Continuous Raster Research Articles

Mapping the distribution of tuberculosis cases and associated factors identified through routine program implementation and community-based active screening in Central Ethiopia.

Smear-positive TB patients greatly contribute to community-level transmission of this disease. Locating hotspots would make it easier to prioritize and target control interventions. This study aimed to assess the spatial distribution of smear-positive index TB cases and their secondary cases and the predictors of clustering of smear-positive TB cases. This study was conducted in the Silti Zone of Central Ethiopia from 2020 to 2022. The data of smear-positive index TB patients were collected from the unit TB registries of healthcare facilities. Contacts of all index TB patients were screened in the community and tested to identify secondary TB patients. We performed spatial analysis, including Moran's I statistic, the Getis-Ord Gi* statistic and geographically weighted regression (GWR), to assess the global distribution, local clustering and predictors of clustering of smear-positive TB patients, respectively. Additionally, we used inverse distance weighting (IDW) interpolation to predict the distribution of smear-positive TB cases and develop a continuous raster map for places with no data. Spatial autocorrelation analysis revealed that the distribution of smear-positive TB patients exhibited significant clustering (Moran's I = 0.70029; p value < 0.000). The Getis-Ord Gi* output indicated the presence of statistically significant hotspots as well as cold spots in the study area. Significant hotspots were found in 11 Kebeles of the Silti, Dalocha and Misrak Silti districts. Significant coldspots were also found in five kebeles of the Silti and Misrak districts. GWR analysis revealed that no education, primary education, family size and thatched roof houses were significant predictors of the spatial clustering of smear-positive TB cases. We also found that the majority of the secondary TB cases were found in hotspots identified through spatial analysis. The study revealed a heterogenous distribution of smear positive TB in the study area and it could act as a model that can be replicated in other regions. The identified hotspots of TB could be targeted through location-based interventions such as systematic active screening in the form of outreach programs to improve the performance of TB prevention and control, including reducing the transmission of TB. Educational status, family size and housing type were some of the factors that significantly influenced the spatial distribution of smear-positive TB in the study area. The distribution of secondary TB cases found through household contact screening coincided with the identified hotspots, indicating greater transmission of the disease in these places.

Standoff Deep Ultraviolet Raman Spectrometer for Trace Detection.

We developed a state-of-the-art, high-sensitivity, low-stray-light standoff deep-ultraviolet (DUV) Raman spectrometer for the trace detection of resonance Raman-enhanced chemical species. As an excitation source for Raman measurements, we utilized our recently developed, second-generation, miniaturized, diode-pumped, solid-state neodymium-doped gadolinium orthovanadate (Nd:GdVO4) laser that generates quasi-continuous wave 228 nm light. This 228 nm excitation enhances the Raman intensities of vibrations of NOx groups in explosive molecules, aromatic groups in biological molecules, and various aromatic hydrocarbons. Our DUV Raman spectrograph utilizes a custom DUV f/8 Cassegrain telescope with an ∼200 mm diameter primary mirror, high-efficiency DUV transmission gratings, custom DUV mirrors, and a custom 228 nm Rayleigh rejection filter. We utilized our new standoff DUV Raman spectrometer to measure high signal-to-noise ratio spectra of ∼50 μg/cm2 drop-cast explosives: ammonium nitrate (AN), trinitrotoluene, pentaerythritol tetranitrate as well as aromatic biological molecules: lysozyme, tryptophan, tyrosine, deoxycytidine monophosphate, deoxyadenosine monophosphate at an ∼3 m distance within 10-30 s accumulation times. We roughly estimate the average ultraviolet resonance Raman (UVRR) detection limits for the relatively homogeneous drop-cast films of explosives and biological molecules to be ∼1 μg/cm2 when utilizing a continuous raster scanning that averages Raman signal over ∼1 cm2 sample area to avoid quick analyte depletion due to ultraviolet (UV) photolysis. We determined 3 m standoff UVRR detection limits for drop-cast AN films and identified factors impacting UVRR detection limits such as analyte photochemistry and analyte morphology. We found a detection limit of ∼0.5 μg/cm2 for drop-cast AN films on glass substrates when the Raman signal is averaged over ∼0.5 cm2 of sample surface using a continuous raster scan. For a step raster scan, when the probed sample area is limited to the laser spot size, the detection limit is approximately tenfold higher (∼5 μg/cm2) due to the impact of UV photochemistry.

