Time Series Of Landsat Imagery Research Articles

A novel approach towards continuous monitoring of forest change dynamics in fragmented landscapes using time series Landsat imagery

Carbon emissions from forest ecosystems are greatly impacted by the acceleration of fragmentation and edge effects. Understanding these effects requires accurate monitoring of changes in fragmented forest landscapes. However, these changes are often low-intensity and small-scale, making it difficult to detect them using medium spatial resolution satellite images (e.g., Landsat). To address this challenge, this study developed the Pure Forest Index (PFI), which uses a combination of the existing vegetation index (VI) and spectral mixture analysis (SMA) to more effectively detect and characterize the contribution of forests to the observed spectral response of a pixel. The PFI was applied to detect forest changes in the Amazon rainforest from 1986 to 2020 using the Continuous Change Detection and Classification (CCDC) algorithm (hereafter referred to as the CCDC-PFI algorithm). The results showed reliable performance in mapping forest changes, with an overall accuracy of 0.94 (±0.03) at the spatial scale and a temporal accuracy of 91.1 % (within a two-year window). Comparison with other indices revealed that the PFI improves the ability to monitor forest dynamics with an increased overall accuracy of 0.02–0.35. The PFI also demonstrated advantages in enhancing sub-pixel forest information and suppressing non-forest backgrounds in various scenes compared to conventional VIs. The proposed approach is expected to benefit further research on forests and ecosystems.

Mapping LULC Dynamics and Its Potential Implication on Forest Cover in Malam Jabba Region with Landsat Time Series Imagery and Random Forest Classification

Pakistan has an annual deforestation rate of 4.6% which is the second highest in Asia. It has been described by the Food and Agriculture Organization (FAO) that the deforestation rate increased from 1.8–2.2% within two decades (1980–2000 and 2000–2010). KPK (Khyber Pakhtunkhwa), Pakistan’s northwestern province, holds 31% of the country’s total forest resources, the majority of which are natural forests. The Malam Jabba region, known for its agro-forestry practices, has undergone significant changes in its agricultural, forestry, and urban development. Agricultural and built-up land increased by 77.6% in the last four decades, and significant changes in land cover especially loss in forest, woodland, and agricultural land were observed due to flood disasters since 1980. For assessing and interpreting land-cover dynamics, particularly for changes in natural resources such as evergreen forest cover, remote sensing images are valuable assets. This study proposes a framework to assess the changes in vegetation cover in the Malam Jabba region during the past four decades with Landsat time series data. The random forest classifier (RF) was used to analyze the forest, woodland, and other land cover changes over the past four decades. Landsat MMS, TM, ETM+, and OLI satellite images were used as inputs for the random forest (RF) classifier. The vegetation cover change for each period was calculated from the pixels using vegetation indices such as NDVI, SAVI, and VCI. The results show that Malam Jabba’s total forest land area in 1980 was about 236 km2 and shrank to 152 km2 by 2020. The overall loss rate of evergreen forests was 35.3 percent. The mean forest cover loss rate occurred at 2.1 km2/year from 1980 to 2020. The area of woodland forest decreased by 87 km2 (25.43 percent) between 1980 and 2020. Other landcover increased by 121% and covered a total area of 178 km2. The overall accuracy was about 94% and the value of the kappa coefficient was 0.92 for the change in forest and woodland cover. In conclusion, this study can be beneficial to researchers and decision makers who are enthusiastic about using remote sensing for monitoring and planning the development of LULC at the regional and global scales.

