RS Data Research Articles

Improving AGB estimations by integrating tree height and crown radius from multisource remote sensing.

Precise estimation of forest above ground biomass (AGB) is essential for assessing its ecological functions and determining forest carbon stocks. It is difficult to directly obtain diameter at breast height (DBH) based on remote sensing imagery. Therefore, it is crucial to accurately estimate the AGB with features extracted directly from RS. This paper demonstrates the feasibility of estimating AGB from crown radius (R) and tree height (H) features extracted from multi-source RS data. Accurate information on tree height (H), crown radius (R), and diameter at breast height (DBH) can be obtained through point clouds generated by airborne laser scanning (ALS) and terrestrial laser scanning (TLS), respectively. Nine allometric growth equations were used to fit coniferous forests (Larix principis-rupprechtii) and broadleaf forests (Fraxinus chinensis and Sophora japonica). The fitting performance of models constructed using only "H" or "R" was compared with that of models constructed using both combined. The results showed that the quadratic polynomial model constructed with "H+R" fitted the AGB estimation better in each vegetation type, especially in the scenario of mixed tall and short coniferous forests, in which the R2 and RMSE were 0.9282 and 25.30 kg (rRMSE 17.31%), respectively. Therefore, using high-resolution data to extract crown radius and tree height can achieve high-precision, global-scale estimation of forest above ground biomass.

Support Vector Machine Algorithm for Mapping Land Cover Dynamics in Senegal, West Africa, Using Earth Observation Data

This paper addresses the problem of mapping land cover types in Senegal and recognition of vegetation systems in the Saloum River Delta on the satellite images. Multi-seasonal landscape dynamics were analyzed using Landsat 8-9 OLI/TIRS images from 2015 to 2023. Two image classification methods were compared, and their performance was evaluated in the GRASS GIS software (version 8.4.0, creator: GRASS Development Team, original location: Champaign, Illinois, USA, currently multinational project) by means of unsupervised classification using the k-means clustering algorithm and supervised classification using the Support Vector Machine (SVM) algorithm. The land cover types were identified using machine learning (ML)-based analysis of the spectral reflectance of the multispectral images. The results based on the processed multispectral images indicated a decrease in savannas, an increase in croplands and agricultural lands, a decline in forests, and changes to coastal wetlands, including mangroves with high biodiversity. The practical aim is to describe a novel method of creating land cover maps using RS data for each class and to improve accuracy. We accomplish this by calculating the areas occupied by 10 land cover classes within the target area for six consecutive years. Our results indicate that, in comparing the performance of the algorithms, the SVM classification approach increased the accuracy, with 98% of pixels being stable, which shows qualitative improvements in image classification. This paper contributes to the natural resource management and environmental monitoring of Senegal, West Africa, through advanced cartographic methods applied to remote sensing of Earth observation data.

Estimating wheat evapotranspiration through remote sensing utilizing GeeSEBAL and comparing with lysimetric data

Accurate estimation of ET is vital for water resource management. In recent decades, researchers have focused on utilizing satellite imagery for this purpose. The use of RS data has enabled the development of new models that provide detailed spatial assessments. GeeSEBAL, an automated ET estimation tool, employs the SEBAL algorithm via GEE. The current version of GeeSEBAL utilizes Landsat images and ERA5 global reanalysis data to produce time series estimates. Landsat 8 images were processed into a 16-day time series spanning 2013–2022, specifically during the wheat growing season. To validate the GeeSEBAL model for 2013–2014, results were compared against lysimeter data. Subsequently, ET was calculated for the years 2015–2022. The evaluation of GeeSEBAL against lysimetric data, by metrics such as R2, RMSE, MAE, NSE, and NRMSE, yielded values of 0.94, 0.98, 0.07, 0.86, and 0.62, respectively. Those findings underscore the importance of GeeSEBAL for estimating wheat ET in regions with limited data availability.

Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types around Cheetham Wetlands, Port Phillip Bay, Australia

This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised classification. Technical approaches are based on using the ‘r.learn.train’ module, which employs the scikit-learn library of Python. The methodology includes the following algorithms: (1) random forest (RF), (2) support vector machine (SVM), (3) an ANN-based approach using a multi-layer perceptron (MLP) classifier, and (4) a decision tree classifier (DTC). The tested methods using AI demonstrated robust results for image classification, with the highest overall accuracy exceeding 98% and reached by the SVM and RF models. The presented scripting approach for GRASS GIS accurately detected changes in land cover types in southern Victoria over the period of 2013–2024. From our findings, the use of AI and ML algorithms offers effective solutions for coastal monitoring by analysis of change detection using multi-temporal RS data. The demonstrated methods have potential applications in coastal and wetland monitoring, environmental analysis and urban planning based on Earth observation data.

Diurnal Variation in Surface Incident Solar Radiation Retrieved by CERES and Himawari-8

The diurnal variation of surface incident solar radiation (Rs) has a significant impact on the Earth’s climate. Satellite-retrieved Rs datasets display good spatial and temporal continuity compared with ground-based observations and, more importantly, have higher accuracy than reanalysis datasets. Facilitated by these advantages, many scholars have evaluated satellite-retrieved Rs, especially based on monthly and annual data. However, there is a lack of evaluation on an hourly scale, which has a profound impact on sea–air interactions, climate change, agriculture, and prognostic models. This study evaluates Himawari-8 and Clouds and the Earth’s Radiant Energy System Synoptic (CERES)-retrieved hourly Rs data covering 60°S–60°N and 80°E–160°W based on ground-based observations from the Baseline Surface Radiation Network (BSRN). Hourly Rs were first standardized to remove the diurnal and seasonal cycles. Furthermore, the sensitivities of satellite-retrieved Rs products to clouds, aerosols, and land cover types were explored. It was found that Himawari-8-retrieved Rs was better than CERES-retrieved Rs at 8:00–16:00 and worse at 7:00 and 17:00. Both satellites performed better at continental sites than at island/coastal sites. The diurnal variations of statistical parameters of Himawari-8 satellite-retrieved Rs were stronger than those of CERES. Relatively larger MABs in the case of stratus and stratocumulus were exhibited for both hourly products. Smaller MAB values were found for CERES covered by deep convection and cumulus clouds and for Himawari-8 covered by deep convection and nimbostratus clouds. Larger MAB values at evergreen broadleaf forest sites and smaller MAB values at open shrubland sites were found for both products. In addition, Rs retrieved by Himawari-8 was more sensitive to AOD at 10:00–16:00, while that retrieved by CERES was more sensitive to COD at 9:00–15:00. The CERES product showed larger sensitivity to COD (at 9:00–15:00) and AOD (at 7:00–10:00) than Himawari-8. This work helps data producers know how to improve their future products and helps data users be aware of the uncertainties that exist in hourly satellite-retrieved Rs data.

Mapping of Erosion by Wind with RS Data and GIS (case study of Dedoplistskaro Municipality, Georgia)

Mapping of Erosion by Wind with RS Data and GIS (case study of Dedoplistskaro Municipality, Georgia)

Delineating the uranium anomalous zones using remote sensing and radiometric data: a case study from Gabal Umm Tinassib area, North Eastern Desert, Egypt

