Multi-Temporal Remote Sensing Data Research Articles

Monitoring Land Use Changes in the Yellow River Delta Using Multi-Temporal Remote Sensing Data and Machine Learning from 2000 to 2020

The Yellow River Delta (YRD), known for its vast and diverse wetland ecosystem, is the largest estuarine delta in China. However, human activities and climate change have significantly degraded the wetland ecosystem in recent decades in the YRD. Therefore, an understanding of the land use modifications is essential for the efficient management and preservation of ecosystems in this region. This study utilized time series of remote sensing data and the extreme gradient boosting method to generate land use maps of the YRD from 2000 to 2020. Several methods, including transition matrix, land use dynamic degree, and standard deviation ellipse, were employed to explore the characteristics of land use transitions. The results underscore significant spatial variations in land use over the past two decades. The most rapid increase was observed in built-up area, followed by terrestrial water and tidal flats, while unutilized land experienced the fastest decrease, followed by forest–grassland. The spatial distribution patterns of agricultural land, built-up area, terrestrial water, and forest–grassland demonstrated stronger directionality compared to other land use types. The wetlands have expanded in size and improved in structure. Unutilized land has been converted into artificial wetlands comprising ponds, reservoirs, salt ponds, shrimp and crab ponds, and natural wetlands featuring mudflats and forest–grassland. The wetland conservation efforts after 2008 have proven very effective, playing a positive role in ecological and environmental preservation, as well as in regional sustainable development.

Assessment of Spatio-Temporal changes of Forest Cover using Remote Sensing techniques in Pavagadh Region, Gujarat State

For effective forest management, it is essential to consider forest patterns and periodic changes in forest cover. Several spectral vegetation indices derived from multi-temporal Remote Sensing data are useful to track the changes over time. The major objective of this study was to monitor the changes in forest cover during the past three decades in the Pavagadh area of Panchmahal district, Gujarat State, India. Various indices like Normalized Difference Vegetation Index (NDVI), Land Surface Temperature, and Urban Thermal Field Variance Index (UTFVI) with the Ecological Evaluation Index were analyzed for assessment of Spatio-temporal changes in the forest cover. Multi-temporal Landsat-TM and OLI sensor data for the years 1991, 2001, 2011, and 2021 were utilized covering the study area. The results indicated that the total forest cover area has gradually increased from 1991 to 2021 and the total forest area has doubled during this period of 30 years. The comparative study of NDVI and Land Surface Temperature map brings out a significant fact that in the areas where moderate and dense forest cover is present, the Land Surface Temperature was lower as compared to areas with poor vegetation cover. This indicates that there is an inverse relationship between forest cover distribution and Land Surface Temperature. However, the Ecological evaluation index shows that the forest vegetation quality is gradually improving to normal conditions with the excellent category and UTFVI value (< 0) in reference to the year 1991.

Preliminary Analysis of Reclamation Changes in Macao Using Multisource and Multitemporal Remote-Sensing Data

Liu, Z.; Zang, J., and Liu, J., 2023. Preliminary analysis of reclamation changes in Macao using multisource and multitemporal remote-sensing data. Journal of Coastal Research, 39(1), 90–102. Charlotte (North Carolina), ISSN 0749-0208. Macao is a typical reclamation city. A systematic analysis of reclamation changes can be used to relate reclamation to socioeconomic development. In this study, multisource and multitemporal satellite remote-sensing data were used to analyze the temporal and spatial characteristics, driving forces for reclamation, and nearshore water quality of Macao. The results showed that the Macao coastline is moving towards the sea. The total area of Macao increased from 29.19 km2 in 1999 to 35.83 km2 in 2019. The largest proportion of reclamation land-use type was public facilities land, whereas the smallest proportion was green land and square land. The land distribution gradually became more compact up to 2007 and dispersed thereafter. The factors of population, gross domestic product, number of visitors to Macao, and total export trade had different influences on reclamation in Macao during different time periods, where the population factor had the largest overall impact. The concentrations of chlorophyll and suspended sediment in Macao coastal waters were high nearshore and low offshore.