Estimating fractional cover of saltmarsh vegetation species in coastal wetlands in the Yellow River Delta, China using ensemble learning model

Saltmarshes in coastal wetlands provide important ecosystem services. Satellite remote sensing has been widely used for mapping and classification of saltmarsh vegetation, however, medium-spatial-resolution satellite datasets such as Landsat-series imagery may induce mixed pixel problems over saltmarsh landscapes which are spatially heterogeneous. Sub-pixel fractional cover estimation of saltmarsh vegetation at species level are required to better understand the distribution and canopy structure of saltmarsh vegetation. In this study, we presented an approach framework for estimating and mapping the fractional cover of major saltmarsh species in the Yellow River Delta, China based on time series Landsat 8 Operational Land Imager data. To solve the problem that the coastal area is frequently covered by clouds, we adopted the recently developed virtual image-based cloud removal (VICR) algorithm to reconstruct missing image values under the cloud/cloud shadows over the time series Landsat imagery. Then, we developed an ensemble learning model (ELM), which incorporates Random Forest Regression (RFR), K-Nearest Neighbor Regression (KNNR) and Gradient Boosted Regression Tree (GBRT) based on temporal-spectral features derived from the time-series cloudless images to estimate the fractional cover of major vegetation types, i.e., Phragmites australis, Suaeda salsa and the invasive species, Spartina alterniflora. High spatial resolution imagery acquired by the Unmanned Aerial Vehicle and Gaofen-6 satellites were used for reference sample collections. The results showed that our approach successfully estimated the fractional cover of each saltmarsh species (average of R-square:0.891, RMSE: 7.48%). Through four scenarios of experiments, we found that the ELM is advantageous over each individual model. When the images during key months were absent, cloud removal for the Landsat images considerably improved the estimation accuracies. In the study area, Spartina alterniflora covers the largest area (5753.97 ha), followed by Phragmites australis with spatial extent area of 4208.4 ha and Suaeda salsa of 1984.41 ha. The average fractional cover of S. alterniflora was 58.45%, that of P. australis was 51.64% and that of S.salsa was 51.64%.

The Relative Timing of Population Growth and Land Use Change—A Case Study of North Taiwan from 1990 to 2015

Urban expansion is a form of land cover and land use change (LCLUC) that occurs globally, and population growth can be a driver of and be driven by LCLUC. Determining the cause–effect relationship is challenging because the temporal resolution of population data is limited by decadal censuses for most countries. The purpose of this study is to explore the relationship and relative timing between population change and land use change based on a case study of northern Taiwan from 1990 to 2015. A unique dataset on population was acquired from annually-updated governmental-based population registers maintained at the district level, and land-use expansion data (Residential, Employment, and Transportation Corridor categories) were derived from dense time series of Landsat imagery. Linear regression was applied to understand the general relationship between population and land use and their changes. The strongest relationships were found between population and areal extent of Residential land use, and between population change and Residential areal change. Lagged correlation analysis was implemented for identifying the time lag between population growth and land use change. Most districts exhibited Residential and Employment expansion prior to population growth, especially for districts in the periphery of metropolitan areas. Conversely, the core of metropolitan areas exhibited population growth prior to Residential and Employment expansion. Residential and Employment expansion were deemed to be drivers of population change, so population change was modeled with ordinary least square and geographically weighted regression with Residential and Employment expansion in both synchronized and time lag manners. Estimated population growth was found to be the most accurate when geographic differences and time lags from urban land use expansion were both incorporated.

Carbon uptake of the sugarcane agroecosystem is profoundly impacted by climate variations due to seasonality and topography

Sugarcane is an economically important crop whose growth and carbon uptake play vital roles in the carbon cycle, food security, and global trade development. However, the response of the carbon uptake of sugarcane to climate variations and the influencing factors are still unclear. In this study, seven sugarcane plantation distribution maps during 2000 and 2018 in Guangxi, China, were generated using time series Landsat and Sentinel-2 imagery, and these maps were used to produce stable sugarcane sites by identifying pixels without land cover transformation. Time series gross primary production (GPP) and climate datasets were used to analyze the response of annual GPP to temperature, precipitation, and their interacting effects under different landforms (rugged ground and flat ground) and growing periods. The results showed a significantly positive correlation between annual GPP change and temperature variations. Extreme precipitation, including extreme drought and extreme wetness, caused the annual GPP to decrease sharply, except for extreme wetness in the early growing period. The decrease in annual GPP mainly appeared when extreme drought and hot conditions or extreme wet and cold conditions coexisted, ranging from −20.37% to −0.23%, while the increase in annual GPP especially appeared when non-extreme precipitation and high temperature co-occurred, ranging from 0.02% to 8.44%. In addition, the responses of sugarcane annual GPP change to extreme temperature and precipitation were significantly different between rugged and flat ground. Overall, this study indicated that spatiotemporal heterogeneities existed in the response of sugarcane carbon uptake to climate variations. In the future, effective measures should be adopted in sugarcane management and cultivation to reduce the negative effects of climate change on crop production. • Sugarcane’s carbon uptake response discrepantly to heat and water stresses. • Impacts of climate variations on sugarcane GPP varied with growing stages. • Rugged grounds change the response of sugarcane GPP to extreme climates.

