region‐specific conditions and therefore require conservation policies tailored for local conditions. Deforestation and forest degradation are major drivers of land cover change in tropical regions. Yet, regional differences exist. To address these nuances, sub‐pixel analysis was applied to discriminate forest loss at regional scales, especially those caused by forest degradation in a biodiversity hotspot. The results obtained can serve as a guide towards meaningful conservation interventions.

Quantitative evaluation of human motion from image data requires both high geometric precision and mathematical interpretability. To address the limitations of pixel-level posture analysis and empirical performance scoring, this study proposes a geometry-driven quantitative modeling framework for image-based motion evaluation. Sub-pixel edge detection based on quadratic polynomial interpolation is first employed to construct a precise continuous representation of limb contours from image sequences. By abstracting the human arm as a spatial rigid-body system, posture evaluation is reformulated as an optimization problem governed by geometric constraints and physical principles. An optimal swing trajectory is obtained by minimizing the total kinetic energy of the system, which is solved numerically using Newton’s iterative method, avoiding the explicit solution of highly coupled inverse kinematics. To further analyze the contribution of multiple performance-related variables within a unified quantitative framework, a hybrid feature attribution strategy integrating Random Forest, XGBoost, and LightGBM is introduced. The proposed mixed feature mining approach reduces model dependency and enhances the robustness of factor importance ranking. The effectiveness of the proposed framework is validated using image data collected from a cloud-based table tennis classroom. The experimental results demonstrate that the geometry-driven modeling approach provides stable, interpretable, and discriminative evaluation outcomes, indicating its potential applicability to broader image-based human motion analysis tasks.

As applied to the conditions of wildfires in Siberia, remote sensing is adapted to record the radiation power from the active fire zone in the range of 3.929–3.989 μm (Terra/MODIS data). The limits of variation of the detected values ​​of heat radiation are evaluated. Sporadic peaks that exceed the mean value of heat radiation in the fire field by a value of 2.5σ were correlated with high-intensity fires, including crown fires. The probability of remote fire detection in crown stage was no less than 65%. The quantitative dependence of the Fire Radiative Power (FRP) on the area of ​​the active zone was determined using a subpixel analysis. The fraction of forest fires in Siberia with areas of extreme heat radiation is shown to be 5.5 ± 1.2% of the total wildfires. The total area of ​​high-intensity wildfires including crown fires is at least 8.5% of the average annual wildfire area and reaches values ​​of 15–25% during extreme fire seasons.

AbstractIn this work, an improved approach of the pixel-based analysis of the Terra/MODIS imagery is proposed. The approach allows us to improve the accuracy in estimating characteristics of the combustion zone when detecting thermal anomalies. The investigation is carried out based on the imagery of active vegetation fires in Siberian forests by the MODIS radiometer in the spectral ranges of 3930 to 3990 and 10 780 to 11 280 μm (bands 21 and 31, respectively). It is proposed to describe the approximation of the temperature profile of the fire front using an exponential function. Using the nonuniform approximation of the temperature distribution on the surface in the vicinity of the active combustion zone allows us to determine the portion of the active pixel of the Terra/MODIS image with the given temperature excess over the background temperature in it. This improves the accuracy in extracting active combustion zones and classifying the heat release rate at the subpixel level. This approach is applicable to monitoring fire development phases in the near real time mode.

A study was carried out to estimate the actual evapotranspiration (ET) over a 1074 km2 of the humid area of Perak State (Malaysia), where water and evaporation cycle deeply influences the climate, natural resources and human living aspects. Images from both Terra and Aqua platforms of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor were used for ET estimation by employing the Surface Energy Balance Algorithm for Land (SEBAL) model. As a part of the accuracy assessment process, in-situ measurements on soil temperature and reference ET (ET0) were recorded at the time of satellite overpass. In order to enhance the accuracy of the generated ET maps, MODIS images were subjected to sub-pixel analysis by assigning weights for different land surface cover (urban, agriculture and multi-surface areas) reflections. The weighting process was achieved by integrating ET from pure pixels with the respective site-specific ET0 of each land cover. The enhanced SEBAL model estimated ET exhibited a good correlation with the in-situ measured Penman-Montieth ET0, with R2 values for the Aqua and the Terra platforms of 0.67 and 0.73, respectively. However, the correlation of the non-enhanced ET maps resulted in R2 values of 0.61 and 0.68 for the Aqua and the Terra platforms, respectively. Hence, the results of this study revealed the feasibility of employing the sub-pixel analysis method for an accurate estimation of ET over large areas.

Laser stripe center extraction is a key step for the profile measurement of line structured light sensors (LSLS). To accurately obtain the center coordinates at sub-pixel level, an improved gray-gravity method (IGGM) was proposed. Firstly, the center points of the stripe were computed using the gray-gravity method (GGM) for all columns of the image. By fitting these points using the moving least squares algorithm, the tangential vector, the normal vector and the radius of curvature can be robustly obtained. One rectangular region could be defined around each of the center points. Its two sides that are parallel to the tangential vector could alter their lengths according to the radius of the curvature. After that, the coordinate for each center point was recalculated within the rectangular region and in the direction of the normal vector. The center uncertainty was also analyzed based on the Monte Carlo method. The obtained experimental results indicate that the IGGM is suitable for both the smooth stripes and the ones with sharp corners. The high accuracy center points can be obtained at a relatively low computation cost. The measured results of the stairs and the screw surface further demonstrate the effectiveness of the method.

