Multi-temporal Unmanned Aerial Vehicle Research Articles

Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images

Accurate and timely monitoring of biochemical and biophysical traits associated with crop growth is essential for indicating crop growth status and yield prediction for precise field management. This study evaluated the application of three combinations of feature selection and machine learning regression techniques based on unmanned aerial vehicle (UAV) multispectral images for estimating the bio-parameters, including leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC), at key growth stages of winter wheat. The performance of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for the bio-parameters estimation was compared with that of Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) regression with internal feature selectors. A consumer-grade multispectral UAV was used to conduct four flight campaigns over a split-plot experimental field with various nitrogen fertilizer treatments during a growing season of winter wheat. Eighteen spectral variables were used as the input candidates for analyses against the three bio-parameters at four growth stages. Compared to LASSO and RF internal feature selectors, the SFS algorithm selects the least input variables for each crop bio-parameter model, which can reduce data redundancy while improving model efficiency. The results of the SFS-SVR method show better accuracy and robustness in predicting winter wheat bio-parameter traits during the four growth stages. The regression model developed based on SFS-SVR for LAI, LCC, and CCC, had the best predictive accuracy in terms of coefficients of determination (R2), root mean square error (RMSE) and relative predictive deviation (RPD) of 0.967, 0.225 and 4.905 at the early filling stage, 0.912, 2.711 μg/cm2 and 2.872 at the heading stage, and 0.968, 0.147 g/m2 and 5.279 at the booting stage, respectively. Furthermore, the spatial distributions in the retrieved winter wheat bio-parameter maps accurately depicted the application of the fertilization treatments across the experimental field, and further statistical analysis revealed the variations in the bio-parameters and yield under different nitrogen fertilization treatments. This study provides a reference for monitoring and estimating winter wheat bio-parameters based on UAV multispectral imagery during specific crop phenology periods.

Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering.

High throughput field phenotyping techniques employing multispectral cameras allow extracting a variety of variables and features to predict yield and yield related traits, but little is known about which types of multispectral features are optimal to forecast yield potential in the early growth phase. In this study, we aim to identify multispectral features that are able to accurately predict yield and aid in variety classification at different growth stages throughout the season. Furthermore, we hypothesize that texture features (TFs) are more suitable for variety classification than for yield prediction. Throughout 2021 and 2022, a trial involving 19 and 18 European wheat varieties, respectively, was conducted. Multispectral images, encompassing visible, Red-edge, and near-infrared (NIR) bands, were captured at 19 and 22 time points from tillering to harvest using an unmanned aerial vehicle (UAV) in the first and second year of trial. Subsequently, orthomosaic images were generated, and various features were extracted, including single-band reflectances, vegetation indices (VI), and TFs derived from a gray level correlation matrix (GLCM). The performance of these features in predicting yield and classifying varieties at different growth stages was assessed using random forest models. Measurements during the flowering stage demonstrated superior performance for most features. Specifically, Red reflectance achieved a root mean square error (RMSE) of 52.4g m-2 in the first year and 64.4g m-2 in the second year. The NDRE VI yielded the most accurate predictions with an RMSE of 49.1g m-2 and 60.6g m-2, respectively. Moreover, TFs such as CONTRAST and DISSIMILARITY displayed the best performance in predicting yield, with RMSE values of 55.5g m-2 and 66.3g m-2 across the two years of trial. Combining data from different dates enhanced yield prediction and stabilized predictions across dates. TFs exhibited high accuracy in classifying low and high-yielding varieties. The CORRELATION feature achieved an accuracy of 88% in the first year, while the HOMOGENEITY feature reached 92% accuracy in the second year. This study confirms the hypothesis that TFs are more suitable for variety classification than for yield prediction. The results underscore the potential of TFs derived from multispectral images in early yield prediction and varietal classification, offering insights for HTP and precision agriculture alike.