Land cover classification and urbanization monitoring using Landsat data: A case study in Changsha city, Hunan province, China

The United Nations predicts that by 2050, 64.1% of the developing world and 85.9% of the developed world will be urbanized. This has resulted in a rapid change in land use and land cover types in the areas surrounding cities in all countries, particularly in China, which determines the relevance of this article. The aim of the study was to evaluate the dynamics of land cover change in Changsha City, Hunan Province, China, between 2005 and 2020, using Landsat time series satellite images and the Random Forest classification algorithm. The data acquisition, pre-processing, and analysis were conducted on the Google Earth Engine (GEE) publicly available online platform. Land cover thematic continuous raster maps were produced using ESRI ArcGIS 10.5.1 software. The overall classification accuracy was obtained by more than 83% for every produced map and the Kappa coefficient was 0.84 and higher, which approves the reliable classification results that are close to similar recent studies in terms of obtained accuracy. The study shows that from 2005 to 2020, the area of settlement in Changsha City, China, increased significantly, with an exponential increase in urban area from 3.23% to 15.95%. The proportion of forest cover gradually decreased from 2005 to 2015 but increased from 2015 to 2020. Cropland was the second most dominant land cover type, with a peak of almost 50% in 2010. Water bodies remained stable at around 3%. The proportion of open soil and bare land cover fluctuated between 180 and 400 km2 (1.5-3%). The study suggests that the offered monitoring approach provides reliable results, and the research findings can be used for sustainable urban planning and management, as well as conservation and development initiatives. The remote sensing data and advanced GIS technologies can provide decision-makers with the accurate data to ensure sustainable development in this area

Groundwater Potential Mapping Using Remote Sensing and Random Forest Machine Learning Model: A Case Study from Lower Part of Wadi Yalamlam, Western Saudi Arabia

Groundwater storage is influenced by many geo-environmental factors. Most of these factors are prepared in the form of categorical data. The present study utilized raster satellite data instead of categorical data and a Random Forest machine learning model to identify groundwater potential zones at the downstream parts of Wadi Yalamlam, western Saudi Arabia. Eighteen groundwater-influenced variables are prepared in continuous raster format from ASTER GDEM, TRMM, and SPOT-5 satellite data. The Random Forest (RF) model is trained using (70%) of the target variable and validated using the rest (30%). The accuracy, sensitivity, and F1-score are all generated to evaluate the model performance. SPOT band 3, band 4, and the rainfall variables are the most important for groundwater potential mapping contributing 11%, 7%, and 8% during the prediction stage. The GDEM elevation variable contributed 6% and the slope variable scored 1%. The main conclusions of the study are: (1) The RF machine learning algorithm successfully identified three groundwater potential zones with an accuracy of 96%. (2) The high, moderate, and low potential groundwater zones covered 11.5%, 59.9%, and 28.6% of the study area respectively. (3) Majority of high and moderate zones lie within the pumping rate range between 10 and 20 m3/day. (4) The approach developed in this study can be applied to any other wadis having the same conditions to help authorities and decision-makers in planning and development projects.

Открытые источники данных для моделирования ареалов: информационные системы о биоразнообразии и наборы пространственных данных условий среды

Открытые источники данных для моделирования ареалов: информационные системы о биоразнообразии и наборы пространственных данных условий среды

Influence of scanning strategies on laser-cladded AISI 420 steel

ABSTRACT In this study, the influence of different laser scanning strategies on substrate deformation, microstructure and properties of laser-cladded thin-walled 420 martensitic stainless steel (MSS) plate have been carefully investigated. The continuous offset-out scanning strategy (COSS) and continuous raster scanning strategy (CRSS) was compared with the subarea offset-out scanning strategies (SOSS) and subarea raster scanning strategy (SRSS) with different sequences. The results show that the minimum and maximum macroscopic deformation was generated using COSS and CRSS, respectively. The temperature distribution with characteristic of quasi-symmetry was generated by COSS, which can largely suppress the substrate deformation, in marked contrast to a long ellipse symmetry by CRSS. All the laser-cladded 420 MSS layers exhibit lath-martensite microstructure, while some cracks are distributed by using SOSS. The optimal comprehensive performance of the laser-cladded 420 MSS can be obtained using COSS, with smallest outermost deformation of 1.57 mm, superior hardness of 54.9 HRC and wear rate of 0.9626 × 10−5 mm3/N·m.