AbstractRemote sensing (RS) and airborne gamma-ray spectrometric (AGS) methods are utilized to delineate significant uranium zones and altered mineralization areas in Gabal Umm Tinassib and its surrounding region, situated in the northern section of the Egyptian Eastern Desert. AGS serves as a valuable tool for mapping surface geology and conducting mineral exploration. It assesses the concentrations of radioactive elements such as potassium (K), equivalent uranium (eU), and equivalent thorium (eTh). The concentration of radioelements exhibits measurable and significant variation according to lithology. On the other hand, several processing steps are employed for the RS data to generate high-quality images for geological mapping and to identify the mineralized alteration zones. The analysis of RS and AGS data in this study led to insightful conclusions. The utilization of False Color Composite (FCC) with the three best bands derived from the Optimum Index Factor (OIF), Principal Component Analysis (PCA) to extract two highly informative datasets, and the application of two band ratios contributed to accurate geological mapping. These band ratios notably identified identical alteration locations on both younger and older granite basement rocks. Additionally, the constrained energy minimization (CEM) technique effectively pinpointed alterations across these strata. The statistical analysis of AGS data revealed that radioactivity levels in the region range from 1.3 to 19.3 Ur for the total-count (TC), 0.2–3.6% for K, 0.09–11.6 ppm for eU, and 1.1–30.0 ppm for eTh. The estimated coefficient of variability (CoV) demonstrated that the three radio-elements exhibited normal distribution patterns across different rock units, with CoV values of less than 100%, except for K in the Malha Formation. High radiometric readings are observed in the outcroppings of younger and older granites. However, the lowest readings are recorded over undifferentiated Upper-Cretaceous sediments, Abu Rimth Formation, Galala Formation, and some parts of Quaternary sediments. The derived ternary radio-elements map highlights significant radiometric and related uranium anomalous zones as bright white regions. A strong correlation was found between high radiometric anomalous zones and the presumed occurrence of alteration zones in the study area.

Inversion of coastal cultivated soil salt content based on multi-source spectra and environmental variables

Soil salinization seriously hinders the development of efficient ecological agriculture in coastal areas. The use of Landsat, Sentinel series and hyperspectral data is an ideal way for assessing soil salinity indicators. However, environmental data (e.g. climate, terrain and parent material) are important factors for estimating such indicators. It is necessary to find the advantages and limitations of a combination of satellite images, hyperspectral data and environmental variables (ENVI) for assessing soil salinity accurately. Various data or their combinations ([I] remote sensing [RS], i.e. bands and salinity indices of Landsat 9 and Sentinel 2; [II] ENVI, including soil attributes, climate and topography; and [III] RS + ENVI) were used to construct the salinity inversion model using random forest (RF) and extremely randomized trees (ERT) for cultivated areas in the coastal plain of Dongtai City, China. The hyperspectral data were also resampled to match the range of the image bands. RF performed better than ERT for all types of analyzed data, and RS + ENVI exhibited the best performance for Sentinel 2 (R2 = 0.86). Compared with the RS data alone, Landsat 9 and Sentinel 2 provided higher salinity simulations (41% and 126%, respectively) after combination with ENVI, and salinity mapping was closer to the actual soil salinity measurements. The variables of slope, salinity index (SIT), difference index and SIT had the highest contribution in Landsat 9, Sentinel 2 and resampled hyperspectrum based on Landsat 9 and Sentinel 2, respectively. In conclusion, RS + ENVI based on Sentinel 2 data is the recommended approach for monitoring the salt content of coastal cultivated soil.

Constructing cropland ecological stability assessment method based on disturbance-resistance-response processes and classifying cropland ecological types

The cropland ecosystem stability (CES) has received increasing attention, especially in ecologically fragile areas, because of its impact on cropland quality, agricultural production and its ability to resist external disturbances. In this study, we first introduced the concepts of resilience and resistance, proposed the ecosystem disturbance-resistance-response process, and established a framework for evaluating the spatial and temporal dynamics of the CES based on RS data, and innovatively combined the RS assessment results of CES with soil field samples data to further classify cropland ecological types (CET) in a key agricultural areas of the Qinghai-Tibetan Plateau, which can effectively identify those croplands in need of priority ecological protection. Results indicate that the combined interactions of disturbance, resistance and response systems affect CES, forming a complex process with significant fluctuations and spatial variations. We also conclude that the disturbance system is positively influenced by topography and precipitation, while slope negatively affects resistance system. Hydrothermal conditions positively influence resistance system, while the response system is influenced by environmental factors at a lower intensity in six periods. It was interesting to note that soil α-biodiversity indicators are significantly and positively correlated with CES at the end of the study period. Therefore, based on the CES assessment results, we further combined the soil α-biodiversity indicators to classify the type of spatial pattern of CET and found that the eastern and northern areas have better quality, which implied an increase in the CES and a higher level of soil biodiversity, which was ideal for cropland expansion. On the contrary, we concluded that the ecosystem maintenance of the Huangshui headwaters and the northern mountainous areas needs to be strengthened in order to reverse the ecological fragility here and safeguard the cropland productive capacity.