Intelligent Segmentation and Change Detection of Dams Based on UAV Remote Sensing Images

Guilin is situated in the southern part of China with abundant rainfall. There are 137 reservoirs, which are widely used for irrigation, flood control, water supply and power generation. However, there has been a lack of systematic and full-coverage remote sensing monitoring of reservoir dams for a long time. According to the latest public literature, high-resolution unmanned aerial vehicle (UAV) remote sensing has not been used to detect changes on the reservoir dams of Guilin. In this paper, an intelligent segmentation change detection method is proposed to complete the detection of dam change based on multitemporal high-resolution UAV remote sensing data. Firstly, an enhanced GrabCut that fuses the linear spectral clustering (LSC) superpixel mapping matrix and the Sobel edge operator is proposed to extract the features of reservoir dams. The edge operator is introduced into GrabCut to redefine the new energy function’s smooth item, which makes the segmentation results of enhanced GrabCut more robust and accurate. Then, through image registration, the multitemporal dam extraction results are unified to the same coordinate system to complete the difference operation, and finally the dam change results are obtained. The experimental results of two representative reservoir dams in Guilin show that the proposed method can achieve a very high accuracy of change detection, which is an important reference for related research.

Monitoring Seasonal Growth of Eucalyptus Plantation under Different Forest Age and Slopes Based on Multi-Temporal UAV Stereo Images

Eucalyptus grows rapidly and can grow up to 10 m per year, making them an important source of wood supply. Guangxi is a typical karst terrain, with hills and plains being the main growth areas for Eucalyptus. The differences in terrain can affect the seasonal growth of Eucalyptus plantations at different forest ages, which in turn affects the stocking of Eucalyptus. Currently, indiscriminate logging and management not only cause resource waste but also bring economic losses to operators. In this study, an unmanned aerial vehicle (UAV) was used to obtain multi-temporal stereo images to investigate the seasonal growth differences in structural parameters of individual Eucalyptus trees under different forest ages and slopes, providing data support for the precise management of Eucalyptus resources. The results showed that the tree height, crown width, diameter at breast height (DBH), and aboveground biomass (AGB) of individual trees, extracted based on UAV stereo images, are highly correlated with the field measured data, with an R2 of 0.99, 0.78, 0.75, and 0.92, and RMSE of 0.21 m, 0.16 m, 1.17 cm, and 3.79 kg/tree, respectively. The growth of Eucalyptus structural parameters varies in different seasons, with faster growth in spring and autumn, accounting for 76.39%, 73.75%, 73.65%, and 73.68% of the total annual growth, respectively. The growth of different structural parameters of individual trees is closely related to forest age, with tree height, crown width, and DBH gradually slowing down with the increase in forest age, while AGB shows a trend of first increasing and then decreasing. The differences in terrain also have a certain impact on the growth of individual Eucalyptus tree structural parameters. For individual 1-year-old and 3-year-old Eucalyptus trees, those located on gentle slopes grow faster in spring and autumn than those located on flat land. However, those located on flat land grow faster in summer and winter than those located on gentle slopes. For tree height, crown width, DBH, and AGB, the maximum annual growth differences between Eucalyptus trees on gentle slopes and flat are 3.17 m, 0.26 m, 1.9 cm, and 9.27 kg/tree, respectively. The results indicate that based on UAV stereo images, the individual tree structural parameters in Eucalyptus plantations under different forest ages and slopes can be extracted, as well as seasonal growth can be monitored, providing data support for logging, planting, and management of Eucalyptus plantations.

A Prediction Model of Maize Field Yield Based on the Fusion of Multitemporal and Multimodal UAV Data: A Case Study in Northeast China

