Spatial Field Research Articles

Urban explosion and hotspots of forest loss in western Himalaya: Mapping land use/cover change trends since 1975

The Western Himalayan Region (WHR) is under constant threat from Land Use and Land Cover Change (LULCC) brought on by various socioeconomic activities such as urbanization, agricultural expansion, and population growth. Detailed research into the scope of these changes is necessary to comprehend the challenges they represent. This study aimed at (1) examining and quantifying spatiotemporal patterns of LULCC from 1975 to 2015, (2) mapping the hotspots of forest loss, and (3) modelling future LULCC for the year 2055 using medium spatial resolution multispectral satellite imagery (MSS, TM, LISS-I, LISS-III, AWiFS), field data, visual interpretation and logistic regression model in the WHR. Logistic regression integrated various drivers of LULCC, including climatic, topographic, and socio-economic factors and past LULCC trends to forecast potential LULC in the WHR by 2055. The results highlight an outrageous 184% increase in regional urbanization. Expansion of urban and cropland (12%) led to deforestation which reduced the area of natural habitats, specifically forests (−11%), water bodies (−8%) and scrubland (−6%). A significant rise in barren land by 30% was noted, while there was a decrease of 20% in glacier/snow melt. The future projection for 2055 anticipates continuing current trends, emphasizing an intensified urban expansion of 63% and an approximate 9% reduction in overall forest coverage. The accuracy of the predicted 2015 LULC map was assessed using ROC/AUC (Receiver Operating Characteristic/Area under Curve) which was found to be 0.79.

Calibration-free parallel transmission of the cervical, thoracic, and lumbar spinal cord at 7T.

To address the limitations of spinal cord imaging at ultra-high field (UHF) due to time-consuming parallel transmit (pTx) adjustments. This study introduces calibration-free offline computed universal shim modes that can be applied seamlessly for different pTx RF coils and spinal cord target regions, substantially enhancing spinal cord imaging efficiency at UHF. A library of channel-wise relative maps for the cervical spinal cord (six datasets) and thoracic and lumbar spinal cord (nine datasets) was constructed to optimize transmit homogeneity and efficiency for these regions. A tailored B0 shim was optimized for the cervical spine to enhance spatial magnetic field homogeneity further. The performance of the universal shims was validated using absolute saturation based mapping and high-resolution 2D and 3D multi-echo gradient-recalled echo (GRE) data to assess the image quality. The proposed universal shims demonstrated a 50% improvement in efficiency compared to the default (zero phase) shim mode. homogeneity was also improved by 20%. The optimized universal shims achieved performance comparable to subject-specific pTx adjustments, while eliminating the need for lengthy pTx calibration times, saving about 10 min per experiment. The development of universal shims represents a significant advance by eliminating time-consuming subject-specific pTx adjustments. This approach is expected to make UHF spinal cord imaging more accessible and user-friendly, particularly for non-pTx experts.

A study of pedestrian wayfinding behavior based on desktop VR considering both spatial knowledge and visual information

Understanding pedestrian wayfinding behavior is crucial for traffic management and building design. The use of virtual reality technology presents an efficient approach for investigating pedestrian wayfinding behavior in large public spaces, offering numerous advantages for data collection. However, the impact of different scenario dimensions on pedestrian wayfinding behavior in large public spaces remains unclear. Additionally, the selection of virtual experiment scenario dimensions currently relies primarily on researchers’ experience and practical conditions, lacking sufficient evidence to support their rational. Another challenge is the limited focus on spatial knowledge’s effect on wayfinding behavior, with insufficient analysis of the utility of pedestrian visual information and a lack of precise methods to quantify visual field information accurately. This study addresses these gaps by incorporating spatial knowledge at multiple scales and pedestrian visual field information as influencing factors in the analysis of wayfinding behavior. Furthermore, it distinguishes between three-dimensional and two-dimensional scenarios to compare the impact of dimensional differences on pedestrian wayfinding behavior. By analyzing behavior data from non-immersive wayfinding experiments, this research employs statistical analysis methods and a deep learning framework to derive results regarding the factors influencing wayfinding behavior. The findings demonstrate that considering both spatial and visual field information effectively enhances the predictive ability of the wayfinding model. Additionally, dimensional differences significantly influence the pedestrian wayfinding process. These results offer empirical evidence to guide researchers in selecting experimental scenarios of pedestrian behavior and provide insights for public space layout, signage design, and improving pedestrian efficiency.

