Thermal Infrared Sensor Research Articles

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

Estimation of Land Surface Temperature from Chinese ZY1-02E IRS Data

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data.

PENGGUNAAN SINAR INFRA MERAH UNTUK DETEKSI PANAS BUMI DAERAH SANGKANHURIP, KUNINGAN, JAWA BARAT

Geothermal resources can be explored with aerial photographs followed by surveys and analysis of geological, geochemical and geophysical data as well as shallow drilling. This research was conducted to determine the potential of geothermal resources using a camera capable of capturing infrared rays from objects and photo targets.Geothermal exploration is generally carried out with an initial survey using remote imagery and aerial photography techniques. Taking photos using a tool equipped with a Thermal Infrared Sensor (TIRS), namely a camera equipped with Forward Looking InfraRed (FLIR).The initial survey in this study was conducted at five locations of geothermal springs in the Sangkanhurip area, Kuningan, West Java. From these five locations using FLIR cameras detected geothermal resources with hot spring temperatures between 30 to 40 degrees Celsius.To find out the types of rocks that exist, a geological survey was carried out to make a geological map. The data and information obtained are then analyzed to obtain a map that displays geology with surface temperature data in the study area.This map can be utilized by companies interested in exploring and producing geothermal energy for use as a source of energy for Geothermal Power Generation (PLTP). Further analysis can be carried out with the Geochemical Method for predicting subsurface temperature. This discovery fulfills the research objective of detecting geothermal resources using infrared rays.

Temperature Accuracy Analysis of Land Cover Around Urban Rivers Using Thermal Infrared Sensor–Mounted UAV

Temperature Accuracy Analysis of Land Cover Around Urban Rivers Using Thermal Infrared Sensor–Mounted UAV

ИЗМЕНЕНИЕ ОЛЕДЕНЕНИЯ СЕВЕРНОГО СКЛОНА ИЛЕ АЛАТАУ ЗА СЕМИДЕСЯТИЛЕТНИЙ ПЕРИОД

The problem of shortage of water resources, especially fresh water and runoff during the growing season, is relevant for all countries in arid regions. In Central Asia, including Kazakhstan, mountainous regions are one of the main suppliers of fresh water. The climate-caused degradation of mountain glaciation causes changes in mountain ecosystems, having direct impact to human life, causes changes in hydrological cycles, biogeochemistry of rivers and glacial lakes, affecting the quality and availability of water resources. The article presents the results of the seventy-year monitoring of changes in the area of glaciation of the northern slope of Ile Alatau. According to the satellite surveys of the Landsat 5 TM+ and Landsat 8, 9 OLI TIRS sensors for 1990, 2006, 2014 and 2022, the state of glaciation was assessed. To analyze the degradation of the glacial system were used the data from previous inventories of this region - as of 1955, 1974, 1979, 1990, 2008. For 67 years, the glaciation of the northern slope of Ile Alatau has decreased in area by 140.4 km2 (by 49%), losing 2.1 km2 or 0.72% of its area per year. The rate of degradation has not changed much in comparison with previous studies of the glaciation of this region. Based on the forecast, by the end of the 21st century, the glaciation of the northern slope of the Ile Alatau may disappear, of course, by the remaining conditions of degradation.

Assessing Urban Land Surface Temperature Fluctuations Amidst the Covid-19 Pandemic: A Landsat 8 and Landsat 9 Study of Bandar Lampung City