The prediction of crop yield plays a crucial role in national economic development, encompassing grain storage, processing, and grain price trends. Employing multiple sensors to acquire remote sensing data and utilizing machine learning algorithms can enable accurate, fast, and nondestructive yield prediction for maize crops. However, current research heavily relies on single-type remote sensing data and traditional machine learning methods, resulting in the limited robustness of yield prediction models. To address these limitations, this study introduces a field-scale maize yield prediction model named the convolutional neural network–attention–long short-term memory network (CNN-attention-LSTM) model, which utilizes multimodal remote sensing data collected by multispectral and light detection and ranging (LIDAR) sensors mounted on unmanned aerial vehicles (UAVs). The model incorporates meteorological data throughout the crop reproductive stages and employs the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) for the initial part of the vegetative stage (initial part of the V period), the later part of the vegetative stage (later part of the V period), the reproductive stage (R period), and the maturity stage (M period), along with LIDAR data for Point75–100 in the later part of the V period, Point80–100 in the R period, and Point50–100 in the M period, complemented by corresponding meteorological data as inputs. The resulting yield estimation demonstrates exceptional performance, with an R2 value of 0.78 and an rRMSE of 8.27%. These results surpass previous research and validate the effectiveness of multimodal data in enhancing yield prediction models. Furthermore, to assess the superiority of the proposed model, four machine learning algorithms—multiple linear regression (MLR), random forest regression (RF), support vector machine (SVM), and backpropagation (BP)—are compared to the CNN-attention-LSTM model through experimental analysis. The outcomes indicate that all alternative models exhibit inferior prediction accuracy compared to the CNN-attention-LSTM model. Across the test dataset within the study area, the R2 values for various nitrogen fertilizer levels consistently exceed 0.75, illustrating the robustness of the proposed model. This study introduces a novel approach for assessing maize crop yield and provides valuable insights for estimating the yield of other crops.

Improved Yield Prediction of Ratoon Rice Using Unmanned Aerial Vehicle-Based Multi-Temporal Feature Method

Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture. However, the unique agronomic practice (i.e., varied stubble height treatment) in rice ratooning could lead to inconsistent rice phenology, which had a significant impact on yield prediction of ratoon rice. Multi-temporal unmanned aerial vehicle (UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods. Thus, in this study, we explored the performance of combination of agronomic practice information (API) and single-phase, multi-spectral features [vegetation indices (VIs) and texture (Tex) features] in predicting ratoon rice yield, and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice. The results showed that the integrated use of VIs, Tex and API (VIs & Tex + API) improved the accuracy of yield prediction than single-phase UAV imagery-based feature, with the panicle initiation stage being the best period for yield prediction (R2 as 0.732, RMSE as 0.406, RRMSE as 0.101). More importantly, compared with previous multi-temporal UAV-based methods, our proposed multi- temporal method (multi-temporal model VIs & Tex: R2 as 0.795, RMSE as 0.298, RRMSE as 0.072) can increase R2 by 0.020–0.111 and decrease RMSE by 0.020–0.080 in crop yield forecasting. This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture, which is of great significance to take timely means for ensuring ratoon rice production and food security.

Detection of Growth Change of Young Forest Based on UAV RGB Images at Single-Tree Level

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, more and more UAVs have been used in forest survey. UAV (RGB) images are the most widely used UAV data source in forest resource management. However, there is some uncertainty as to the reliability of these data when monitoring height and growth changes of low-growing saplings in an afforestation plot via UAV RGB images. This study focuses on an artificial Chinese fir (Cunninghamia lancelota, named as Chinese Fir) young forest plot in Fujian, China. Divide-and-conquer (DAC) and the local maximum (LM) method for extracting seedling height are described in the paper, and the possibility of monitoring young forest growth based on low-cost UAV remote sensing images was explored. Two key algorithms were adopted and compared to extract the tree height and how it affects the young forest at single-tree level from multi-temporal UAV RGB images from 2019 to 2021. Compared to field survey data, the R2 of single saplings’ height extracted from digital orthophoto map (DOM) images of tree pits and original DSM information using a divide-and-conquer method reached 0.8577 in 2020 and 0.9968 in 2021, respectively. The RMSE reached 0.2141 in 2020 and 0.1609 in 2021. The R2 of tree height extracted from the canopy height model (CHM) via the LM method was 0.9462. The RMSE was 0.3354 in 2021. The results demonstrated that the survival rates of the young forest in the second year and the third year were 99.9% and 85.6%, respectively. This study shows that UAV RGB images can obtain the height of low sapling trees through a computer algorithm based on using 3D point cloud data derived from high-precision UAV images and can monitor the growth of individual trees combined with multi-stage UAV RGB images after afforestation. This research provides a fully automated method for evaluating the afforestation results provided by UAV RGB images. In the future, the universality of the method should be evaluated in more afforestation plots featuring different tree species and terrain.