Effects of the spatial resolution of the Virtual Epileptic Patient on the identification of epileptogenic networks

Abstract Digital twins play an increasing role in clinical decision making. This study evaluates a digital brain twin approach in presurgical evaluation, the Virtual Epileptic Patient (VEP), which estimates the epileptogenic zone in patients with drug-resistant epilepsy. We built the personalized digital brain twins of 14 patients and a series of synthetic dataset by considering different spatial configurations of the epileptogenic and/or propagation zone networks (EZN and PZN, respectively). Brain source signals were simulated with a high spatial resolution neural field model (NFM) composed of 81942 nodes, embedding both long-range (between brain regions) and short-range (within brain regions) coupling. Brain signals were then projected to stereotactic electroencephalographic (SEEG) contacts with an accurate forward solution. An inversion procedure based on a low spatial resolution neural mass model (NMM) composed of 162 nodes was applied to estimate the excitability of each region in each simulation. The ensuing estimated EZN/PZN was compared to the simulated ground truth by means of classification metrics. Overall, we observed correct but degraded performance when using an NMM to estimate the EZN from data simulated with an NFM, which was significant for the simplest spatial configurations. We quantified the reduced performance and demonstrated that the oversimplification of the forward problem is its principal cause. We showed that the absence of local coupling in the NMM affects the inversion process by an overestimation of the excitability, representing a significant clinical impact when using this procedure in the context of presurgical planning. In conclusion, this study highlighted the importance to shift from an NMM towards a full NFM modeling approach for the estimation of EZN, with a particularly relevant need when considering the most complex clinical cases.

Non-telecentric photoacoustic microscopy for multi-scale imaging.

Optical-resolution photoacoustic microscopy (OR-PAM) excels in precisely imaging a biological tissue based on absorption contrast. However, existing OR-PAMs are confined by fixed compromises between spatial resolution and field of view (FOV), preventing the integration of large FOV and local high-resolution within one system. Here, we present a non-telecentric OR-PAM (nTC-PAM) that empowers efficient adaptation of FOV and spatial resolution to match the multi-scale requirement of diverse biological imaging. Our method allows for a large-scale transformation in FOV and even surpassing the nominal FOV of the objective with minimal marginal degradation of the lateral resolution. We demonstrate the advantage of nTC-PAM through multi-scale imaging of the leaf phantom, mouse ear, and cortex. The results reveal that nTC-PAM can switch the FOV and spatial resolution to meet the requirements of different biological tissues, such as large-scale imaging of the whole cerebral cortex and high-resolution imaging of microvascular structures in local brain regions.

Measurement Of Spatial-Temporal Waves In The Laboratory Using Computer Vision Technolog

Given the complexity of ocean wave phenomena, physical modeling continues to be essential in various engineering and research applications. Wave height, a characteristic measured in nearly all projects, is commonly assessed using resistance/capacitance wave gauges. However, a significant limitation of these traditional wave gauges is their point-based nature, which restricts their ability to describe detailed spatial wave characteristics, especially during wave nonlinearity or when waveforms change. Furthermore, to reconstruct the 3D wave field, an array of wave gauges is often employed alongside an appropriate interpolation algorithm. Additionally, wave gauges may alter flow/wave conditions in specific scenarios. To address these limitations, various non-intrusive measurement techniques have been proposed. Gomit et al. (2022) classified these into stereoscopic, projection, and light-based methods, including LiDAR (Blenkinsopp et al., 2012), RGB-D cameras (Martínez-Aranda et al., 2018), and binocular stereo vision (Li et al., 2022). However, their applicability is constrained by factors such as optical setup complexity and device cost. With the rapid advancement of computer vision technology, it is possible to improve these issues and better describe temporal and spatial wave field variations using such techniques. This conference contribution details the reconstruction of the temporal and spatial 3D free water surface using photogrammetry techniques applied to images from consumer webcams. It also compares these results with measurements obtained from a wave gauge.