An Efficient User-Friendly Integration Tool for Landslide Susceptibility Mapping Based on Support Vector Machines: SVM-LSM Toolbox

Landslide susceptibility mapping (LSM) is an important element of landslide risk assessment, but the process often needs to span multiple platforms and the operation process is complex. This paper develops an efficient user-friendly toolbox including the whole process of LSM, known as the SVM-LSM toolbox. The toolbox realizes landslide susceptibility mapping based on a support vector machine (SVM), which can be integrated into the ArcGIS or ArcGIS Pro platform. The toolbox includes three sub-toolboxes, namely: (1) influence factor production, (2) factor selection and dataset production, and (3) model training and prediction. Influence factor production provides automatic calculation of DEM-related topographic factors, converts line vector data to continuous raster factors, and performs rainfall data processing. Factor selection uses the Pearson correlation coefficient (PCC) to calculate the correlations between factors, and the information gain ratio (IGR) to calculate the contributions of different factors to landslide occurrence. Dataset sample production includes the automatic generation of non-landslide data, data sample production and dataset split. The accuracy, precision, recall, F1 value, receiver operating characteristic (ROC) and area under curve (AUC) are used to evaluate the prediction ability of the model. In addition, two methods—single processing and multiprocessing—are used to generate LSM. The prediction efficiency of multiprocessing is much higher than that of the single process. In order to verify the performance and accuracy of the toolbox, Wuqi County, Yan’an City, Shaanxi Province was selected as the test area to generate LSM. The results show that the AUC value of the model is 0.8107. At the same time, the multiprocessing prediction tool improves the efficiency of the susceptibility prediction process by about 60%. The experimental results confirm the accuracy and practicability of the proposed toolbox in LSM.

The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN)

The continuous distributions of PM2.5 concentrations and predictor variables in the surrounding regions influence the PM2.5 concentrations in the prediction positions notably, yet few machine learning models quantified the spatially continuous interactions between PM2.5 concentrations and predictor variations, which limits the prediction accuracy. To fill this gap, a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN) was proposed. For STWC-DNN, three sub-networks, Single Pixel Network (SPN), Multiple Station Network (MSN), and Continuous Region Network (CRN) were designed to analyze the influence of predictor variables at the prediction position, the influence of PM2.5 concentrations from surrounding stations, and the influence of continuous raster predictor variables from surrounding pixels respectively. STWC-DNN was experimented using hourly Himawari AOD data and the outputs were compared with a series of advanced models. STWC-DNN achieved higher accuracy than existing models and the sample-based, time-based, and station-based 10-fold cross-validation (CV) R2 were 0.92, 0.90, and 0.79, respectively. The principle of establishing STWC-DNN sheds useful lights on the effective use of raster predictor variables and automatic spatiotemporal weight function to better estimate PM2.5 and other airborne pollutants based on multiple data sources. The codes of STWC-DNN are now available at https://github.com/wangzh2022/STWC-DNN.

Accuracy assessment for Landsat 8 thermal bands in measuring sea surface temperature over Kuwait and North West Arabian Gulf

Studying physical oceanography is one of the important fields of remote sensing applications. Previously, the thermal mapping of seas and oceans relied on primitive methods, such as the use of sensors installed on buoys, extracting contour lines, and deriving the values from the confluence of contour lines. Today's remote sensing provides more advanced methods for extracting sea surface temperature (SST) values for all bodies of water as a continuous raster model, through thermal sensors installed on satellites designated to monitor and observe the Earth. The Landsat program has facilitated a quantum leap by providing its data free for the public. What has become increasingly important is the inclusion, in Landsat 8, of a thermal band on the TIRS sensor through which SST can be extracted with a spatial resolution of 100 m2. In this article, the accuracy of the two thermal bands (band 10 and 11) of Landsat 8 was validated in estimating the SST of Kuwaiti and Northwest Arabian Gulf waters, through the use of 62 thermal images and 66 ground-truthing points (GTPs) taken from the field in the period from July 2013 to March 2020. This was achieved through a function provided by the ENVI 5.3 software - “brightness temperature” - to derive the surface temperature. The accuracy of Landsat 8 to monitor the SST of Kuwait and north-west Arabian Gulf waters was validated by calculating the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The accuracy of the thermal band 10 was ± 2.03 degrees (7.9%), while the accuracy of the thermal band 11 was ± 3.13 degrees (13.7%). Therefore, this study demonstrated that the thermal band 10 of Landsat 8 is more accurate than the thermal band 11 in monitoring the SST of Kuwaiti and north-west Arabian Gulf waters, with a difference of ± 1.1 degrees (5.8%).