Community activities before, during, and after the Covid-19 pandemic have undergone significant changes. This is due to the limitation and exemption of activities set by the government which can also result in changes in surface temperature. Physical separation from the Large-Scale Social Restrictions (PSBB) has led to a decrease in communal activities like traffic and the industrial sector, which has a policy of allowing most employees to work from home. This study intends to examine variations in Bandar Lampung surface temperature that took place in 2020, 2021, and. Identifying surface temperature using remote sensing technology, including Landsat 8 and Landsat 9 images. Both of these images have advantages with a large number of bands, especially the presence of a Thermal Infrared Sensor (TIRS) wavelength which has a sensitivity to detect temperature. Utilization of this wavelength can distinguish parts of the earth's surface that have a hotter temperature than the surrounding area. The land surface temperature (LST) approach can be used to determine the dynamics of surface temperature variations before, during, and after the Covid-19 epidemic. Image processing and analysis are done using Google Earth Engine. The results of the analysis of surface temperatures before Covid, the image recording time in 2020 has a value range of 13oC - 32oC, during the occurrence of Covid, the 2021 recording time has a value range of 3oC - 33oC, while after Covid, the 2022 recording time has a value range of 18oC - 32oC.Keywords: Covid-19, Landsat 8, Landsat 9, LST

A Smart Crop Water Stress Index-Based IoT Solution for Precision Irrigation of Wine Grape.

The Scholander-type pressure chamber to measure midday stem water potential (MSWP) has been widely used to schedule irrigation in commercial vineyards. However, the limited number of sites that can be evaluated using the pressure chamber makes it difficult to evaluate the spatial variability of vineyard water status. As an alternative, several authors have suggested using the crop water stress index (CWSI) based on low-cost thermal infrared (TIR) sensors to estimate the MSWP. Therefore, this study aimed to develop a low-cost wireless infrared sensor network (WISN) to monitor the spatial variability of MSWPs in a drip-irrigated Cabernet Sauvignon vineyard under two levels of water stress. For this study, the MLX90614 sensor was used to measure canopy temperature (Tc), and thus compute the CWSI. The results indicated that good performance of the MLX90614 infrared thermometers was observed under laboratory and vineyard conditions with root mean square error (RMSE) and mean absolute error (MAE) values being less than 1.0 °C. Finally, a good nonlinear correlation between the MSWP and CWSI (R2 = 0.72) was observed, allowing the development of intra-vineyard spatial variability maps of MSWP using the low-cost wireless infrared sensor network.

A comparative spectral assessment approach of SEBAL and SEBS for actual evaporation estimation in Ardabil Province

Evapotranspiration is a crucial process in the Earth's water and climate cycle, responsible for transforming water from liquid to water vapor. This transformation plays a vital role in the global water cycle and has a significant impact on the climate, weather patterns, and precipitation in various regions. In this study, actual evapotranspiration in the Mughan plain of Ardabil province has been estimated using spectral data from the OLI and TIRS sensors of the Landsat 8 satellite. The SEBAL (Surface Energy Balance Algorithm for Land) and SEBS (Surface Energy Balance System) methods have been employed to calculate these phenomena. SEBAL is an energy balance algorithm designed for land surfaces, utilizing satellite data to estimate actual evapotranspiration accurately. Similarly, SEBS is a surface energy balance system that provides a more precise estimation of evapotranspiration and transpiration rates. Data from the OLI and TIRS sensors of Landsat 8 were collected from the study area for this research. By applying the SEBAL and SEBS methods to these data, actual evapotranspiration values in the Mughan plain were obtained. The results indicated that SEBAL showed a broader range of actual evapotranspiration values (0.74 to 5.8 mm) compared to SEBS (1.25 to 8.85 mm), highlighting its ability to distinguish different regions with varying evapotranspiration rates. However, SEBAL's implementation is more computationally demanding than SEBS, making the calculation process time-consuming. Overall, both algorithms demonstrated relatively high capabilities in calculating instantaneous evapotranspiration using spectral data. The findings of this study can be valuable for water resources and agricultural management in the research area, as well as for water resource planning and environmental studies.