Revisiting alpha resting state dynamics underlying hallucinatory vulnerability: Insights from Hidden semi‐Markov Modeling

BackgroundResting state (RS) brain activity is inherently non-stationary. Hidden semi-Markov Models (HsMM) can characterize continuous RS data as a sequence of recurring and distinct brain states along with their spatio-temporal dynamics. New methodRecent explorations suggest that HsMM state dynamics in the alpha frequency band link to auditory hallucination proneness (HP) in non-clinical individuals. The present study aimed to replicate these findings to elucidate robust neural correlates of hallucinatory vulnerability. Specifically, we aimed to investigate the reproducibility of HsMM states across different data sets and within-data set variants as well as the replicability of the association between alpha brain state dynamics and HP. ResultsWe found that most brain states are reproducible in different data sets, confirming that the HsMM characterized robust and generalizable EEG RS dynamics on a sub-second timescale. Brain state topographies and temporal dynamics of different within-data set variants showed substantial similarities and were robust against reduced data length and number of electrodes. However, the association with HP was not directly reproducible across data sets. Comparison with existing methodsThe HsMM optimally leverages the high temporal resolution of EEG data and overcomes time-domain restrictions of other state allocation methods. ConclusionThe results indicate that the sensitivity of brain state dynamics to capture individual variability in HP may depend on the data recording characteristics and individual variability in RS cognition, such as mind wandering. Future studies should consider that the order in which eyes-open and eyes-closed RS data are acquired directly influences an individual’s attentional state and generation of spontaneous thoughts, and thereby might mediate the link to hallucinatory vulnerability.

SolarET: A generalizable machine learning approach to estimate reference evapotranspiration from solar radiation

Irrigation is the most significant consumer of freshwater worldwide. Deciding on the right amount of irrigation is crucial for sustainable water management and food production. The Penman-Monteith (P-M) reference crop evapotranspiration (ETO) is the gold standard in irrigation management and scheduling; however, its calculation requires measurements from multiple sensors over an extended reference grass surface. The cost of land, sensors, maintenance, and water to keep the grass surface green impedes having a dense network of ETO stations. To solve this challenge, this research aims to develop an input-limited ETO estimation approach based on historical weather data and machine learning (ML) algorithms to relax the need for a reference grass surface. This approach, called "SolarET," takes solar radiation (RS) data as its sole input. RS is the only meteorological driving factor of ETO that does not rely on the measuring surface. To test the generalizability of SolarET, we test its performance over unseen arbitrary locations across California. California is chosen as the case study since it is one of the world's most hydrologically altered and agriculturally productive regions. In total, 19,088,736 hourly data samples from 131 automated weather stations have been used in this study. The ML models have been trained over 114 stations and tested over 17 unseen stations, each representing a California climatic zone. Our findings point to the superiority of decision tree-based algorithms versus neural networks. SolarET works best in irrigation-oriented regions of California (e.g., Central Valley) and is less accurate in coastal and desert zones. Our results demonstrate the higher accuracy of SolarET using hourly (RMSE = 0.93 mm/day) and daily (RMSE = 0.97 mm/day) RS data in comparison to well-known empirical alternatives like Priestley-Taylor (PT) (RMSE = 1.35 mm/day) and Hargreaves-Samani (HS) (RMSE = 2.69 mm/day).