FPM-WSI: Fourier ptychographic whole slide imaging via feature-domain backdiffraction

Fourier ptychographic microscopy (FPM) theoretically provides a solution to the trade-off between spatial resolution and field of view (FOV), and has promising prospects in digital pathology. However, block reconstruction and then stitching has become an unavoidable procedure for reconstruction of large FOV due to vignetting artifacts. This introduces digital stitching artifacts, as the existing image-domain optimization algorithms are highly sensitive to systematic errors. Such obstacles significantly impede the advancement and practical implementation of FPM, explaining why, despite a decade of development, FPM has not gained widespread recognition in the field of biomedicine. We report a feature-domain FPM (FD-FPM) based on the structure-aware forward model to realize stitching-free, full-FOV reconstruction. The loss function is uniquely formulated in the feature domain of images, which bypasses the troublesome vignetting effect and algorithmic vulnerability via feature-domain backdiffraction. Through massive simulations and experiments, we show that FD-FPM effectively eliminates vignetting artifacts for full-FOV reconstruction, and still achieves impressive reconstructions despite the presence of various systematic errors. We also found it has great potential in recovering the data with a lower spectrum overlapping rate, and in realizing digital refocusing without a prior defocus distance. With FD-FPM, we achieved full-color and high-throughput imaging (4.7 mm diameter FOV, 336 nm resolution in the blue channel) free of blocking-and-stitching procedures on a self-developed Fourier ptychographic microscopy whole slide imaging platform. The reported FD-FPM shows the value of FPM for various experimental circumstances, and offers physical insights useful for the developments of models for other computational imaging techniques. The reported platform demonstrates high-quality, high-speed imaging and low cost, and could find applications in many fields of biomedical research, as well as in clinical applications.

Large-scale flow field super-resolution via local-global fusion convolutional neural networks

Particle image velocimetry (PIV) techniques have a limited field of view of the flow field and can only capture high-resolution flow fields in localized areas. To obtain a larger measurement range, multiple cameras must be used to capture the flow field simultaneously and then stitch the parts together. However, this method can be expensive. We propose the local-global fusion convolutional neural network (LGF-CNN) for reconstructing large-field flow fields with high spatial resolution based only on two flow data types: local small-field high spatial resolution wake velocity fields and global large-field low spatial resolution velocity fields. The core of the model consists of convolutional neural network (CNN) architecture to learn the mapping relationship between the small field of view with high spatial resolution and the large field of view with low spatial resolution. Using the effectively trained LGF-CNN model, we demonstrate its ability to reconstruct high-resolution velocity fields around the circular cylinder. The LGF-CNN is rigorously validated on a number of representative datasets, including simulated data for Reynolds numbers of 200 and 500, as well as experimental data for a Reynolds number of 3.3 × 104 with the steady jet at the rear stagnation point of the cylinder. The results demonstrate the ability of LGF-CNN to generate accurate velocity fields with high spatial resolution, including reliable detection of high-frequency components. The proposed method could reduce the number of cameras required for large-field, high spatial resolution PIV measurements, thereby reducing experimental costs.

A bird droppings flashover analysis method based on the combination of electric field distortion and spatial electric field distribution

To study the flashover of air gap caused by bird droppings shorting air gap and the influence of space electric field change on it, the distortion electric field simulation, space electric field simulation, and related flashover test of transmission line bird droppings falling process were carried out. By analyzing the specific influencing factors of bird droppings flashover fault, it is pointed out that the electric field distortion caused by bird droppings will affect the spatial electric field distribution around the insulator and thus affect the air gap flashover. This study analyzes the specific causes of bird dropping flashover of transmission line insulators, reduces the number of bird damage trips of transmission lines, reduces the cost of operation and maintenance of transmission lines, improves the intrinsic safety of transmission lines, and ensures the safe and reliable operation of power grids.

Bolt loosening monitoring with passive wireless-based smart washers

Ensuring sufficient preload of bolted connections is crucial for maintaining the safety of railway operations. Accidents resulting from loose bolts caused by inadequate preload are a recurring concern. Traditional bolt preload detection methods suffer from reliability issues and bulky equipment requirements. To address these challenges, this study proposes a novel solution utilising passive wireless measurement systems. However, the communication effectiveness of these systems can be significantly impacted by the surrounding metal environment. This research introduces an intelligent clover washer design to mitigate the adverse effects of eddy currents induced by the metal environment. The design incorporates a new clover antenna and washer structure, effectively reducing the influence of the metal environment and improving the communication quality. The proposed design has undergone comprehensive prototyping, simulation and experimental verification. The results demonstrate a significant improvement over the traditional circular antenna and washer combination under similar conditions. Specifically, the sensing distance of the new clover washer is enhanced by 60% and the stable communication distance is improved by 75%. The experimental results highlight the ability of the new clover smart washer to generate a stronger spatial magnetic field and exhibit reduced susceptibility to the metal washer, thereby enhancing communication effectiveness.