Spatial characterization of global heat waves using satellite-based land surface temperature

The warming world greatly suffers the increase of frequency, severity and duration of heat wave events, which would cause the significant societal and environmental damages and implications from local to global scales. In order to eliminate the limitations of site observations in accurate spatial extent identification and large scale monitoring, this study tried to investigate global heat waves in 2018 using spatial datasets of land surface temperature (LST) derived from AQUA TIR sensors. A 15-year daily maximum LST dataset was used to identify the hot days and heat wave events using the relative threshold method based on the probability density function analysis of LST sequences, then they were adopted in analyzing spatial patterns of duration, frequency and intensity of heat wave events using heat wave numbers (HWN), the frequency of the appeared heat wave days (HWF) and the amplitude of heat wave impacts (HWA) indices, respectively. Our results indicated that more heat wave events and longer heat wave duration happened in low and middle latitudes under the warmer land surface in 2018 than last decade, especially many heat wave events occurred in the environmentally fragile regions and the dense population area. Urban regions achieved the largest HWA by 45.5 ± 6.9℃. Both annual HWF and HWN highlighted seven hot spot regions influenced by heat waves, and most of them distributed around the latitude 30°N and the latitude 30°S. Extreme heat wave of hot spot regions in the Southern Hemisphere mainly happened in winter and spring in contrast to that in North Hemisphere appeared in summer and autumn. Dry climate could contribute to the occurrence of heat waves, and heat wave indices variations also implied the compound consequences between heat waves and drought. Our findings demonstrate that remote sensing datasets are capable of providing the continuous whole-Earth coverage of heat wave changes, and they would play more important roles in preventing or mitigating the impacts of extreme heat events on people and natural environment.

Enhancing leaf area index and biomass estimation in maize with feature augmentation from unmanned aerial vehicle-based nadir and cross-circling oblique photography

Rapid and accurate estimation of plant phenotypes plays a vital role in effective breeding and management of maize crops. Unmanned aerial vehicle (UAV) platforms are emerging as a valuable tool in the assessment of crop phenotypes, offering a promising avenue to enhance the accuracy and efficiency of phenotypic analysis. This study executed two UAV photography methods to acquire aerial images for the estimation of leaf area index (LAI) and above-ground biomass (AGB) in summer maize. Nadir photography was implemented to obtain data from three sources including multi-spectral (MS), RGB, and thermal infrared (TIR) sensors. Additionally, RGB imaging-based cross-circling oblique (CCO) photography was implemented to obtain accurate 3D point cloud data. The nadir photography data was processed to generate orthomosaic image and extract features including vegetation index, canopy cover, canopy height, canopy temperature, and textural information. Features including canopy occupation volume, plant area index, canopy cover, and canopy height were extracted from the CCO photography-derived point cloud data. Furthermore, a data augmentation method, called linear regression-based feature augmentation (LRFA), was proposed for augmenting features extracted from nadir and CCO photography. The results showed that the introduction of CCO photography improved the accuracy of LAI and AGB estimation during the big trumpet stage and LAI estimation during the milk stage compared to the integrated multi-source nadir photography data. Notably, the LRFA derived features outperformed raw features in the majority of modeling scenarios, with the random forest achieved an average accuracy (R2) improvement of 6.1% for LAI estimation and 3.7% for AGB estimation. This study highlights the significance of combining different photography technologies and feature augmentation method for estimating maize phenotypes, providing novel opportunities for crop growth monitoring in modern agriculture.

Monitoring of wetland turbidity using multi-temporal Landsat-8 and Landsat-9 satellite imagery in the Bisalpur wetland, Rajasthan, India