Spatiotemporal Characterization and Analysis of River Morphology Using Long-Term Landsat Imagery and Stream Power

Meandering rivers are among the most dynamic Earth-surface systems, which generally appear in fertile valleys, the most valuable lands for agriculture and human settlement. Landsat time series and morphological parameters are complementary tools for exploring river dynamics. Karun River is the most effluent and largest meandering river in Iran, which keeps the Karun’s basin economy, agriculture, and industrial sections alive; hence, investigating morphological changes in this river is essential. The morphological characteristics of Karun have undergone considerable changes over time due to several tectonic, hydrological, hydraulic, and anthropogenic factors. This study has identified and analyzed morphological changes in Karun River using a time series of Landsat imagery from 1985–2015. On that basis, morphological dynamics, including the river’s active channel width, meander’s neck length, water flow length, sinuosity index, and Cornice central angle, were quantitatively investigated. Additionally, the correlation between the stream power and morphological factors was explored using the data adopted from the hydrometric stations. The results show that the dominant pattern of the Karun River, due to the sinuosity coefficient, is meandering, and the majority of the river falls in the category of developed meander rivers. Moreover, the number of arteries reduced in an anabranch pattern, and the river has been migrating towards the downstream and eastern sides since 1985. This phenomenon disposes a change in the future that can be hazardous to the croplands and demands specific considerations for catchment management.

Developing seagrass index for long term monitoring of Zostera japonica seagrass bed: A case study in Yellow River Delta, China

Seagrass beds offer unique and vital ecological services as an important blue carbon ecosystem in coastal wetlands. Zostera japonica is an intertidal seagrass species native to eastern Asia and is one of the most widely distributed seagrass species in China. However, little is known on the long-term variations of Z. japonica extents. Automatic mapping method for Z. japonica seagrass beds is in urgent need to fill this knowledge gap. In this study, we proposed a new SeaGrass Index (SGI) for automatic and rapid mapping of Z. japonica based on time-series Landsat satellite imagery, aiming to alleviate the influence of tidal inundation and enhance the separability from other coastal cover types. The SGI considers both spectral and phenological characteristics of Z. japonica, as well as the spatial location of Z. japonica. We took the Yellow River Delta (YRD), China as our study area, where Z. japonica was first discovered and reported in 2015. Based on SGI, Z. japonica extents during 1985–2018 were extracted using multi-Otsu thresholding algorithm. Accuracy assessments based on field investigations and high-resolution imagery showed that SGI successfully separated seagrass beds from other cover types, especially intertidal salt marshes, with overall accuracies > 95%, producer’s accuracies > 90% and user’s accuracies > 94%. Our study provides the first long-term maps of seagrass beds in YRD. The area of Z. japonica showed large variations during 1985–2018, ranging from 149 ha in 2005–2006 to 1302.9 ha in 2011–2012. The spatial distribution of Z. japonica varied with the morphological change of the estuary caused by river channel shifts. Since 2011, Z. japonica seagrass beds have undergone area degradation due to the invasion of S. alterniflora. The area was only 332.3 ha in 2017–2018. Coastal erosion and extreme climate events such as drought and typhoon might also explain degradation of seagrass beds in YRD. We expect that the SGI will advance automatic and rapid mapping methods for intertidal seagrass beds, and the Z. japonica maps will provide a baseline data for restoration and management of seagrasses at regional scale.