Monitoring Mining Surface Subsidence with Multi-Temporal Three-Dimensional Unmanned Aerial Vehicle Point Cloud

Long-term and high-intensity coal mining has led to the increasingly serious surface subsidence and environmental problems. Surface subsidence monitoring plays an important role in protecting the ecological environment of the mining area and the sustainable development of modern coal mines. The development of surveying technology has promoted the acquisition of high-resolution terrain data. The combination of an unmanned aerial vehicle (UAV) point cloud and the structure from motion (SfM) method has shown the potential of collecting multi-temporal high-resolution terrain data in complex or inaccessible environments. The difference of the DEM (DoD) is the main method to obtain the surface subsidence in mining areas. However, the obtained digital elevation model (DEM) needs to interpolate the point cloud into the grid, and this process may introduce errors in complex natural topographic environments. Therefore, a complete three-dimensional change analysis is required to quantify the surface change in complex natural terrain. In this study, we propose a quantitative analysis method of ground subsidence based on three-dimensional point cloud. Firstly, the Monte Carlo simulation statistical analysis was adopted to indirectly evaluate the performance of direct georeferencing photogrammetric products. After that, the operation of co-registration was carried out to register the multi-temporal UAV dense matching point cloud. Finally, the model-to-model cloud comparison (M3C2) algorithm was used to quantify the surface change and reveal the spatio-temporal characteristics of surface subsidence. In order to evaluate the proposed method, four periods of multi-temporal UAV photogrammetric data and a period of airborne LiDAR point cloud data were collected in the Yangquan mining area, China, from 2020 to 2022. The 3D precision map of a sparse point cloud generated by Monte Carlo simulation shows that the average precision in X, Y and Z directions is 44.80 mm, 45.22 and 63.60 mm, respectively. The standard deviation range of the M3C2 distance calculated by multi-temporal data in the stable area is 0.13–0.19, indicating the consistency of multi-temporal photogrammetric data of UAV. Compared with DoD, the dynamic moving basin obtained by the M3C2 algorithm based on the 3D point cloud obtained more real surface deformation distribution. This method has high potential in monitoring terrain change in remote areas, and can provide a reference for monitoring similar objects such as landslides.

Multi-temporal orthophoto and digital surface model registration produced from UAV imagery over an agricultural field

Correcting the three-dimensional geometric error is essential to effectively use the multi-temporal unmanned aerial vehicle (UAV) orthophoto and digital surface model (DSM) acquired from the agricultural field. Although ground control points (GCPs) obtained through field surveys are usually used to calibrate geometrical errors establishing/maintaining GCPs and surveying them in the field are time-consuming and inefficient. Therefore, we propose a simple and efficient methodology to improve the geometric registration of multi-temporal orthophotos and DSMs without GCPs. In the proposed method, coarse to fine image registration is performed first, which corrects severe to slight errors by sequential feature and area-based matching methods. Subsequently, we extract height-invariant regions in multi-temporal DSM pairs, called elevation invariant feature (EIF), using the EIFs to register DSMs by estimating a linear regression model. Various experiments were conducted to analyze the absolute and relative accuracies using ten multi-temporal orthophotos and DSMs, and the robustness of the proposed method was evaluated using data obtained from another site. The experimental results demonstrate that the geometric quality of registered orthophotos and DSMs was significantly improved.