Satellite imagery has emerged as the predominant method for performing spatial and temporal water quality analyses on a global scale. This study employs remote sensing techniques to monitor the water quality of the Bisalpur wetland during both the pre and post-monsoon seasons in 2013 and 2022. The study aims to investigate the prospective use of Landsat-8 (L8) and Landsat-9 (L9) data acquired from the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the temporal monitoring of turbidity. Concurrently, the study examines the relationship of turbidity with water surface temperature (WST) and chlorophyll-a (Chl-a) concentrations. We utilized visible and near-infrared (NIR) bands to conduct a single-band spectral response analysis of wetland turbidity. The results reveal a notable increase in turbidity concentration in May 2022, as this timeframe recorded the highest reflectance (0.28) in the NIR band. Additionally, the normalized difference turbidity index (NDTI) formula was used to assess the overall turbidity levels in the wetland. The results indicated that the highest concentration was observed in May 2013, with a value of 0.37, while the second-highest concentration was recorded in May 2022, with a value of 0.25. The WST was calculated using thermal band-10 in conjunction with Chlorophyll-a, utilizing the normalized difference chlorophyll index (NDCI). The regression analysis shows a positive correlation between turbidity and WST, as indicated by R2 values of 0.41 in May 2013 and 0.40 in May 2022. Furthermore, a robust positive relationship exists between turbidity and Chl-a, with a high R2 value of 0.71 in May 2022. These findings emphasize the efficacy of the L8 and L9 datasets for conducting temporal analyses of wetland turbidity, WST, and Chl-a. Additionally, this research underscores the critical role of satellite imagery in assessing and managing water quality, particularly in situations where in-situ data is lacking.

Deriving fine-scale patterns of sea surface temperature in coral reef habitats using the Landsat 8 thermal infrared sensor

The available sea surface temperature (SST) products are too coarse to assess the fine-scale (<1 km) SST variations related to coral bleaching. In this study, we proposed an optimal SST inversion model using Landsat 8 thermal infrared sensor (TIRS) images to derive fine-scale SST patterns in the coral reef habitats of the Xisha Islands, South China Sea. Our study included two parts: 1) six SST inversion models were developed using the radiative transfer method and the split window (SW) algorithm in the hot season and cool season, from which the optimal SST inversion model was determined; and 2) the optimal model was applied to 47 Landsat 8 TIRS images to derive the SST spatial and temporal pattern among the geomorphic zones of six reefs in hot and cool season conditions. Compared with the measured sea temperature data and the verified MODIS SST products, the SST6 model using the SW algorithm was optimal, with an RMSE of approximately 0.64°C in the hot season. The average SST results usually had a pattern of reef flat > lagoon > reef slope/offshore sea. The reef flat was usually approximately 0.05°C–0.2°C hotter than the lagoon in the hot season. The SST in the lagoon also increased from south to north and the shallow lagoon was usually warmer than the deep lagoon in the hot season. Our results suggested that scleractinian corals in the reef flat and the lagoon were more susceptible to bleaching-level thermal stress than other geomorphic zones. During the cool season, the SST fluctuated markedly among coral reefs and geomorphic zones.

Faba bean and pea harvest index estimations using aerial-based multimodal data and machine learning algorithms.

Early and high-throughput estimations of the crop harvest index (HI) are essential for crop breeding and field management in precision agriculture; however, traditional methods for measuring HI are time-consuming and labor-intensive. The development of unmanned aerial vehicles (UAVs) with onboard sensors offers an alternative strategy for crop HI research. In this study, we explored the potential of using low-cost, UAV-based multimodal data for HI estimation using red-green-blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors at 4 growth stages to estimate faba bean (Vicia faba L.) and pea (Pisum sativum L.) HI values within the framework of ensemble learning. The average estimates of RGB (faba bean: coefficient of determination [R2] = 0.49, normalized root-mean-square error [NRMSE] = 15.78%; pea: R2 = 0.46, NRMSE = 20.08%) and MS (faba bean: R2 = 0.50, NRMSE = 15.16%; pea: R2 = 0.46, NRMSE = 19.43%) were superior to those of TIR (faba bean: R2 = 0.37, NRMSE = 16.47%; pea: R2 = 0.38, NRMSE = 19.71%), and the fusion of multisensor data exhibited a higher estimation accuracy than those obtained using each sensor individually. Ensemble Bayesian model averaging provided the most accurate estimations (faba bean: R2 = 0.64, NRMSE = 13.76%; pea: R2 = 0.74, NRMSE = 15.20%) for whole growth stage, and the estimation accuracy improved with advancing growth stage. These results indicate that the combination of low-cost, UAV-based multimodal data and machine learning algorithms can be used to estimate crop HI reliably, therefore highlighting a promising strategy and providing valuable insights for high spatial precision in agriculture, which can help breeders make early and efficient decisions.