Dynamics of Low-Lying Sandy Coast of the Gydan Peninsula, Kara Sea, Russia, Based on Multi-Temporal Remote Sensing Data

The retreat rates of Arctic coasts have increased in recent decades at many sites, and an essential part of coasts considered accumulative before have turned erosional due to global climate changes and construction in the coastal zone. In this paper, we study a 7 km long coastal section of the western Gydan Peninsula in a new construction area. Based on the interpretation of multi-temporal satellite imagery, we assessed coastal dynamics in distinct periods from 1972 to 2020. We analyzed the geological structure of the coast as well as changes in hydrometeorological parameters with time, and considering the human impact, we proposed the main drivers of spatial and temporal variations of coastal dynamics. The studied low-lying sandy accumulative marine terrace was more or less stable in the period before construction (1972–2014). However, with the area’s development, the coast dynamics changed drastically: in 2014–2017, three-quarters of the studied area experienced retreat, and the average retreat rate amounted to 5.8 m/yr, up to 28.5 m/yr near the construction sites. We relate this coastal erosion intensification to human impact combined with the growth of hydrometeorological forcing. Although coastal erosion slowed down after 2017, the retreat trend remained. In the coming years, with Arctic climate warming, erosion of the studied coast will continue.

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data

The detection of degraded soil distribution areas is an urgent task. It is difficult and very time consuming to solve this problem using ground methods. The modeling of degradation processes based on digital elevation models makes it possible to construct maps of potential degradation, which may differ from the actual spatial distribution of degradation. The use of remote sensing data (RSD) for soil degradation detection is very widespread. Most often, vegetation indices (indicative botany) have been used for this purpose. In this paper, we propose a method for constructing soil maps based on a multi-temporal analysis of the bare soil surface (BSS). It is an alternative method to the use of vegetation indices. The detection of the bare soil surface was carried out using the spectral neighborhood of the soil line (SNSL) technology. For the automatic recognition of BSS on each RSD image, computer vision based on deep machine learning (neural networks) was used. A dataset of 244 BSS distribution masks on 244 Landsat 4, 5, 7, and 8 scenes over 37 years was developed. Half of the dataset was used as a training sample (Landsat path/row 173/028). The other half was used as a test sample (Landsat path/row 174/027). Binary masks were sufficient for recognition. For each RSD pixel, value “1” was set when determining the BSS. In the absence of BSS, value “0” was set. The accuracy of the machine prediction of the presence of BSS was 75%. The detection of degradation was based on the average long-term spectral characteristics of the RED and NIR bands. The coefficient Cmean, which is the distance of the point with the average long-term values of RED and NIR from the origin of the spectral plane RED/NIR, was calculated as an integral characteristic of the mean long-term values. Higher long-term average values of spectral brightness served as indicators of the spread of soil degradation. To test the method of constructing soil degradation maps based on deep machine learning, an acceptance sample of 133 Landsat scenes of path/row 173/026 was used. On the territory of the acceptance sample, ground verifications of the maps of the coefficient Cmean were carried out. Ground verification showed that the values of this coefficient make it possible to estimate the content of organic matter in the plow horizon (R2 = 0.841) and the thickness of the humus horizon (R2 = 0.8599). In total, 80 soil pits were analyzed on an area of 649 ha on eight agricultural fields. Type I error (false positive) of degradation detection was 17.5%, and type II error (false negative) was 2.5%. During the determination of the presence of degradation by ground methods, 90% of the ground data coincided with the detection of degradation from RSD. Thus, the quality of machine learning for BSS recognition is sufficient for the construction of soil degradation maps. The SNSL technology allows us to create maps of soil degradation based on the long-term average spectral characteristics of the BSS.

Predictive Soil Mapping in the Boreal Plains of Northern Alberta Using Multi-Temporal Remote Sensing Data and Terrain Derivatives

Predictive Soil Mapping in the Boreal Plains of Northern Alberta Using Multi-Temporal Remote Sensing Data and Terrain Derivatives

Integrated use of multi-temporal multi-sensor and multiscale Remote Sensing data for the understanding of archaeological contexts: the case study of Metaponto, Basilicata.

Abstract This paper is focused on the archaeological area of Metaponto (Mexomov-nov) and its territory, located in southern Italy. The area played an important role for the agricultural economy and the traffic of goods and people, from the south of Italy towards the central regions, starting from the Neolithic period, and reaching the zenith with the Greek polis of Metaponto and its hinterland. The site is herein analyzed through an integrated use of several Earth observation and remote sensing technologies and ancillary data produced over the years by archaeologists and scholars. The aim was to identify new buried elements of archaeological interest, for the reconstruction of the historical-archaeological landscape. Through the combined use of optical and radar satellite images, high-resolution images obtained by Unmanned Aerial System (visible, multispectral, and thermal infrared), geophysical data, and archival data, it was possible to deepen the knowledge of the area, in particular the “Castrum” area, identifying new buried evidence (structures, roads, and elements of the ancient landscape).