Waveform design of radar coincidence imaging radiation field based on image entropy

AbstractRadar coincidence imaging (RCI) is a high‐resolution radar imaging mode which constructs a temporal spatial stochastic radiation field (TSSRF) and uses the correlation between reference signal and echoes for imaging. The correlation between the reference matrix and the echoes is the main factor affecting the imaging performance. In fact, radar target characteristics cause fluctuations in the radar cross section and variations in the scattering intensity of each resolution element. The latter degrades the correlation between the reference matrix and the echoes, seriously affecting the image reconstruction. This paper designs the waveform based on image entropy under fixed transmitting and receiving arrays. The targets with fluctuating scattering intensity at each resolution element are statistically modelled. Simulation results show this approach can improve the imaging performance and reduce the energy dissipation degree of the target grid.

Validation of cardiac image-derived input functions for functional PET quantification

PurposeFunctional PET (fPET) is a novel technique for studying dynamic changes in brain metabolism and neurotransmitter signaling. Accurate quantification of fPET relies on measuring the arterial input function (AIF), traditionally achieved through invasive arterial blood sampling. While non-invasive image-derived input functions (IDIF) offer an alternative, they suffer from limited spatial resolution and field of view. To overcome these issues, we developed and validated a scan protocol for brain fPET utilizing cardiac IDIF, aiming to mitigate known IDIF limitations.MethodsTwenty healthy individuals underwent fPET/MR scans using [18F]FDG or 6-[18F]FDOPA, utilizing bed motion shuttling to capture cardiac IDIF and brain task-induced changes. Arterial and venous blood sampling was used to validate IDIFs. Participants performed a monetary incentive delay task. IDIFs from various blood pools and composites estimated from a linear fit over all IDIF blood pools (3VOI) and further supplemented with venous blood samples (3VOIVB) were compared to the AIF. Quantitative task-specific images from both tracers were compared to assess the performance of each input function to the gold standard.ResultsFor both radiotracer cohorts, moderate to high agreement (r: 0.60–0.89) between IDIFs and AIF for both radiotracer cohorts was observed, with further improvement (r: 0.87–0.93) for composite IDIFs (3VOI and 3VOIVB). Both methods showed equivalent quantitative values and high agreement (r: 0.975–0.998) with AIF-derived measurements.ConclusionOur proposed protocol enables accurate non-invasive estimation of the input function with full quantification of task-specific changes, addressing the limitations of IDIF for brain imaging by sampling larger blood pools over the thorax. These advancements increase applicability to any PET scanner and clinical research setting by reducing experimental complexity and increasing patient comfort.

Detection and monitoring of mining-induced subsidence with Gaofen-3 and Sentinel-1A SAR datasets

ABSTRACT The mining-induced subsidence has caused severe casualties and poses a threat to the safe operation of coal extraction, especially under the roads, buildings and rivers. This alarming situation demands the large-area subsidence detection and the large-gradient deformation monitoring to ensure safety. Although the traditional Interferometric Synthetic Aperture Radar (InSAR) technology can monitor surface deformation in high accuracy, unfortunately, it often fails to recover the sufficient deformation when facing the effects of decorrelation and large gradient displacement. Therefore, this study firstly proposes a Stacking-InSAR and coherence gradient fusion method to detect the mining-induced subsidence over large area. Secondly, the robust offset tracking method is applied to retrieve the large-gradient deformation in Buertai mining site. Both Gaofen-3 and Sentinel-1A Synthetic Aperture Radar (SAR) datasets are acquired over the border of Shaanxi and Inner Mongolia, China, for experimental test. The results show that 179 mining-induced subsidence sites are detected with the proposed method, and the complete spatial temporal deformation fields are obtained by both Gaofen-3 and Sentinel-1A offset tracking method with maximum deformation of 3 m in range direction. This study provides a valuable tool for detecting the fine boundary of mining subsidence and recovering the accurate deformation time series.

Biplane Enhancement Coil for Magnetic Induction Tomography of Cerebral Hemorrhage.