To meet the requirements of high accuracy in image edge localization and strong noise resistance for computer vision calibration and precise measurement, an improved Zernike moment sub-pixel high-precision measurement method for circular hole-like workpieces is proposed. Firstly, the Canny operator is used as a coarse edge detection algorithm, with the traditional Gaussian filter in the Canny operator replaced by an improved Laplacian edge-adaptive median filter. This approach demonstrates improved edge preservation compared to traditional and adaptive median filtering, especially under high-concentration noise. Then, a sub-pixel edge detection algorithm is applied to refine the edges, thus enhancing the edge localization accuracy. An improved Zernike moment sub-pixel detection algorithm is employed for precise edge point detection. The improved algorithm selects a Zernike moment parameter template with higher detection accuracy. Finally, the inner and outer diameters of the circular hole-like part are measured by fitting the profile using the least squares method. Experimental results on several different workpieces demonstrate that the proposed algorithm achieves higher accuracy than the traditional Zernike moment sub-pixel method, with an error reduction of 75.1%, meeting the precision requirements in modern industrial part manufacturing processes.

High-efficiency and high-accuracy deformation analysis using digital image correlation (DIC) has become increasingly important in recent years, considering the ongoing trend of using higher resolution digital cameras and common requirement of processing a large sequence of images recorded in a dynamic testing. In this work, to eliminate the redundant computations involved in conventional DIC method using forward additive matching strategy and classic Newton–Raphson (FA-NR) algorithm without sacrificing its sub-pixel registration accuracy, we proposed an equivalent but more efficient DIC method by combining inverse compositional matching strategy and Gauss-Newton (IC-GN) algorithm for fast, robust and accurate full-field displacement measurement. To this purpose, first, an efficient IC-GN algorithm, without the need of re-evaluating and inverting Hessian matrix in each iteration, is introduced to optimize the robust zero-mean normalized sum of squared difference (ZNSSD) criterion to determine the desired deformation parameters of each interrogated subset. Then, an improved reliability-guided displacement tracking strategy is employed to achieve further speed advantage by automatically providing accurate and complete initial guess of deformation for the IC-GN algorithm implemented on each calculation point. Finally, an easy-to-implement interpolation coefficient look-up table approach is employed to avoid the repeated calculation of bicubic interpolation at sub-pixel locations. With the above improvements, redundant calculations involved in various procedures (i.e. initial guess of deformation, sub-pixel displacement registration and sub-pixel intensity interpolation) of conventional DIC method are entirely eliminated. The registration accuracy and computational efficiency of the proposed DIC method are carefully tested using numerical experiments and real experimental images. Experimental results verify that the proposed DIC method using IC-GN algorithm and the existing DIC method using classic FA-NR algorithm generate similar results, but the former is about three to five times faster. The proposed reliability-guided IC-GN algorithm is expected to be a new standard full-field displacement tracking algorithm in DIC.

The limits of detection for earthquake surface deformation in the spatial domain have improved with advances in remote sensing imagery data availability, resolution, and analysis. Sub‐pixel correlation and digital elevation model (DEM) differencing from sub‐meter, earthquake‐spanning satellite optical imagery has enhanced surface rupture mapping and deformation measurements. However, knowledge of measurement accuracy and uncertainty is limited. To address this, we construct orthophotos and digital elevation models (DEMs) from repeat high resolution (∼0.5 m) satellite optical imagery along two sections of the Garlock fault, California with clear fault geomorphology and differing land cover. We deform later sets of DEMs and images with synthetic earthquakes containing both diffuse and discrete horizontal and vertical displacements. Sub‐pixel image correlation and DEM differencing demonstrate how vegetation degrades recovered displacement accuracy. In barren land cover, horizontal displacements are detectable to an expected ∼1/10th‐pixel size. With shrubs, trees, and grass, detectable displacements increase to >1/2‐pixel size, and filtering results by correlation score and using elevation values as input rather than image values improves accuracy. Vertical displacement detection thresholds remain lower in vegetation, at >1‐pixel size. Higher slope angles degrade displacement recovery, worsened by vegetation. Diminishing seasonal separation improves accuracy over vegetated regions, though not to the level achieved in barren environments. These results will inform research and operational efforts on the utility of high resolution satellite optical imagery for detecting deformation in varied land cover. Furthermore, they reveal where alternative measurements, such as from LiDAR or radar interferometry, are required to mitigate the effects of vegetation and capture fine‐scale crustal deformation. Plain Language Summary High resolution (<1 m/pixel) satellite images taken before and after earthquakes are used to measure ground movement and improve our understanding of earthquake processes. However, we lack formal accuracy and uncertainty estimates of the measurements derived from correlating these images. Here, we impose synthetic earthquakes on real satellite images of the Garlock fault in southern California to test the detection limits of image correlation in regions with varied land cover, including bare ground, grassland, and forest. We find that vegetation obscures measurements of ground deformation, increasing the detection limits by over fivefold from areas without vegetation. Using images from the same year and season can mitigate the effects of vegetation, but not to the level achieved over bare land. The results inform future research and operational efforts on the capabilities of optical image correlation for measuring earthquakes, and highlight where alternative measurements may be needed to mitigate the effects of vegetation and reveal fine‐scale ground movement. Key Points Vegetation hinders optical image correlation efficacy for crustal deformation measurements Displacement detection thresholds vary from ∼0.1 to 3 pixel width depending on measured component, slope, correlation score, and land cover Prior knowledge of earthquake displacement direction, land cover, and slope streamline imagery processing decisions in response situations