Editorial Foreword to the Special Issue on Recent Advances in Multitemporal Remote-Sensing Data Processing

This special issue of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) contains 11 papers both from the extended outcomes of the Multitemp 2019 presented papers, and from the submissions by following a general Call-for-Papers of this special issue. These papers focus on the interesting and relevant topics in the multitemporal data analysis.

Indices Extraction from Multitemporal Remote Sensing Data for Mapping Urban Built-Up

Remote sensing data analysis in the cloudy area is still a challenging process. Fortunately, remote sensing technology is fast growing. As a result, multitemporal data could be used to overcome the problem of the cloudy area. Using multitemporal data is a common approach to address the cloud problem. However, most methods only use two data, one as the main data and the other as complementary of the cloudy area. In this paper, a method to harness multitemporal remote sensing data for automatically extracting some indices is proposed. In this method, the process of extracting the indices is done without having to mask the cloud. Those indices could be further used for many applications such as the classification of urban built-up. Landsat-8 data that is acquired during 2019 are stacked, therefore each pixel at the same position creates a list. From each list, indices are extracted. In this study, NDVI, NDBI, and NDWI are used to mapping built-up areas. Furthermore, extracted indices are divided into four categories by their value (maximum, quantile 75, median, and mean). Those indices are then combined into a simple formula to mapping built-up to see which produces better accuracy. The Pleiades as high-resolution remote sensing data is used to assist supervised classification for assessment. In this study, the combination of mean NDBI, maximum NDVI, and mean NDWI result highest Kappa coefficient of 0.771.

Effect of tide level on submerged mangrove recognition index using multi-temporal remotely-sensed data

Mangrove forests are intertidal wetland with a diverse assemblage of trees, shrubs and palms growing along tropical and subtropical coastlines. Effective mapping of mangrove forests has not yet been achieved due to the periodicity of tidal dynamics. Our previous studies showed that a submerged mangrove recognition index (SMRI), which was proposed based on the differential spectral signature of mangrove forests from high and low tides, has potential advantages in mangrove discrimination and classification. However, the effect of tide level on the performance of SMRI is still unclear. In this study, GaoFen-1 images with various tide heights were acquired, and SMRI images from low tide to high tide were obtained. Then, the resulting SMRI images were compared in detail, and the relationship between tide level and SMRI values was analyzed. This experiment was accomplished via a case study in Yunlin, Guangxi Province in China. The results showed that an increased difference in tide level led to an increase in the number of pixels of high SMRI values, indicating that more undetected submerged mangrove forests could be distinguished using SMRI. Furthermore, an exponential relationship was observed between SMRI and tide level. It suggests that SMRI effectively helps to distinguish submerged mangrove forests from multi-tide remotely-sensed imagery, and also benefits accurate mapping of mangrove forests.

Modelling the Behavior of DVI and IPVI Vegetation Indices Using Multi-Temporal Remotely Sensed Data

Remote sensing techniques are widely used to detect and analyze land cover changes due to their accuracy and cost-effectiveness. Among the various spectral indices derived from the satellite data Difference Vegetation Index (DVI) and Infrared Percentage Vegetation Index (IPVI) vegetation indices applied to model the behavior of the indices in the study of suburb ecosystem vegetation cover over twenty years. To achieve the aim of the study two objectives were formulated; detect Spatial-temporal variations in urban vegetation and how suitable the selected algorithms to study urban ecosystem vegetation. The study area is a rapidly developing area consists of several suburbs including Battaramulla, Malabe, and Kaduwela, Sri Lanka. The study used Landsat data and pre-processing, processing, geometric and atmospheric corrections were performed using ERDAS imagine mapping software and all the mappings were carried out via Arc GIS software. The results show Infrared Percentage Vegetation Index (IPVI) algorithm as the most suitable vegetation index to study suburb ecosystem vegetation than Difference Vegetation Index (DVI) in the study area. Therefore, the study recommends IPVI than DVI to study ecosystem vegetation in sub-urban areas.

Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

Attentive Spatial Temporal Graph CNN for Land Cover Mapping From Multi Temporal Remote Sensing Data

Satellite image time series (SITS) collected by modern Earth Observation (EO) systems represent a valuable source of information that supports several tasks related to the monitoring of the Earth surface dynamics over large areas. A main challenge is then to design methods able to leverage the complementarity between the temporal dynamics and the spatial patterns that characterize these data structures. Focusing on land cover classification (or mapping) tasks, the majority of approaches dealing with SITS data only considers the temporal dimension, while the integration of the spatial context is frequently neglected. In this work, we propose an attentive spatial temporal graph convolutional neural network that exploits both spatial and temporal dimensions in SITS. Despite the fact that this neural network model is well suited to deal with spatio-temporal information, this is the first work that considers it for the analysis of SITS data. Experiments are conducted on two study areas characterized by different land cover landscapes and real-world operational constraints (i.e., limited labeled data due to acquisition costs). The results show that our model consistently outperforms all the competing methods obtaining a performance gain, in terms of F-Measure, of at least 5 points with respect to the best competing approaches on both benchmarks.

Determination of Shoreline Changes along Upstream of Se San River, Kontum Province based Multi-temporal Remote Sensing Data Analysis

Se San river upstream includes Poko tributary (on the right bank) and Dak Bla tributary (on the left bank), mostly located in Kon Tum province. The process of river sediment decline has dramatic shoreline changes in this area, which becomes a driving-force to modify the current socio-economic development as well as the impact on territorial planning in the future. This study aims to analyze the shoreline changes by extracting multi-temporal satellite imagery of Landsat in the period of 1990-2013 and identify its change effects on land-use. The results show that the strongest erosion rate was -2.96 m/year in Dak Bla tributary (in Kon Tum town). And in the Poko tributary, the average value of erosion rate is -1.31 m/year and the average accretion rate is 1.17 m/year. In the context of dramatic land-use change, this approach allows to support for territorial management and illustrates the accretion-erosion relationship in river basin evolution.

Combining SAR and Optical Earth Observation with Hydraulic Simulation for Flood Mapping and Impact Assessment

Timely mapping, measuring and impact assessment of flood events are crucial for the coordination of flood relief efforts and the elaboration of flood management and risk mitigation plans. However, this task is often challenging and time consuming with traditional land-based techniques. In this study, Sentinel-1 radar and Landsat images were utilized in collaboration with hydraulic modelling to obtain flood characteristics and land use/cover (LULC), and to assess flood impact in agricultural areas. Furthermore, indirect estimation of the recurrence interval of a flood event in a poorly gauged catchment was attempted by combining remote sensing (RS) and hydraulic modelling. To this end, a major flood event that occurred in Sperchios river catchment, in Central Greece, which is characterized by extensive farming activity was used as a case study. The synergistic usage of multitemporal RS products and hydraulic modelling has allowed the estimation of flood characteristics, such as extent, inundation depth, peak discharge, recurrence interval and inundation duration, providing valuable information for flood impact estimation and the future examination of flood hazard in poorly gauged basins. The capabilities of the ESA Sentinel-1 mission, which provides improved spatial and temporal analysis, allowing thus the mapping of the extent and temporal dynamics of flood events more accurately and independently from the weather conditions, were also highlighted. Both radar and optical data processing methods, i.e., thresholding, image differencing and water index calculation, provided similar and satisfactory results. Conclusively, multitemporal RS data and hydraulic modelling, with the selected techniques, can provide timely and useful flood observations during and right after flood disasters, applicable in a large part of the world where instrumental hydrological data are scarce and when an apace survey of the condition and information about temporal dynamics in the influenced region is crucial. However, future missions that will reduce further revisiting times will be valuable in this endeavor.

Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets and Machine Learning

Key Points Groundwater withdrawals are not actively monitored in most places of the world at a scale necessary to implement sustainable solutions Various multitemporal remote sensing data are integrated into a machine learning framework to effectively predict groundwater withdrawals The results over the High Plains Aquifer, Kansas, USA, show that this approach is applicable to similar regions having sparse in situ data

Application of Landsat Time-Series Data in Island Ecological Environment Monitoring: A Case Study of Zhoushan Islands, China

Chen, C.; He, X.; Lu, Y., and Chu, Y., 2020. Application of Landsat time-series data in island ecological environment monitoring: A case study of Zhoushan Islands, China. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 193–199. Coconut Creek (Florida), ISSN 0749-0208.An ecological environment is a complex system fundamental to the sustainable development of societies and economies. Landsat series satellites are widely used for monitoring the changes in Earth's surface morphology. With long time-series data, Landsat can provide new methods and technical support for eco-environmental monitoring. This study developed remote-sensing monitoring methods based on Landsat data to observe the changes in the ecological environment in the Zhoushan region, China. The regional environmental changes in vegetation, water, and other key ecological factors were analyzed using multitemporal remote-sensing data. This study shows the effectiveness of this method for the extraction of key eco-environmental factors such as vegetation and water body area and the evaluation of regional eco-environmental quality. This study also reveals ecological patterns in the Zhoushan region and provides data support for regional eco-environmental assessment and management.