Magnetic Induction Tomography (MIT) is a non-invasive imaging technique used for dynamic monitoring and early screening of cerebral hemorrhage. Currently, there is a significant challenge in cerebral hemorrhage MIT due to weak detection signals, which seriously affects the accuracy of the detection results. To address this issue, a dual-plane enhanced coil was proposed by combining the target field method with consideration of the spatial magnetic field attenuation pattern within the imaging target region. Simulated detection models were constructed using the proposed coil and cylindrical coil as excitation coils, respectively, and simulation imaging tests were conducted using the detection results. The simulation results indicate that compared to the cylindrical coil, the proposed coil enhances the linearity of the magnetic field within the imaging target region by 60.43%. Additionally, it effectively enhances the detection voltage and phase values. The simulation results of hemorrhage detection show that the proposed coil improves the accuracy of hemorrhage detection by 18.26%. It provides more precise detection results, offering a more reliable solution for cerebral hemorrhage localization and detection.

Photodetector array for measuring the spatial–temporal distribution characteristics of electric field in oil–pressboard insulation based on the Kerr electro‐optic effect

AbstractAccurate measurement and effective analysis of the space electric field and charge characteristics within the oil–pressboard insulation structure of converter transformer are essential for achieving precise insulation structure design. Presently, most electric field measurement methods relying on the Kerr electro‐optical effect are limited to single‐point measurements. Given the complexity of converter transformer insulation structures, including their large scale and the use of various materials, existing technologies and findings struggle to provide comprehensive electric field observations within the oil–pressboard region. After leveraging the Kerr electro‐optic effect, a high‐precision 32‐unit photodetector array has been developed to achieve regional measurements of spatial‐temporal electric field in oil–pressboard insulation. The array boasts a measurement spatial resolution of 1.4 mm2 and a sensitivity of 0.15 kV/mm, ensuring measurement accuracy exceeding 96.50%. Through practical measurements of the spatial electric field distribution within the oil–pressboard insulation under direct current voltage, the non‐uniform distribution of the electric field is effectively captured in oil. Notably, the maximum deviation in field strength at different positions within the transformer oil can reach 18.5%. Moreover, as the applied voltage increases, the unevenness coefficient of the field strength gradually rises, peaking at 1.19, which signifies a progressive expansion in the area affected by electric field distortion. By conducting tests to assess the dispersion of volume resistivity in samples from various positions within large sheets of pressboard, the variation in volume resistivity of materials is posited as one of the contributing factors to the non‐uniform distribution of electric field within the oil. The detailed accomplishments aim to provide essential technical and theoretical support for optimising insulation structure design of converter transformers.

Learned Inference of Annual Ring Pattern of Solid Wood

AbstractWe propose a method for inferring the internal anisotropic volumetric texture of a given wood block from annotated photographs of its external surfaces. The global structure of the annual ring pattern is represented using a continuous spatial scalar field referred to as the growth time field (GTF). First, we train a generic neural model that can represent various GTFs using procedurally generated training data. Next, we fit the generic model to the GTF of a given wood block based on surface annotations. Finally, we convert the GTF to an annual ring field (ARF) revealing the layered pattern and apply neural style transfer to render orientation‐dependent small‐scale features and colors on a cut surface. We show rendered results of various physically cut real wood samples. Our method has physical and virtual applications such as cut‐preview before subtractive fabricating solid wood artifacts and simulating object breaking.

Joint identification method for thermal–mechanical coupling loads

The crucial role in evaluating structural fatigue life, implementing health monitoring, and optimizing lightweight and reliable design is played by load identification technology. With the development of the aviation industry towards high-speed aircraft, the challenges under high temperature and high load conditions have been intensified. Unfortunately, in environments characterized by the coexistence of force and heat, difficulties are faced by existing load identification methods in achieving key decoupling and accurate identification of force and temperature fields. To address this gap, the Joint Identification Method (JIM), a new method that integrates force and thermal strain patterns, is introduced in this article. This method has been developed through in-depth research on “joint physical fields” and “joint basis functions.” Multiple physical fields for load identification are systematically decomposed by our method, each field is theoretically deduced, and they are seamlessly reassembled using the same structural spatial field. Through the method, mechanical strain and thermal strain are separated in high-precision temperature field reconstruction, and a basis function method for identifying the full field load distribution under a few measurement points is obtained. To verify the accuracy of our proposed Joint Identification Method (JIM), five experiments were designed, and the results consistently demonstrated the superiority of our method in accuracy (with an error always below 5%), efficiency (avoiding a large number of redundant calculations in traditional Duhamel methods), and reliability in static and dynamic load static heat identification. In the end, accurate, efficient, and reliable identification of the structural strain field, structural load field, and temperature field were achieved by our method.