Characterizing ecosystem change in wetlands using dense earth observation time series

Wetlands in drylands are vulnerable to degradation and disappearance due to the combined effects of increasing anthropogenic disturbances and climatic extremes. Such influences may drive non-linear shifts in surface responses that require long-term monitoring approaches for their study. Here, we used a piece-wise regression model to characterize long-term Ecosystem Change Types (ECT) in the surface water and vegetation dynamics of the Inner Niger Delta wetlands in Mali between 2000 and 2019. We also examined the added benefits of using a dense Landsat time series for such segmented trend analysis in comparison with MODIS products that are regularly used for ecosystem trends assessment. Our approach has found statistically significant (p < 0.05) long-term changes in wetland ecosystems, as calculated from Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI) image series on both the MODIS and the Landsat scales. The class-specific accuracies of the detected ECTs were evaluated through the validation of temporal trajectories based on the TimeSync logic at selected probability sample locations. Results showed higher user's, producer's, and overall accuracies (OA) when using a dense Landsat time series (OA = 0.89 ± 0.01), outperforming the MOD09A1 time series (OA = 0.37 ± 0.03). Our study provides a robust framework for long-term wetland monitoring that demonstrates the benefits of applying dense Landsat time-series imagery for accurate quantifications of linear and non-linear ecosystem responses in vast highly dynamic floodplain systems. Delivering such an improved assessment, in a spatial resolution that better resolves the characteristics of wetlands ecosystems, has the potential to support the information needs of global conservation and restoration efforts.

Satellite based lake bed elevation model of Lake Urmia using time series of Landsat imagery

This study presents a lake bed elevation model of Lake Urmia. In the course of model generation, a time series of the extent of the lake surface was derived from 129 satellite images with different acquisition dates based on the Landsat sensors Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI). Due to the rapid shrinking of the lake during the last two decades, lake surface areas ranging from 890 km2 to 6125 km2 could be covered. The water edge of the various lake extents was then linked to the observed water level on the day of the satellite image acquisition. The resulting contour lines, covering water levels between 1270.04 m and 1278.42 m a.s.l. and thus representing the lakebed morphology in its shallow parts, were merged with existing data (deeper parts) and interpolated to generate a lake bed elevation model. Finally, Lake Urmia’s Level-Area-Volume relationships were derived from the lake bed elevation model and compared to bathymetric data previously published.

Medium Spatial Resolution Mapping of Global Land Cover and Land Cover Change Across Multiple Decades From Landsat

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

Assessing degradation of lake wetlands in Bashang Plateau, China based on long-term time series Landsat images using wetland degradation index

Continuous monitoring of wetland dynamics and regional-scale assessment of wetland degradation are important to understand wetland ecosystem processes and to formulate restoration measures. The lake wetlands in Bashang Plateau play vital roles in providing essential resources and maintaining biodiversity in North China, while they were not fully investigated in previous studies. In addition, previous studies on wetland degradation assessment mostly relied on wetland area change every five or ten years. Continuous dynamics of wetlands was rarely considered. In this study, we developed a new wetland degradation index (WDI) to assess lake wetland degradation in Bashang Plateau based on time series Landsat imagery from 1985 to 2020 on Google Earth Engine platform. WDI integrates three indicators, namely lake shrinkage index (LSI), vegetation degradation index (VDI) and soil degradation index (SDI), which represents hydrodynamics of wetland inundation, vegetation, and soil conditions of wetlands, respectively. Different from previous research that utilized remotely sensed image to extract wetland every five or ten years, the LSI considers time-series continuous dynamics of lake water inundations and the transitions between water and non-water on a pixel-by-pixel basis. Our results showed that small lake wetlands with area less than 8 ha are dominant in Bashang Plateau. Both the number and the area of lake wetlands showed large interannual variations. The number (area) of lake wetlands reached 1715 (31,419 ha) in 1995–1996 and were only 493 (7,717 ha) in 2009–2010. The total area of the lake wetlands in 2019–2020 was only around 1/3 of that in 1995–1996. The LSI, VDI, SDI, as well as WDI demonstrate large spatial variations among the lake wetlands and within the lake wetlands. Wetlands in the western and central subregions experienced more severe degradation than those in the eastern subregion, and the edge of the wetlands normally showed more severe degradation. The resulted degradation grades are consistent with our field investigations. Among the 11 counties in Bashang Plateau, Zhangbei (ZB) county in the western subregion has the largest area with severe and very severe degradation grades. Degradation of lake wetlands was mainly associated with groundwater level declination which was induced by anthropogenic activities and economic development. The proposed WDI in this study can be widely applicable for regional-scale wetland degradation assessment using remote sensing techniques and thus further help decision making in lake wetland conservation and restoration.