A comprehensive approach to evaluate coastal dune evolution in Haiyang, China

Coastal dunes record the evolutionary process of coastal dynamic geomorphological systems, and provide valuable information on climate change, sea level changes, environmental evolution. These dunes are essential for global change research. In view of the dynamic changes in coastal dunes caused by extreme weather, coastal erosion and human activities, we propose a comprehensive evaluation method for coastal dune evolution, which monitors Haiyang Ten Thousand Meters Beach of China from three perspectives: point, line, and surface. This method integrates LiDAR, GNSS, and RS technologies. The research results indicate that between 2016 and 2018, 68% of the GNSS point measurements in 22 survey profiles of coastal dunes were eroded along the foreshore. During 2011-2020, the coastline based on the RS data basically remained stable; only the estuarine district underwent significant changes, and the overall average change rate was 1.32 m/a. The overall morphological evolution of coastal dunes obtained by LiDAR in 2018 was relatively stable, but there was a weak erosion trend in the foreshores of the coastal dunes. This study is beneficial for providing precise targets for remediating and restoring eroded coastal areas in the future. Furthermore, it can provide technical support and informational information for ecological environment protection, coastal erosion prevention and environmental evolution in the Haiyang Ten Thousand Meters Beach Marine Nature Reserve.

Analysis of the Impact of Sustainable Growth Rate and ESG Risk Score on the Valuation of Listed Companies on Indonesia Stock Exchange

The purpose of this study is to ascertain how the ESG Risk Score (ESG RS) and Sustainable Growth Rate (SGR) affect the value of companies that are listed on the Indonesia Stock Exchange. Through statistical analysis of secondary data of ESG RS and SGR processed from audited financial reports available on the Indonesia Stock Exchange, the study uses a descriptive and verificative methodology. 825 listed companies that are actively trading on the IDX between 2019 and 2022 make up the research's population. As for the sample size, it consists of 32 businesses that were chosen using the purposive sampling technique. This study uses SPSS 26 for data analysis in order to compute the outcomes of the autocorrelation, heteroskedasticity, fixed panel model, and random effect model tests. According to the research investigation, the impact of SGR and ESG RS on firm valuation is not statistically significant. The study's conclusion is that, over the 2019–2022 timeframe, companies listed on the Indonesia Stock Exchange continuously scored higher on the ESG Risk Score when SGR and ESG RS were present, despite a small but beneficial influence. The results show that a company's valuation is positively impacted by strong financial performance, as measured by SGR. However, even if the Sustainability Report discloses that ESG RC is a quality indicator of ESG Risk Management and is used to track the advancement of SDG and ESG programs in the Indonesian capital market, it has not yet significantly impacted the evaluation of corporate valuation. These findings provide insightful information that will help the Financial Services Authority, the Indonesia Stock Exchange, and businesses better understand and assess how these aspects can contribute to and support the development of SDG and ESG in Indonesia.

Concerning the main directions of biological diversity ex situ and in situ conservation providing by the Botanical Garden of Samara University

The article presents information on the results achieved by the Botanical Garden of Samara University in the field of biological diversity conservation in ex situ and in situ directions. The composition of 4 parts of living plants collection in open ground and greenhouse, together estimated over 4.5 thousand of seed plants taxa, is briefly characterized. The information on the seed bank development is given, about the implementation of experimental rare plants reintroduction into natural communities. The results gained by integration of ground-based monitoring and RS data, including operational surveying of target plant communities using UAVs, is briefly described also.

Analysis of soil salinity monitoring in irrigated areas (in case of Shimbay district, Republic of Karakalpakstan)

Soil salinity increasing is one of the most dangerous factors for food security and ecological conditions in the world and also in Uzbekistan. Currently, salinity affects around 47% of Uzbekistan's irrigated lands. The study area is located in the western part of Uzbekistan and downstream of the Amu Darya River. The satellite images were acquired between the 2015- 2024 years over the 5-year time period. In order to analyse soil salinity was used Normalized Difference Salinity Index (NDSI) by the ArcGIS Pro software and RS data. Especially, slightly saline soils are the biggest occupied land and equal to 30% of the total area of the district, which of 127,735.4 ha. Also, saline soils are the second biggest land class which occupies 28.1% of the total area of the district, of which 119,612.9 ha. Meanwhile, non-saline soils occupied 20.55% of the total area, as well as both strongly saline and extremely saline soils occupied area is 21.34% of the total district of the study area, which is 87456.6 ha with 90827.43 ha.