On-orbit monitoring flying aircraft day and night based on SDGSAT-1 thermal infrared dataset

Aerial moving target detection is an important technology to ensure the safety of civil flight and airspace. Different from ground-based active radar and space-based visible imager, on-orbit thermal infrared sensor (TIS), which records the temperature difference between background and targets, is proved to be a stable means of monitoring the aircraft throughout the day. Limited by low spatial resolution and dataset scarcity, there are few existing the research of space-based aircraft detection based on TIS. In this paper, a new multi-model joint method to detect on-orbit flying aircrafts with only 1 × 1 to 2 × 2 pixels is proposed and validated. And the first three-band thermal infrared flying aircrafts dataset (TIFAD) with 30 m spacial resolution is produced from SDGSAT-1 thermal infrared images, including the analysis of its infrared feature. Since the point target cannot by distinguished by the shape, all samples are selected by an improved aircraft contrails detection algorithm based on progressive probabilistic Hough transform, from more than 325GB SDGSAT-1 TIS remote sensing images (RSIs). And training by TIFAD, state-of-the-art YOLOv8 algorithm is applied to more than 17,767 medium spatial resolution thermal infrared images, and the potential aircrafts (with contrails or non-contrails) in RSIs are effectively detected. Moreover, the giga floating point operations (GFLOPs) of detecting flying targets from RSIs with 300 km width range is only 8.9. This paper is believed to provide a technical support of detecting aircraft targets around the globe throughout the day, and supplement on-orbit flying targets dataset of TIS images.

Improving estimation of maize leaf area index by combining of UAV-based multispectral and thermal infrared data: The potential of new texture index

Crop leaf area index (LAI) is one of the important indicators to evaluate crop growth and guide field management, and can be used to predict crop yield. Spectral and thermal information extracted by multispectral (MS) and thermal infrared (TIR) sensors mounted on an unmanned aerial vehicle (UAV) can be used for LAI estimation. Image texture is sensitive to the changes in crop surface grayscale or color characteristics, and can be combined with spectral and thermal information to estimate the LAI. But single texture metric has limitations in LAI estimation. Therefore, the purpose of this study is to construct new texture indices based on texture metrics extracted from MS and TIR images, and combined spectral and thermal information to enhance the estimation accuracy of maize LAI. Three replicates of maize experiments under different irrigation treatments were conducted in 2020. The MS and TIR sensors were mounted on a UAV to acquire maize canopy images during critical growth stages and acquire field LAI value of samples synchronously. The LAI estimation models were established using MS data, TIR data, as well as their combination. These models were constructed by Back Propagation Neural Network (BPNN), Partial least squares regression (PLSR), and Random Forest Regression (RFR). Finally, the performance of LAI estimation models was evaluated by the coefficient of determination (R2), root mean square error (RMSE) and relative root mean square error (rRMSE). Results shown that: (i) Among the eight kinds of texture metrics extracted from MS and TIR images, the texture metric mean (MEA) has the best performance. Compared with single texture metrics, texture indices constructed by different metrics has stronger correlation with LAI. (ii) Adding texture indices to estimation models significantly improved model accuracy, especially multispectral three-texture index(MS-TTI) has higher LAI estimation potential than thermal infrared three-texture index(TIR-TTI). (iii) Compared with the use of MS or TIR data alone, the estimation model constructed by combining MS data and TIR data have better performance. The best estimation model obtained by the RFR method (R2 = 0.862, RMSE = 0.246 and rRMSE = 10.20 %) further improved the LAI estimation of maize, with R2 increasing by 6.55 % and 14.48 %, respectively. In conclusion, the combination of MS and TIR data can effectively improve the estimation accuracy of maize LAI, and also provide a feasible method for monitoring crop growth.