Linking robust spatiotemporal datasets to assess and monitor habitat attributes of a threatened species.

Accessibility of multispectral, multitemporal imagery combined with recent advances in cloud computing and machine learning approaches have enhanced our ability to model habitat characteristics across broad spatial and temporal scales. We integrated a large dataset of known nest and roost sites of a threatened species, the Mexican spotted owl (Strix occidentalis lucida), in the southwestern USA with Landsat imagery processed using the Continuous Change Detection and Classification (CCDC) time series algorithm on Google Earth Engine. We then used maximum entropy modeling (Maxent) to classify the landscape into four ‘spectral similarity’ classes that reflected the degree to which 30-m pixels contained a multispectral signature similar to that found at known owl nest/roost sites and mapped spectral similarity classes from 1986–2020. For map interpretation, we used nationally consistent forest inventory data to evaluate the structural and compositional characteristics of each spectral similarity class. We found a monotonic increase of structural characteristics typically associated with owl nesting and roosting over classes of increasing similarity, with the ‘very similar’ class meeting or exceeding published minimum desired management conditions for owl nesting and roosting. We also found an increased rate of loss of forest vegetation typical of owl nesting and roosting since the beginning of the 21st century that can be partly attributed to increased frequency and extent of large (≥400 ha) wildfires. This loss resulted in a 38% reduction over the 35-year study period in forest vegetation most similar to that used for owl nesting and roosting. Our modelling approach using cloud computing with time series of Landsat imagery provided a cost-effective tool for landscape-scale, multidecadal monitoring of vegetative components of a threatened species’ habitat. Our approach could be used to monitor trends in the vegetation favored by any other species, provided that high-quality location data such as we presented here are available.

A bi-directional strategy to detect land use function change using time-series Landsat imagery on Google Earth Engine: A case study of Huangshui River Basin in China

Constructed land, cropland, and ecological land are undergoing intense competition in many rapidly-developing regions. One of the major reasons to cause frequent land use (LU) conversions is the policy dynamics. The detection of such conversions is thus a prerequisite to understanding urban dynamics and how policies shape landscapes. This paper presents a bi-directional strategy to detect the LU change of the Huangshui River Basin of China from 1987 to 2018 using time-series Landsat imagery. We first initialized classification and optimization of remote sensing images using the Random Forest algorithm; We then detected bi-directional spatio-temporal changes based on the distribution probability of land-cover types. Our results reveal complicated dynamics underlying the net increase in urban and built-up land (UB) and the net decrease in cropland. In this area, due to the implementation of ecological compensation projects such as ecological migration and mine restoration, we found that on average 5.52 km2 of UB was converted into ecological land (forest, grassland and shrubland) every year, even though UB has expanded 3.6 times in the last 30 years with multiple conversions for cropland and ecological land. Meanwhile, 60% of lost cropland was converted to shrubland and grassland, and 40% was converted to UB. The accuracy of LU classification increases by 6.03% from 88.17%, and kappa coefficient increases by 2.41% from 85.16, compared to the existing initial results and uni-directional detection method. This study highlights the importance of the use of an effective remote sensing-based strategy for monitoring high-frequency LU changes in watershed areas with complicated human-nature interactions.

Urbanization influences small mammal composition, but not species richness in forest fragments in Accra, Ghana.