An intelligent water drop algorithm with deep learning driven vehicle detection and classification

<abstract> <p>Vehicle detection in Remote Sensing Images (RSI) is a specific application of object recognition like satellite or aerial imagery. This application is highly beneficial in different fields like defense, traffic monitoring, and urban planning. However, complex particulars about the vehicles and the surrounding background, delivered by the RSIs, need sophisticated investigation techniques depending on large data models. This is crucial though the amount of reliable and labelled training datasets is still a constraint. The challenges involved in vehicle detection from the RSIs include variations in vehicle orientations, appearances, and sizes due to dissimilar imaging conditions, weather, and terrain. Both specific architecture and hyperparameters of the Deep Learning (DL) algorithm must be tailored to the features of RS data and the nature of vehicle detection tasks. Therefore, the current study proposes the Intelligent Water Drop Algorithm with Deep Learning-Driven Vehicle Detection and Classification (IWDADL-VDC) methodology to be applied upon the Remote Sensing Images. The IWDADL-VDC technique exploits a hyperparameter-tuned DL model for both recognition and classification of the vehicles. In order to accomplish this, the IWDADL-VDC technique follows two major stages, namely vehicle detection and classification. For vehicle detection process, the IWDADL-VDC method uses the improved YOLO-v7 model. After the vehicles are detected, the next stage of classification is performed with the help of Deep Long Short-Term Memory (DLSTM) approach. In order to enhance the classification outcomes of the DLSTM model, the IWDA-based hyperparameter tuning process has been employed in this study. The experimental validation of the model was conducted using a benchmark dataset and the results attained by the IWDADL-VDC technique were promising over other recent approaches.</p> </abstract>

Improving Scarce RS Data Classification With Independent Noise and Feature Mutual Exclusion

Improving Scarce RS Data Classification With Independent Noise and Feature Mutual Exclusion