Optimized Chaotic encrypted image based on continuous raster scan method

As the greatest effective method of protection of multimedia data is image encryption is extensively used in data hiding, security authentication and content protection. Nevertheless, the security and efficiency are still the key issues of image encryption algorithm. However, the existing image encryption techniques face the issues namely poor resistance to plaintext attacks, simple algorithmic structures and small key space. In order to overcome the problem of traditional techniques, a joint image encryption techniques of continuous raster Scan, Henon Chaotic Map and Chebyshev Chaotic Map are developed for providing a security of images. Initially, the continuous raster is used to scramble the pixels of each blocks, where images are partitioning an image into non overlapping blocks and starting point is randomly selected in Henon Chaotic Map to permute the block. To calculate a random matrix, Chebyshev Chaotic Map is used to generate the sequence based on the secret keys. Finally, bitwise XOR operation between the scrambled image and random matrix is conducted to obtain the encrypted image. The proposed method takes very less time for encryption. Simulation results show that the algorithm is efficient and usable for the security of the image encryption system.

Application of Mobile Terrestrial LiDAR Scanning Systems for Identification of Potential Pavement Rutting Locations

Periodic measurement and identification of the presence and severity of pavement rutting are crucial for pavement management programs conducted by state transportation agencies. This paper proposes a novel analytical method for identifying pavement rutting locations using data collected by mobile terrestrial LiDAR scanning (MTLS). Four vendor MTLS systems were evaluated based on their ability to accurately reproduce a roadway’s transverse profile. To establish ground-truth measurements, 2 in. interval pavement transverse profiles, which included rutting sections, were collected using traditional surveying techniques. MTLS transverse profiles were evaluated using partial curve mapping, Fréchet distance, area, curve length, and dynamic time warping techniques. Resultant pavement transverse profiles were compared between vendors and a profile created from traditional surveying. Results indicate that calibrated MTLS systems can provide accurate transverse profiles for potential identification of pavement rut areas. Based on this determination, a novel method was developed for use in identifying locations of pavement rutting through analysis of the curvature of MTLS raster surfaces. After evaluating three grid cell sizes for elevation raster surfaces, a raster grid cell size of 1 ft × 1 ft was determined to be most suitable for identifying continuous concave raster cell groups along wheel path trajectories. These cell groupings were found to reliably identify pavement rutting locations. The analytical procedures employed through application of this method consist of an efficient workflow process that is not reliant on a time-consuming continuous comparison with an MTLS-modeled uniform surface.

Tailorable rigidity and energy-absorption capability of 3D printed continuous carbon fiber reinforced polyamide composites

The aim of the present work was to study the tailorable rigidity and energy absorption capability of 3D printed short and continuous carbon fiber reinforced polyamide (3DP-scCFRPA). The synergistic reinforcement of laminates by both short and continuous carbon fibers was superior to the individual carbon fiber reinforcement for the mechanical properties. Continuous carbon fiber raster angle, stacking sequence and loading direction were considered as the main factors affecting mechanical properties of 3DP-scCFRPA in this investigation. The flexural strength, modulus and energy absorption capability of 3DP-scCFRPA were investigated by three-point bending testing. The deformation processes and failure mechanisms of laminated composites were analyzed in association with morphological evolution. The results showed that laminates with 0°continuous fiber raster angle (along with the length direction), separated distribution of continuous carbon fiber layers, and loaded in thickness direction, presented the highest flexural modulus and strength as high as 16.1 GPa and 262.0 MPa, respectively. The printed laminates with ± 45° continuous fiber raster angle, separated distribution of continuous carbon fiber reinforced layers, and loaded in width direction showed the highest energy absorption of 1613.3 MJ/mm3. Continuous fibers with 0° raster angle could effectively improve rigidity of composites. The introduction of ±45° layers into laminates significantly increased the energy absorption capability of composites. Separated continuous fiber reinforced layers were helpful to impede crack propagation and presented better interfaces with higher interfacial strength between 3D printed layers.

Automatic Focusing Control in Beaconless APT System

Abstract The beaconless acquisition pointing and tracking (APT) system uses signal light to act as beacon. But the signal light’s divergence angle is so small that it is difficult to capture. In this work, aiming at the different requirements of divergence angle for a free-space optical-communication (FSO) system in a different working phase, we use a stepper motor for automatic focusing control in the acquisition and pointing process to change the spot size. For this purpose, we derive a formula for the average acquisition time of continuous raster scanning. The relationship between the received power and the divergence angle under the influence of the atmospheric transmission and the pointing errors is analyzed to obtain the optimum range of the divergence angle. Also the relationship between defocusing and the divergence angle of collimated beam is given. Finally, we prove by experiments that the automatic focusing control method effectively solves the contradiction of the different divergence angles between communication phase and acquisition phase.