Multitemporal UAV surveys of geomorphological changes caused by postfire heavy rain in Kamaishi city, northeast Japan

Unmanned aerial vehicles (UAVs) are widely utilized for surveying various sediment disasters, and the high-resolution imaging they provide reveals detailed topographic characteristics. However, the availability of multitemporal UAV observations, which reveal geomorphological changes before and after the disaster, is still limited. In this research, we performed multitemporal UAV observations for geomorphological changes before and after extreme postfire heavy rain in the Ozaki Peninsula wildfire area in Japan. Significant geomorphological changes were observed associated with eight landslides and stream shifting, with a spatial resolution of 0.30 cm obtained by structure from motion (SfM) processing. To extract the surface changes based on multitemporal UAV observations, in addition to elevation displacement, vertex normal vectors were calculated for each mesh. We found that the elevation displacement showed comprehensive alterations in the entire area and that the changes in vertex normal vectors effectively captured the shape of these alterations. Deterioration, deposition, and scarping were shown for landslides, and the dynamics of erosion and deposition in the cross-section were for stream shifting. In addition, the detected geomorphological changes were compared with the predisaster topographic characteristics. Landslides occurred with a catchment area of 500 to 2,000 m2 and a slope inclination of 30 to 40°, and stream shifting occurred with a catchment area of 10,000 m2 or more and a slope inclination of less than 20°. In both regions affected by landslides and stream shifting, the logarithmic stream power index (logSPI), a natural logarithmic representation of the conventional stream power index (SPI), could describe the characteristics roughly. A logSPI value of 4 to 5 represented landslides, and 5 and greater represented stream shifting, with the probability of landslides and stream shifting occurring in areas with these values exceeding 99.5% and 99.9%, respectively. Therefore, the logSPI was utilized to roughly classify the terrain and geomorphological condition and the points among the unburnt, unlogged-burnt, and logged-burnt areas were compared. Even when the logSPI was similar, the damage was much more severe in the burnt areas. On slopes with a logSPI of approximately 4, there were signs of erosion in the logged-burnt area, and the increase in fallen trees was significant in the unlogged-burnt area, while there were no substantial changes in the unburnt area. In valleys with a logSPI of 8 or greater, sediment and woody debris were significantly deposited in the burnt areas following the postfire rain, while there were few changes in the unburnt area.

Multi-temporal UAV Imaging-Based Mapping of Chlorophyll Content in Potato Crop

Spectral indices based on unmanned aerial vehicle (UAV) multispectral images combined with machine learning algorithms can more effectively assess chlorophyll content in plants, which plays a crucial role in plant nutrition diagnosis, yield estimation and a better understanding of plant and environment interactions. Therefore, the aim of this study was to use UAV-based spectral indices deriving from UAV-based multispectral images as inputs in different machine learning models to predict canopy chlorophyll content of potato crops. The relative chlorophyll content was obtained using a SPAD chlorophyll meter. Random Forest (RF), support vector regression (SVR), partial least squares regression (PLSR) and ridge regression (RR) were employed to predict the chlorophyll content. The results showed that RF model was the best performing algorithm with an R2 of 0.76, Root Mean Square Error (RMSE) of 1.97. Both RF and SVR models showed much better accuracy than PLSR and RR models. This study suggests that the best models, RF model, allow to map the spatial variation in chlorophyll content of plant canopy using the UAV multispectral images at different growth stages.

The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing

Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observations used to predict maize yield. It is also unclear how much improvement can be gained using multi-temporal compared to mono-temporal data. We used a systematic scheme to address those gaps employing UAV multispectral observations at nine development stages of maize (from second-leaf to maturity). Next, the spectral and texture indices calculated from the mono-temporal and multi-temporal UAV images were fed into the Random Forest model for yield prediction. Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (R2 increased by 8.1% and RMSE decreased by 27.4%). For single temporal UAV observation, the fourteenth-leaf stage was the earliest suitable time and the milking stage was the optimal observing time to estimate grain yield. For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (R2 = 0.93, RMSE = 0.77 t·ha−1). Furthermore, we found that the Normalized Difference Red Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), and dissimilarity of the near-infrared image at milking stage were the most promising feature variables for maize yield prediction.