Evaluating the Relationships of Inter-Annual Farmland Vegetation Dynamics with Biodiversity Using Multi-Spatial and Multi-Temporal Remote Sensing Data

Agricultural landscapes are highly dynamic ecosystems, but the effects of temporal farmland vegetation dynamics on species diversity have not been widely studied. In 93 sample farm landscapes in eastern Ontario, Canada, biodiversity data for seven taxa were collected in 2011 and 2012, prior to the initiation of this study. The goal of this study was to determine if trends and variability in vegetation productivity detected in these sample landscapes using long-term archived moderate and coarse resolution remote sensing time series data are related to the measured biodiversity. Mid-summer Moderate Resolution Imaging Spectroradiometer (MODIS) (2000–2011) and Landsat 5 (1985–2011) Normalized Difference Vegetation Index (NDVI) data were used with the Thiel–Sen slope and Contextual Mann–Kendall trend analysis to identify pixels showing significant trends. NDVI temporal metrics included 1) the percentage of pixels in each landscape with a significant negative or positive trend, and 2) the temporal coefficient of variation (CV) of both the mean and spatial CV of landscape NDVI. Larger areas of significant positive NDVI trends were found in the sample landscapes than negative trends, the former being associated with agricultural intensification or crop changes and the latter with smaller areas of natural vegetation removal. Landsat better-detected changes in individual fields or small areas of natural vegetation due to its much smaller pixel size. In addition, the longer Landsat time series showed a change in the NDVI trend from positive (1985–2000) to negative or a leveling off (2000–2011) for many pixels. In biodiversity modeling, the Landsat temporal CV of NDVI was negatively correlated with 2011–2012 plant and beetle diversity, while plant biodiversity was positively correlated with the percentage of pixels in a sample landscape showing a significantly positive NDVI trend. No significant relationships were found using the MODIS data. This study shows that temporal trends and variability in farmland vegetation density derived from Landsat data are related to biodiversity for certain taxa and that such relationships should be considered along with the more commonly studied spatial landscape attributes in evaluating landscape-level impacts of farming on biodiversity.

Mapping Natural Forest Remnants with Multi-Source and Multi-Temporal Remote Sensing Data for More Informed Management of Global Biodiversity Hotspots

Global terrestrial biodiversity hotspots (GBH) represent areas featuring exceptional concentrations of endemism and habitat loss in the world. Unfortunately, geospatial data of natural habitats of the GBHs are often outdated, imprecise, and coarse, and need updating for improved management and protection actions. Recent developments in satellite image availability, combined with enhanced machine learning algorithms and computing capacity, enable cost-efficient updating of geospatial information of these already severely fragmented habitats. This study aimed to develop a more accurate method for mapping closed canopy evergreen natural forest (CCEF) of the Eastern Arc Mountains (EAM) ecoregion in Tanzania and Kenya, and to update the knowledge on its spatial extent, level of fragmentation, and conservation status. We tested 1023 model possibilities stemming from a combination of Sentinel-1 (S1) and Sentinel-2 (S2) satellite imagery, spatial texture of S1 and S2, seasonality derived from Landsat-8 time series, and topographic information, using random forest modelling approach. We compared the best CCEF model with existing spatial forest products from the EAM through independent accuracy assessment. Finally, the CCEF model was used to estimate the fragmentation and conservation coverage of the EAM. The CCEF model has moderate accuracy measured in True Skill Statistic (0.57), and it clearly outperforms other similar products from the region. Based on this model, there are about 296,000 ha of Eastern Arc Forests (EAF) left. Furthermore, acknowledging small forest fragments (1–10 ha) implies that the EAFs are more fragmented than previously considered. Currently, the official protection of EAFs is disproportionally targeting well-studied mountain blocks, while less known areas and small fragments are underrepresented in the protected area network. Thus, the generated CCEF model should be used to design updates and more informed and detailed conservation allocation plans to balance this situation. The results highlight that spatial texture of S2, seasonality, and topography are the most important variables describing the EAFs, while spatial texture of S1 increases the model performance slightly. All in all, our work demonstrates that recent developments in Earth observation allows significant enhancements in mapping, which should be utilized in areas with outstanding biodiversity values for better forest and conservation planning.