Finding cultural heritage traces from modern social media

Finding cultural heritage traces from modern social media

High-efficient extraction of drainage networks from digital elevation models constrained by enhanced flow enforcement from known river maps

Drainage network extraction plays an important role in geomorphologic analyses, hydrologic modeling, and non-point source pollutant simulation, among others. Flow enforcement, by imposing information of known river maps to digital elevation models (DEMs), leads to improved drainage network extraction. However, the existing flow enforcement methods (e.g., the elevation-based stream-burning method) have certain limitations, as they may cause unreal longitudinal profiles, lead to unintended topological errors, and even misinterpret the overall drainage patterns. The present study proposes an enhanced flow enforcement method without elevation modification towards an accurate and efficient drainage network extraction. In addition to preserving the Boolean-value information as to whether a DEM pixel belongs to a stream, the proposed method can also well preserve and fully utilize the topological relations among mapped streamlines and morphological information of each mapped streamline. The method involves two important steps: (1) proposal of an improved rasterization algorithm of mapped streamlines to yield continuous, unambiguous, and collision-free raster equivalent of stream vectors for flow enforcement; and (2) realization of the enhanced flow enforcement in a modified Priority-Flood procedure –– in this way, flows are enforced to completely follow the mapped streamlines, and hence, channel short-circuits and spurious confluences of adjacent streams are avoided. An efficient implementation of the method is made based on a size-balanced binary search tree. The method is also tested over the Rogue River Basin in the United States, using DEMs with various resolutions. Visual and statistical analyses of the results indicate three major advantages of the proposed method: (1) significant reduction in the misinterpretation of drainage patterns; (2) maximum channel displacement of one pixel to the river map at various resolutions; and (3) high computational efficiency.

The Influence of MS Imaging Parameters on UV-MALDI Desorption and Ion Yield.

Ultraviolet matrix-assisted laser desorption/ionization mass spectrometry imaging (UV-MALDI MSI) is a widely used technique for imaging molecular distributions within biological systems. While much work exists concerning desorption in UV-MALDI MS, the effects of commonly varied parameters for imaging applications (repetition rate, use of continuous raster mode and raster speed), which determine spatial resolution and limits of detection for the technique, remain largely unknown. We use multiple surface characterization modalities to obtain quantitative measurements of material desorption and analyte ion yield in thin film model systems of two matrix compounds, arising from different UV-MALDI MSI sampling conditions. Observed changes in resulting ablation feature point to matrix-dependent spatial resolution and laser-induced matrix modification effects. Analyte ion yields of 10-9 to 10-6 are observed. Complex changes in ion yield, between spot and raster sampling and arising from varied laser repetition rate and raster speed, are observed. Graphical Abstract.

Investigating spatial error structures in continuous raster data

The objective of this study is to investigate spatial structures of error in the assessment of continuous raster data. The use of conventional diagnostics of error often overlooks the possible spatial variation in error because such diagnostics report only average error or deviation between predicted and reference values. In this respect, this work uses a moving window (kernel) approach to generate geographically weighted (GW) versions of the mean signed deviation, the mean absolute error and the root mean squared error and to quantify their spatial variations. Such approach computes local error diagnostics from data weighted by its distance to the centre of a moving kernel and allows to map spatial surfaces of each type of error. In addition, a GW correlation analysis between predicted and reference values provides an alternative view of local error. These diagnostics are applied to two earth observation case studies. The results reveal important spatial structures of error and unusual clusters of error can be identified through Monte Carlo permutation tests. The first case study demonstrates the use of GW diagnostics to fractional impervious surface area datasets generated by four different models for the Jakarta metropolitan area, Indonesia. The GW diagnostics reveal where the models perform differently and similarly, and found areas of under-prediction in the urban core, with larger errors in peri-urban areas. The second case study uses the GW diagnostics to four remotely sensed aboveground biomass datasets for the Yucatan Peninsula, Mexico. The mapping of GW diagnostics provides a means to compare the accuracy of these four continuous raster datasets locally. The discussion considers the relative nature of diagnostics of error, determining moving window size and issues around the interpretation of different error diagnostic measures. Investigating spatial structures of error hidden in conventional diagnostics of error provides informative descriptions of error in continuous raster data.