Quantitative Evaluation of Gully Erosion Using Multitemporal UAV Data in the Southern Black Soil Region of Northeast China: A Case Study

The black soil region of northeast China is experiencing severe gully erosion. The lack of periodic, high-resolution, short–medium-term, annual, and seasonal observations considerably limit the comprehensive understanding of the processes and mechanisms of gully erosion caused by multiple forces at the watershed scale. Therefore, in this study, we periodically monitored the geomorphic, morphological, and volume changes of a stabilized gully both annually and seasonally in a small agricultural watershed (6 ha) in the southern black soil region in northeast China based on the centimeter-level resolution of unmanned aerial vehicle (UAV)-derived orthoimages and digital terrain models (DTMs) from 2015 to 2020. Compared with submeter-resolution satellite images, the multitemporal UAV data exhibited strong adaptability and various advantages for the assessment of short–medium-term (≤5 years) gully erosion rates in this region. The results demonstrated that the gully has an actively retreating headcut that was always the main source of sediment yield. The linear, areal, and volumetric gully headcut retreat (GHR) rates were 0.74 m year−1, 7.29 m2 year−1, and 9.66 m3 year−1, respectively. GHR in the rainy season accounted for 94.62% of the annual linear erosion and 87.64% of the areal erosion. In particular, sidewall collapse and gully head expansion dominated in the early rainy season, which accounted for 66.67% of the annual linear erosion and 49% of the areal erosion. Our results provide high-resolution orthoimages and a DTM time series produced by a UAV to evaluate short–medium-term (5 years) GHR rate and quantify the contribution of freeze–thaw processes, snowmelt, and rainfall to gully erosion in the region. The findings contribute to understanding the gully erosion processes induced by multiple forces in the southern black soil region of northeast China.

Kinematics of an Alpine rock glacier from multi-temporal UAV surveys and GNSS data

The quantification of rock glacier dynamics has gained increasing importance in recent years. In this study, the spatial and temporal flow patterns of perennially frozen debris in the active Gran Sometta rock glacier (Western Italian Alps) were investigated with repeated Unmanned Aerial Vehicle (UAV) surveys (2016–2019), Global Navigation Satellite System (GNSS) campaigns (2012−2020), geophysical prospections (2015) and ground surface temperature data (2014–2020).UAV data were used to generate maps of changes and elevation differences of the rock glacier surface by 3D point cloud comparison to evaluate surface lowering and accumulation processes. Horizontal velocities were quantified by an automatic image correlation technique and the results were then compared with horizontal surface velocities from GNSS measurements on selected points. The horizontal velocities estimated with the automatic method agree well with the GNSS velocities with an R2 = 0.99 and a RMSE lower than 0.07 m/y.Point cloud comparisons show surface lowering in the orographic left-hand side of the terminal part and in the central body of the rock glacier. The upper part exhibits almost absence of subsidence and any movement. This is explained by the lack of permafrost in this sector due to its overriding by the development of a small glacier during the Little Ice Age. As a result of the downslope movement, zones of surface rising occurred at the advancing front and at the moving ridge and furrow complexes. Surface velocity decreases from the orographic left to the right-hand side of the rock glacier tongue, where a thaw subsidence of up to 0.05 m/y was also observed. According to the GNSS measurements, the range of flow velocity of the rock glacier increased from 0.17–1.1 m/y in 2013 to 0.21–1.45 m/y in 2015 and then decreased until 2018 when the smallest surface velocity is detected. Since 2018, the creep velocities gradually started to increase again reaching values of 0.23 m/y up to a maximum of 1.9 m/y in the orographic left-hand side of the rock glacier tongue. This agrees with observations from other rock glaciers in the European Alps in recent decades.The complex Gran Sometta rock glacier dynamics can be explained by the heterogeneous distribution of permafrost and related subsurface perennially frozen ground which is thick enough (about 20–30 m) for permafrost creep to occur. Creep rates of the rock glacier permafrost depend also on the ground thermal regime: annual warmer surface conditions promote an acceleration of the creep rates within the rock glacier permafrost, whereas ground surface cooling causes a slight deceleration.