Detecting Thermal Anomalies In Lahendong Geothermal Prospect Using Aster TIR and Landsat 8

This study intends to determine geothermal anomalies area using remote sensing data in the form of Landsat 8 and ASTER satellite imagery data which have Thermal Infrared Sensor (TIRS). Through pre-processing like georeferencing, radiometric calibration, and atmospheric correction, the Landsat 8 TIRS and ASTER data were wont to invert the land surface temperature of the study area during the daytime and night time using the inversion of planck function and emissivity separation algorithm. Result shows the land surface temperatures during daytime and night time of four natural land cover —water, vegetation, built up area, and bare soil—were classified and analyzed. According to the results, vegetation and bare soil show relatively thermal anomalies during the day and comparatively cold anomalies during the night. Otherwise water shows relatively cold anomalies during the day and relatively thermal anomalies during the night. Meanwhile built up area shows relatively thermal anomalies during the day and cold anomalies during the night. Superimposed and calculating mean of the night and day surface temperature can adequately eliminate the relatively cold/thermal anomalies of land cover caused by solar radiation, thus effectively highlighting geothermal anomalies. Thus, Nine geothermal anomalies areas were successfully extracted.

Predicting land use/land cover changes in the Santchou Wildlife Reserve (Santchou, West-Cameroon) using a CA-Markov model

Over the last three decades, the Santchou Wildlife Reserve (SWR) located in the West region of Cameroon has experienced rapid degradation. Land reclamation and population growth have significantly contributed to meeting the increasing demands for foods and urban development. In this study, Landsat 4–5 Thematic Mapper (TM) images of 1988 and 2005, and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) image of 2022 are firstly used for calculating and analysing area changes and then spatial distribution of land use/land cover changes (LULCC) in the SWR for a time span 1988–2005–2022. Secondly, the spatial distribution of LULCC from 2022 to 2027 and from 2027 to 2032 was simulated using a Cellular Automata (CA)-Markov model that allows to predict future changes and identify the possible patterns of evolution. Results exhibit six major LULC classes including Mountain Forest, Degraded Lowland Forest, Lowland Forest, Degraded Mountain Forest, Built-up Area and Cultivated Land. The overall accuracies of LULC classification reached 89.71‒92.24 %, with Kappa coefficients of 0.87‒0.92. However, the prediction model was validated with an overall good agreement of 90.51 %. In general, two significant patterns of change were evidenced and characterized by the rapid expansion of Cultivated Land from 158.6 to 736.4 ha between 1988 and 2022, and could reach 809.0 ha in 2032, while Built-up Area displays a surface change from 12.4 to 29.3 ha, which is expected to attend 67.3 ha in 2032. Change patterns from the past to the future are also marked by a decline of Mountain Forest (2107.4 to 1594.4 ha), Degraded Lowland Forest (1450.1 to 1024.5 ha) and Degraded Mountain Forest (954.6 to 860.6 ha) at the expense of Lowland Forest (2332.5 to 2659.8 ha). Human activities influenced by socio-economic issues remain the main factor of changes in the SWR. Despite the limitations of the CA-Markov model due to the non-consideration of socio-economic factors, this study appears as a new opportunity to develop effective strategies for managing LULCC and protecting ecological biodiversity in the SWR.

Rapid monitoring of tea plants under cold stress based on UAV multi-sensor data

In the context of climate change, extreme weather events, represented by frost injury, are increasingly having a negative impact on the growth of tea plants. This has brought huge losses to the tea industry. Traditionally, the freezing injury of tea plants in the field was assessed by vision. This is labor-intensive and subjective. In this research, multimodal remote sensing data from different periods of natural overwintering tea plantations were collected by using unmanned aerial vehicles (UAV) equipped with multispectral (MS), thermal infrared (TIR) and RGB sensors. And the physiological data of tea leaves on the same day were obtained to construct a tea cold injury score (TCIS). Then, a convolutional neural networks-gate recurrent unit (CNN-GRU) model was improved for estimating TCIS. To better compare the performance of CNN-GRU, a single GRU model and three classical machine learning models were also used for comparison. The study found that: (1) The multimodal data fusion was superior to the unimodal data. The best prediction results were achieved for the combined bimodal MS + RGB data (Rp2 = 0.862, RMSEP = 0.138, RPD = 2.220); (2) The CNN-GRU hybrid model was superior to the other four baseline models. The best effect was achieved based on the multivariate input of MS + RGB (Rp2 = 0.862) or MS + RGB + TIR (Rp2 = 0.850); (3) The accuracy of the model after removing soil features was lower than that of the model without background removal. Therefore, the TCIS-CNN-GRU model combined with multi-source remote sensing data can objectively and accurately evaluate the cold injury phenotype of tea plants, making the CNN-GRU model more scientific and promising.