Spatial assessment of the potential of groundwater quality using fuzzy AHP in GIS

In this study, fuzzy AHP method is used for extracting the water quality indicators based on the Schuler standard and World Health Organization (WHO) guidelines during a 20-year period. For this purpose, the best fit of the zoning model was performed. Furthermore, by comparing the standard errors, the continuous Raster layer was extracted from the important parameters used in generating the qualitative potential assessment index. The classified layer was generated by integrating continuous layers in the GIS environment and with the use of Python programming. The similarity of the outputs of both methods indicates the presence of large sections of aquifers in the middle and southwestern regions of Iran in the “temporarily drinkable” and “bad” classes. The calculations showed that the majority of aquifers that were located in the “inappropriate” class during the first 10 years fell to less valuable class types. Based on the results of the model, there is a direct correlation between the drop in water resources and the decline in the quality indices. In addition, in the Urmia and Bushehr coastal aquifers, due to excessive water withdrawal and salty water penetration, the quality of the table water is in critical condition. Based on the results of the research, the aquifers in the range of Zagros and Alborz mountains show the least change in water quality. The reason for this is the depth of the aquifer and the ability to recharge it.

UAV‐derived habitat predictors contribute strongly to understanding avian species–habitat relationships on the Eastern Qinghai‐Tibetan Plateau

AbstractSpecies–habitat relationships are of core interest in ecological studies. In this research, we explored the potential of small, unmanned aerial vehicles (UAVs) to derive a comprehensive set of habitat predictors to explain distribution variance of an alpine bird community in Jikdril county, on the eastern Qinghai‐Tibetan Plateau (QTP). In 2014 and 2015, we carried out two breeding season bird surveys on 140 plots, from eight sample blocks in the Nyanpo Yutse region. In summer 2014, we conducted 39 flights over the eight sample blocks, and obtained 5500 images which covered 45 km2 of alpine grassland habitat. We used photogrammetry software to generate high‐resolution orthophotos and digital surface models with an average pixel size of 13 cm. We derived both 3D‐ and 2D‐UAV‐based models as habitat predictors used to analyze explained variance in the local bird community at four different sample scales (plot radii of 50 m, 100 m, 150 m and 200 m). We performed canonical correspondence analyses (CCA), with a model‐building approach based on permutation. Furthermore, the proportion of total variance explained by each predictor group was calculated, and analysis of results showed that UAV data derived predictors played a significant role in explaining the variance of the sampled bird community on the QTP. In particular, 3D‐based predictors contributed strongly to the explanation of variance (50 m: 45.44%, 100 m: 44.60%, 150 m: 49.21%, 200 m: 36.22% of the total explained variance per sample size), followed by 2D‐based predictors over the same four sample scales (50 m: 35.45%, 100 m: 34.57%, 150 m: 31.19%, 200 m: 39.48%). Our results indicate that a sample plot scale of 150–200 m radius leads to the most comprehensive explanation of variance (all predictors: 39.59%, P &lt; 0.0001 and 39.13% P &lt; 0.0001). UAV data provide us with fine‐scale (10 cm resolution) continuous raster data on land cover, topographic and landscape features, which are hard to acquire with conventional habitat measurements. We therefore successfully demonstrate the performance of UAV‐derived data in facilitating ecological research at different spatial scales.

THE UNCERTAINTIES ON THE GIS BASED LAND SUITABILITY ASSESSMENT FOR URBAN AND RURAL PLANNING

Abstract. The majority of the research on the uncertainties of spatial data and spatial analysis focuses on some specific data feature or analysis tool. Few have accomplished the uncertainties of the whole process of an application like planning, making the research of uncertainties detached from practical applications. The paper discusses the uncertainties of the geographical information systems (GIS) based land suitability assessment in planning on the basis of literature review. The uncertainties considered range from index system establishment to the classification of the final result. Methods to reduce the uncertainties arise from the discretization of continuous raster data and the index weight determination are summarized. The paper analyzes the merits and demerits of the “Nature Breaks” method which is broadly used by planners. It also explores the other factors which impact the accuracy of the final classification like the selection of class numbers, intervals and the autocorrelation of the spatial data. In the conclusion part, the paper indicates that the adoption of machine learning methods should be modified to integrate the complexity of land suitability assessment. The work contributes to the application of spatial data and spatial analysis uncertainty research on land suitability assessment, and promotes the scientific level of the later planning and decision-making.