A volumetric change detection framework using UAV oblique photogrammetry – a case study of ultra-high-resolution monitoring of progressive building collapse

ABSTRACT In this paper, we present a case study that performs an unmanned aerial vehicle (UAV) based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event. Multi-temporal oblique photogrammetry images are collected with 3D point clouds generated at different stages of the demolition. The geometric accuracy of the generated point clouds has been evaluated against both airborne and terrestrial LiDAR point clouds, achieving an average distance of 12 cm and 16 cm for roof and façade respectively. We propose a hierarchical volumetric change detection framework that unifies multi-temporal UAV images for pose estimation (free of ground control points), reconstruction, and a coarse-to-fine 3D density change analysis. This work has provided a solution capable of addressing change detection on full 3D time-series datasets where dramatic scene content changes are presented progressively. Our change detection results on the building demolition event have been evaluated against the manually marked ground-truth changes and have achieved an F-1 score varying from 0.78 to 0.92, with consistently high precision (0.92–0.99). Volumetric changes through the demolition progress are derived from change detection and have been shown to favorably reflect the qualitative and quantitative building demolition progression.

Habitat classification using convolutional neural networks and multitemporal multispectral aerial imagery

The monitoring of threatened habitats is a key objective of European environmental policies. Due to the high cost of current field-based habitat mapping techniques, there is keen interest in proposing solutions that can reduce cost through increased levels of automation. Our study aims to propose a habitat mapping solution that benefits both from the merits of convolutional neural networks (CNNs) for image classification tasks, as well as from the high spatial, spectral, and multitemporal unmanned aerial vehicle image data, which shows great potential for accurate vegetation classification. The proposed CNN-based method uses multitemporal multispectral aerial imagery for the classification of threatened coastal habitats in the Maharees (Ireland) and shows a high level of classification accuracy.

The use of UAV remote sensing for observing lava dome emplacement and areas of potential lahar hazards: An example from the 2017–2019 eruption crisis at Mount Agung in Bali

Mount Agung (the highest volcano in Bali, Indonesia) began to erupt on November 21, 2017, after having been dormant for 53 years. More than 100,000 people were evacuated within the hazard zone between September 2017 (when the highest volcanic alert was issued) and early 2018. The eruptions continued until June 2019, accompanied by at least 110 explosions. During the eruptive crisis, the observation of the lava dome's emplacement was essential for mitigating the potential hazard. Details of the lava dome growth, including the volumetric changes and effusion rates, provide valuable information about potential eruption scenarios and lahar depositions. In this paper, the essential role of multi-temporal unmanned aerial vehicle (UAV) images in the monitoring of Mt. Agung's lava dome, and in determining the areas of potential lahar hazards during the crisis between 2017 and 2019 is described. A fixed-wing UAV was launched outside the hazard zone to photograph the lava dome on five occasions. Image enhancement, machine learning, and photogrammetry were combined to improve image quality, remove point clouds outliers, and generate digital terrain models (DTMs) and orthoimages. The analysis of the obtained DTMs and orthoimages resulted in qualitative and quantitative data highlighting the changes inside the crater and on the surrounding slopes. These results reveal that, from November 25 to December 16, 2017, the lava dome grew vertically by 126 m and reached a volume of 26.86 ± 0.64 × 106 m3. In addition, its surface experienced a maximal uplift of approximately 52 m until July 2019 with the emergence of a new dome with a volume estimated at 9.52 ± 0.086 × 106 m3. The difference between the DTMs of 2017 and 2019 reveals the total volume of erupted material (886,100 ± 8000 m3) that was deposited on the surrounding slopes. According to the lahar inundation simulation, the deposited material may cause dangerous lahars in 21 drainages, which extend in the north (N), north-east (N-E), south (S), south-east (S-E), and south-west (S-W) sectors of the volcano. This paper presents the use of UAV remote sensing for the production of high-spatial resolution DTMs, which can be used to both observe the emplacement of a lava dome, and to identify areas with potential lahar risk during a volcano crisis.

