Agricultural productivity is subject to various stressors, including abiotic and biotic threats, many of which are exacerbated by a changing climate, thereby affecting long-term sustainability. The productivity of tree crops such as almond orchards, is particularly complex. To understand and mitigate these threats requires a collection of multi-layer large data sets, and advanced analytics is also critical to integrate these highly heterogeneous datasets to generate insights about the key constraints on the yields at tree and field scales. Here we used a machine learning approach to investigate the determinants of almond yield variation in California's almond orchards, based on a unique 10-year dataset of field measurements of light interception and almond yield along with meteorological data. We found that overall the maximum almond yield was highly dependent on light interception, e.g., with each one percent increase in light interception resulting in an increase of 57.9 lbs/acre in the potential yield. Light interception was highest for mature sites with higher long term mean spring incoming solar radiation (SRAD), and lowest for younger orchards when March maximum temperature was lower than 19°C. However, at any given level of light interception, actual yield often falls significantly below full yield potential, driven mostly by tree age, temperature profiles in June and winter, summer mean daily maximum vapor pressure deficit (VPDmax), and SRAD. Utilizing a full random forest model, 82% (±1%) of yield variation could be explained when using a sixfold cross validation, with a RMSE of 480 ± 9 lbs/acre. When excluding light interception from the predictors, overall orchard characteristics (such as age, location, and tree density) and inclusive meteorological variables could still explain 78% of yield variation. The model analysis also showed that warmer winter conditions often limited mature orchards from reaching maximum yield potential and summer VPDmax beyond 40 hPa significantly limited the yield. Our findings through the machine learning approach improved our understanding of the complex interaction between climate, canopy light interception, and almond nut production, and demonstrated a relatively robust predictability of almond yield. This will ultimately benefit data-driven climate adaptation and orchard nutrient management approaches.
There is currently no unified remote sensing system available that can simultaneously produce images with fine spatial, temporal, and spectral resolutions. This letter proposes a unified spatiotemporal spectral blending model using Landsat Enhanced Thematic Mapper Plus and Moderate Resolution Imaging Spectroradiometer images to predict synthetic daily Landsat-like data with a 15-m resolution. The results of tests using both simulated and actual data over the Poyang Lake Nature Reserve show that the model can accurately capture the general trend of changes for the predicted period and can enhance the spatial resolution of the data, while at the same time preserving the original spectral information. The proposed model is also applied to improve land cover classification accuracy. The application in Wuhan, Hubei Province shows that the overall classification accuracy is markedly improved. With the integration of dense temporal characteristics, the user and producer accuracies for land cover types are also improved.
This paper proposes a novel technique, known as the variable-height timing window (TW), for rejecting noise in an active source detection system. The basic principle of a TW and a methodology of using a microprocessor to dynamically adjust the height of the TW are presented. This variable-height TW technique has been applied to a laser-based detection system (LBDS) for detecting vehicle information on highways. Field-test results of the LBDS showed that the adaptive nature of the proposed TW approach can effectively reject various types of noise under different environmental conditions. This variable-height TW technique can also be used to remove out-of-window noise and suppress the effect of in-window noise in various field environments in which the signal/noise (S/N) ratio is variable.
Enlightened by stream medium technology,this paper proposes a method handling 3D terrain data in streaming way for real-time interaction based on network.At the beginning,a stable output depending on dynamic multi-resolution simplification algorithm is applied to get a series of fix-sized non-uniform elevation matrices.A streaming code based on DCT for terrain data is then introduced.The non-uniform elevation matrix is transformed by the code to frequency domain and results in a coefficient matrix.Then the result coefficient matrix is coded in a progressive format.Finally,together with QoS control,which is based on total system performance such as client CPU/rending capability and network traffic factors,the real-time interaction to massive terrain data through network is achieved.This stream processing method for terrain data also enriches traditional terrain interaction system by bringing features such as live or recorded interaction broadcasting,live interaction recording and editing.A sample program demonstrates different decode quantity of terrain scene for corresponding total system performance to achieve real-time interaction through network.
Abstract Timely and accurate population mapping plays an essential role in a wide range of critical applications. Benefiting from the emergence of multi-source geospatial datasets and the development of spatial statistics and machine learning, multi-scale population mapping with high temporal resolutions has been made possible. However, the over-complex models and the strict data requirement resulting from the constant quest for increased accuracy pose challenges to the repeatability of many population spatialization frameworks. Therefore, in this study, using limited publicly available datasets and an automatic ensemble learning model (AutoGluon), we presented an efficient framework to simplify the model training and prediction process. The proposed framework was applied to estimate county-level population density in China and received a good result with an r 2 of 0.974 and an RMSD of 427.61, which is better than the performances of current mainstream population mapping frameworks in terms of estimation accuracy. Furthermore, the derived monthly population maps and the revealed spatial pattern of population dynamics in China are consistent with earlier studies, suggesting the robustness of the proposed framework in cross-time mapping. To our best knowledge, this study is the first work to apply AutoGluon in population mapping, and the framework’s efficient and automated modeling capabilities will contribute to larger-scale and finer spatial-temporal population spatialization studies.
A simple mathematical model of a passive solar house is proposed. The physical model is the house integrated with a color-changed solar wall. Steady state heat transfer equations were set up to determine the temperatures of surfaces and air by using a thermal resistance network. The equations were solved using a Matlab procedure. Satisfactory correlation was obtained with experimental data. The optimization methods for thermal performance of passive solar houses will be presented in further work.
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