Three-dimensional deformation measurement techniques in space multi-physical fields based on digital image correlation

Aiming at the three-dimensional deformation measurement requirement of the high-stability structure of spacecraft in space multi-physical fields, a comprehensive instrument protection device in a vacuum high-low temperature environment is developed using ultra-low temperature anti-condensation and vacuum heat insulation technology. This ensures that the digital image correlation (DIC) measurement camera always remains in a constant temperature and pressure environment and can stably exert its original efficiency in space multi-physical field environments. Subsequently, a path-dependent DIC method based on a graphics processing unit computing parallel acceleration is proposed by combining a pre-interpolation strategy and integrated image technology, which realizes high-precision matching of digital images before and after deformation in a spatial multi-physical field environment and improves the computing efficiency. To address the problem that a common reference point may deform significantly in a space environment with a large temperature change, which leads to inaccurate system accuracy, combined with a random sampling consistency algorithm and singular value decomposition method, the optimal registration of the coordinate system and the solution of deformation were realized by setting the threshold of the reference point margin screening. Finally, a three-dimensional deformation measurement system in high- and low-temperature vacuum environments was built, and the three-dimensional deformation measurement of the satellite antenna in high- and low-temperature vacuum environments was verified, which proves the effectiveness and accuracy of the above method.

POI Route Recommendation Model Based on Symmetrical Naive Bayes Classification Spatial Accessibility and Improved Cockroach Swarm Optimization Algorithm

The commonly used POI route recommendation methods usually ignore the effects of tourists’ interests and transportation geographical conditions, and so may not output the optimal results. To solve the problems, we propose a POI route recommendation model based on symmetrical Naive Bayes classification spatial accessibility (NBCSA) and an improved cockroach swarm optimization algorithm (ICSOA), aiming to recommend POI routes that satisfy tourists’ interests and have the lowest travel costs under tourism transportation geographical conditions. Using the historical POIs visited by tourists as the training set, we construct an improved symmetrical Naive Bayes classification algorithm (NBCA), and the POIs in the destination city are divided into categories by tourists’ preferences. Then we propose an improved NBCSA model to calculate the spatial accessibility field strength (SAFS) for each category’s POIs. Based on the recommended POIs, we propose the ICSOA to recommend optimal POI routes. The experiment verifies that the proposed algorithm can effectively classify the POIs and recommend POIs that best match the tourists’ interests and produce the lowest travel costs. Compared with the TCA and GDA method, the proposed algorithm can output the POI routes with lower travel costs and has higher algorithm execution efficiency. Among the output optimal routes, the proposed algorithm can reduce costs by 5.62% at the lowest and 52.25% at the highest.

Using local knowledge to reconstruct climate‐mediated changes in disease dynamics and yield—A case study on Arabica coffee in its native range

Societal Impact StatementAdapting agriculture to climate change requires an understanding of the long‐term relationship between climate, disease dynamics, and yield. While some countries have monitored major crop diseases for decades or centuries, comparable data is scarce or non‐existent for many countries that are most vulnerable to climate change. For this, a novel approach was developed to reconstruct climate‐mediated changes in disease dynamics and yield. Here, a case study on Arabica coffee in its area of origin demonstrates how to combine local knowledge, climate data, and spatial field surveys to reconstruct disease and yield time series and to postulate and test hypotheses for climate–disease–yield relationships.Summary While some countries have monitored crop diseases for several decades or centuries, other countries have very limited historical time series. In such areas, we lack data on long‐term patterns and drivers of disease dynamics, which is important for developing climate‐resilient disease management strategies. We adopted a novel approach, combining local knowledge, climate data, and spatial field surveys to understand long‐term climate‐mediated changes in disease dynamics in coffee agroforestry systems. For this, we worked with 58 smallholder farmers in southwestern Ethiopia, the area of origin of Arabica coffee. The majority of farmers perceived an increase in coffee leaf rust and a decrease in coffee berry disease, whereas perceptions of changes in coffee wilt disease and Armillaria root rot were highly variable among farmers. Climate data supported farmers' understanding of the climatic drivers (increased temperature, less rainy days) of these changes. Temporal disease‐climate relationships were matched by spatial disease‐climate relationships, as expected with space‐for‐time substitution. Understanding long‐term disease dynamics and yield is crucial to adapt disease management to climate change. Our study demonstrates how to combine local knowledge, climate data and spatial field surveys to reconstruct disease time series and postulate hypotheses for disease‐climate relationships in areas where few long‐term time series exist.