Spatiotemporal distribution and evolution characteristics of successive landslides on the Heifangtai tableland of the Chinese Loess Plateau

Successive landslides leave geomorphic imprints on loess tableland edge hillslopes and dominate the morphologic evolution process. Here, on the basis of long-term landslide inventory, multitemporal unmanned aerial vehicle (UAV) surveys and field investigations, we examined the spatiotemporal distribution of successive landslides and their evolution characteristics in a typical loess tableland, Gansu Province, China. We found that the hot spots of successive landslides changed during the survey period and the hillslopes around the loess tableland experienced different evolutionary stages. Landslide areas are characterized by lower values of local relief, elevation and slope gradient than non-landslide areas and are more exposed to south, southeast and east orientations. Agricultural irrigation-induced successive landslides are responsible for the recession of the loess tableland area, which accelerates the morphological evolution process of the loess tableland. Under the influence of path dependency, more subsequent landslides may occur in a certain period. Moreover, this influence of earlier landslides on subsequent landslides (i.e., path dependency) will decrease with increasing time intervals (year). Two models (lateral and lengthwise development patterns) summarized in this study can describe the typical evolution processes caused by successive landslides in loess tableland areas. The findings of this research provide insights into the spatiotemporal distribution and morphologic evolution processes of agricultural irrigation-induced successive landslides, which is useful for understanding the evolutionary process in loess tableland areas.

Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

Land surface phenology (LSP) enables global-scale tracking of ecosystem processes, but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices (VIs). Due to the importance of drylands for biodiversity, food security, and the carbon cycle, it is necessary to understand the limitations in measuring dryland dynamics. Here, using simulated data and multitemporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, VI uncertainty, and two different detection algorithms. Using simulated data, we found that plants with distinct VI signals, such as deciduous shrubs, can require up to 60% fractional cover to consistently detect LSP. Evergreen plants, with lower seasonal VI amplitude, require considerably higher cover and can have undetectable phenology even with 100% vegetation cover. Our evaluation of two algorithms showed that neither performed the best in all cases. Even with adequate cover, biases in phenological metrics can still exceed 20 days and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. We showed how high-resolution UAV imagery enables LSP studies in drylands and highlighted important scale effects driven by within-canopy VI variation. With high-resolution imagery, the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants, enabling the tracking of phenology at the individual level. Drylands thus have the potential to become an exemplary environment for future LSP research.

UAV-Derived Data Application for Environmental Monitoring of the Coastal Area of Lake Sevan, Armenia with a Changing Water Level

The paper presents the range and applications of thematic tasks for ultra-high spatial resolution data from small unmanned aerial vehicles (UAVs) in the integral system of environmental multi-platform and multi-scaled monitoring of Lake Sevan, which is one of the greatest freshwater lakes in Eurasia. From the 1930s, it had been subjected to human-driven changing of the water level with associated and currently exacerbated environmental issues. We elaborated the specific techniques of optical and thermal surveys for the different coastal sites and phenomena in study. UAV-derived optical imagery and thermal stream were processed by a Structure-from-Motion algorithm to create digital surface models (DSMs) and ortho-imagery for several key sites. UAV imagery were used as additional sources of detailed spatial data under large-scale mapping of current land-use and point sources of water pollution in the coastal zone, and a main data source on environmental violations, especially sewage discharge or illegal landfills. The revealed present-day coastal types were mapped at a large scale, and the net changes of shoreline position and rates of shore erosion were calculated on multi-temporal UAV data using modified Hausdorff’s distance. Based on highly-detailed DSMs, we revealed the areas and objects at risk of flooding under the projected water level rise to 1903.5 m along the west coasts of Minor Sevan being the most popular recreational area. We indicated that the structural and environmental state of marsh coasts and coastal wetlands as potential sources of lake eutrophication and associated algal blooms could be more efficiently studied under thermal UAV surveys than optical ones. We proposed to consider UAV surveys as a necessary intermediary between ground data and satellite imagery with different spatial resolutions for the complex environmental monitoring of the coastal area and water body of Lake Sevan as a whole.