AGRO-ECOLOGICAL CLASSIFICATION OF AGRICULTURAL LAND USING MACHINE LEARNING, GIS AND REMOTE SENSING DATA

The paper is devoted to agro-ecological classification of agricultural land using modern methods of geo-information data analysis and machine learning.
 Background. There are few works in the literature, which cover the issues of accuracy of machine learning (MLL) models for agro-ecological grouping of agricultural land. A large number of raster information layers are used to improve the accuracy of land classification from satellite images. This considerably increases the time of training and testing MMOs, producing thematic maps of agricultural land classification. This approach requires considerably high computing resources and a considerable amount of computer RAM. Raster GIS data models occupy a much larger volume than vector models. In this regard, research on automated agro-ecological (grouping, classification) of agricultural land using vector GIS models is of practical importance.
 Purpose. The aim of the study is to apply GIS methods, remote sensing (ERS) data and machine learning methods for agricultural land grouping.
 Materials and methods. The materials used were synthesized multispectral high spatial resolution Sentinel-2A images, maps of vegetation indices NDVI (Normalized Difference Vegetation Index), OSAVI (Optimized Soil Adjusted Vegetation Index), EVI2 (Enhanced Vegetation Index2), NDWI (Normalized Difference Water Index), SAVI, PVI, GDVI, MCARI, NDRE, TSAVI; topographic map, ALOS DSM (30 m/pixel) and ALOS PALSAR (12.5 m/pixel) satellite images, soil map and field survey results. Field measurements were carried out using the Triump-2 satellite geodetic receiver and included determination of coordinates of characteristic points of land plot boundaries, relief elements, and soil survey.
 Digital spatial model of land use was created using GIS ArcGIS and QGIS, Python engineering libraries were used in the machine learning process.
 Results. Agro-ecological grouping of lands was realized by the example of the farm "Zerno Sibiri" of Novosibirsk region using the following machine learning methods: Random Forest (RF) method, Decision Tree (DT), k-nearest neighbours method (KNN). The best accuracy is the RF machine learning model. The accuracy of the model averaged 97.9% (with training 99.9%, testing 98.8%, and cross validation 95.0%). The Root Mean Square Error (RMSE) is 0.006: for training sample 0.001; test sample 0.076; validation sample 0.123 respectively). The mean kappa coefficient was 0.97 (1.00 for the training sample; 0.982 for the test sample, and 0.927 for the validation sample). 
 Conclusion. The offered method of agro-ecological grouping of agricultural lands by means of GIS, RS data and machine learning methods enabled to distinguish informative quantitative indicators of the relief. The main essence of the proposed method is to create a machine learning model (MLM) based on a spatial dataset. The spatial dataset is formed using geoinformation analysis methods and includes: geomorphometric maps, maps of agrometeorological parameters, soil map, on-farm land management map and operational-territorial units of land classification. The application of vector data model allowed for agro-ecological grouping of agricultural lands in automated mode, to accelerate labor-intensive process of raster data recognition, to increase objectivity of the work. The suggested method of agro-ecological agro-ecological grouping of lands allows taking into account the totality of relief and soil-ecological conditions indicators with the help of geoinformation analysis methods, remote sensing and machine learning.

Dynamic monitoring of urban renewal based on multi-source remote sensing and POI data: A case study of Shenzhen from 2012 to 2020

Accurate information on the spatiotemporal distribution of urban renewal (UR) is important for sustainable urban development. Due to its complexity, existing studies could not completely describe the land cover types after demolition, and lacked the effective conversion rules to monitor the whole process of UR demolition and reconstruction which made it impossible to obtain high-precision UR extent, demolition time, and reconstruction time. This study proposed an UR monitoring framework by combining Point of Interest, nighttime light RS data, time-series RS data from Google Earth high-resolution and Landsat imageries. The urban vacant land was introduced to supplement the land cover classification system for UR monitoring and extracted by DeepLabv3 semantic segmentation model. The new conversion rules were then generated to track the historical changes in urban land types, and the multi-temporal classification model was applied to extract spatial and temporal characteristics of UR process. Results showed a total of 3,525.55 hm2 UR region were identified in Shenzhen during 2012–2020, and the largest demolition and reconstruction areas were both observed in 2019. The F1 and F2 scores of extracted UR extent, UR demolition time, and UR reconstruction time were larger than 0.72, 0.63 and 0.66, respectively, indicating high overall accuracies. Our proposed framework is important for the UR dynamic monitoring and can provide scientific basis for future urban construction.

Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan

Abstract Remote sensing (RS) can efficiently support the quantification of crop water requirements and water productivity (WP) for evaluating the performance of agricultural production systems and provides relevant feedback for management. This research aimed to estimate winter wheat water consumption and WP in the central clay plain of Sudan by integrating remotely sensed images, climate data, and biophysical modelling. The wheat crop was cultivated under a centre-pivot irrigation system during the winter season of 2014/2015. The Landsat-8 satellite data were used to retrieve the required spectral data. The Surface Energy Balance Algorithm for Land (SEBAL) was supported with RS and climate data for estimating the Actual Evapotranspiration (ETa) and the WP for the wheat crop. The SEBAL outputs were validated using the FAO Penman–Monteith method coupled with field measurements and observation. The results showed that the seasonal ETa ranged from 400 to 600 mm. However, the WP was between 1.2 and 1.5 kg/m3 during the wheat cycle. The spatial ETa and WP maps produced by the SEBAL model and Landsat-8 images can improve water use efficiency at field scale environment and estimate the water balance over large agricultural areas.