Thermodynamic Entropy: A Natural Metric for Landcover Classification

Most landcover (LC) studies employ physical characteristics involving spectral reflectance. This study employs the concept of energy and entropy for LC classification. Satellite imageries from Landsat-8, the Operational Land Imager (OLI) and the Thermal Infra-Red Sensor (TIRS) captured at 100 m for three years (2013, 2015 and 2017) and resampled to a spatial resolution of 30 m for multispectral measurements were acquired. The normalized difference vegetation index (NDVI) of the study area was computed for both the wet (September/October) and dry (March) seasons of years 2013, 2015 and 2017, respectively; and the vegetation cover maps and landcover classification of the area based on the NDVI values were generated. Land surface temperature (LST) and net radiation of the surface area were computed and used as inputs for computing the surface entropy flux (SEF) of the study area. Overall, the dry season of 2017 had the highest vegetation cover while the wet season vegetation cover was highest in 2015. The high dry season vegetation cover in 2017 is attributable to higher level of living biomass than previous years. NDVI-based LC classification of the area showed an overlap in distinguishing between built-up areas and vegetation cover as well as between bare (land) surface and free water bodies. This is attributable to skewed measure of centrality for the sample distribution possibly resulting from the quality (resolution) of data used or sampling techniques. Free water bodies had the highest SEF values that fall within expected behavior for bodies of such state (liquids). The vegetation covers had the second highest SEF values for the entire site and the highest on land surface, which could be attributed to higher latent heat fluxes of such covers resulting from the evapotranspiration processes. The bare surfaces and built-up area of the study area were observed to have the lowest SEF values. The LC of the study area was reclassified based on SEF values via ground truthing i.e., by taking statistical distribution of well-known surface classes across the years under study. This was successful in distinguishing vegetation cover, surfaces of free water bodies and bare surfaces. A regression analysis revealed that LST influences SEF by up to 69 – 75% while vegetation cover has no appreciable influence on SEF. A two-sample t-test showed a significant (P = 0.05) difference between the SEF for bare surfaces and that for vegetation cover. All these presents SEF as a reliable natural metric for LC classification.

Quantifying Thermal Infra-Red directional anisotropy using Master and Landsat-8 simultaneous acquisitions

Satellite observations in the Thermal Infra-Red (TIR) domain provide valuable information on Land Surface Temperatures, Evapo-Transpiration and water use efficiency and are useful for monitoring vegetation health, agricultural practices and urban planning. By 2030, there will be 3 new high-resolution global coverage satellite TIR missions in space, all of them with fields of view larger than ± 30°. Directional anisotropy in TIR can affect the estimation of key application variables, such as temperature, and are typically studied by means of field campaigns or physical modelling. In this work, we have evaluated directional effects using simultaneous measurements from Landsat-8 and the ± 45°field of view MASTER airborne TIR sensor from NASA. Differences as high as 6 K are observed in the surface temperatures derived from these simultaneous observations. Those differences are attributed to directional effects, with the greatest differences associated with hotspot conditions, where the solar and satellite viewing directions align. Five well studied parametric directional models have then been fitted to the temperature differences, allowing the amplitude of the measured directional effects to be reduced below 1 K, with small variations between models. These results suggest that a simple correction for directional effects could be implemented as part of the ground segment processing for the upcoming missions.