The Accra Plain of Ghana is experiencing rapid urbanization, but there is scant information on its impact on local biodiversity. We assessed the changes in land use/land cover of the Accra Plain since 1991 and evaluated how the observed changes have influenced local small mammals in forest fragments. We applied supervised classification and intensity analysis time-series Landsat imagery data to assess land use/land cover changes between 1991 and 2017. Small mammals were surveyed in two forest fragments, the Pinkwae and Adumanya forests from June 2019 to January 2020, using capture-mark-recapture technique. We compared our data with baseline data gathered in 1991-1992, when large areas of the city remained mostly undeveloped. Our data revealed that the urban area has increased by 832%, while the forest area declined by 85% between 1991 and 2017. The Pinkwae and Adumanya forests, which covered 120 and 1.5ha, respectively, in 1991 have each been reduced to < 1ha. We found changes in the small mammal species composition in the forest fragments, but not species richness due to species turnover. Grammomys poensis and Dephomys defua are first records for the Accra Plain. Our data suggested that small forest fragments within the urbanizing landscape are important for maintaining the local small mammal species. For the conservation of local small mammals in urbanizing landscapes, it is important to maintain the greatest possible number of small forest fragments and establish policies that prevent forest remnants from being further depleted.

A novel ensemble machine learning and time series approach for oil palm yield prediction using Landsat time series imagery based on NDVI

Accurate oil palm yield prediction is necessary to sustain oil palm production for food security and economic return. However, there are limited studies on comprehensive mapping and accurate oil palm yield prediction using advanced machine learning algorithms. Using multi-temporal remote sensing data, this paper proposed a new approach to predict oil palm yield based on the normalized difference vegetation index (NDVI) and ensemble machine learning algorithm. ReliefF algorithm with linear projection was employed to select the best combination of spectral indices in oil palm discrimination. Oil palm land cover was classified using random forest (RF) and modified AdaBoost algorithms. A time-series approach known as walk-forward validation was firstly introduced to train the model using the 2016-2019 data and the one-step prediction was performed for 2020 using RF and AdaBoost. Result of the study revealed that the RF model (RMSE = 0.384; MSE = 0.148; MAE = 0.147) outperformed the AdaBoost model (RMSE = 0.410; MSE = 0.168; MAE = 0.176). Our research has demonstrated the value of detailed mapping and subsequent yield prediction by developing a novel approach utilising time-series satellite imagery, ensemble machine learning, and NDVI, which will assist decision-makers in managing their practices related to oil palm.

Reconstruction of Satellite Time Series With a Dynamic Smoother

Time series reconstruction methods are widely used to generate smooth and gap-free time series using imagery acquired at coarse spatial resolution and high frequency return intervals. However, as interest has grown in leveraging the nearly 40-a record of Landsat to study long-term changes in terrestrial ecosystems at 30-m spatial resolution, new methods are required to reconstruct time series of Landsat imagery, which have lower temporal density than coarse resolution sensors such as AVHRR or MODIS. To address this need, we introduce a dynamic temporal smoothing (DTS) method that reconstructs sparse and noisy signals into dense time series at regular intervals. The DTS is a weighted smoother with parameters that adjust dynamically to variation in time series and can be applied to both dense and sparse time series measurements. Because the DTS smoother we describe is specifically designed to reconstruct high-quality time series of optical imagery, it has utility for applications focused on land cover and vegetation remote sensing over long time periods at moderate spatial resolution. We present the DTS algorithm that we have implemented and illustrate the ability of the DTS to reconstruct time series of Landsat imagery across multiple sensors (TM, ETM+, and OLI). To demonstrate the effectiveness of the DTS algorithm we apply it and evaluate results across a diverse range of land cover and vegetation types in the South American Southern Cone region.

Automatic Framework of Mapping Impervious Surface Growth With Long-Term Landsat Imagery Based on Temporal Deep Learning Model

The impervious surface (IS) cover and its dynamics are key parameters in research about urban and ecology. This letter proposed an automatic framework to map the IS growth end-to-end based on the temporal deep learning (DL) model and long time-series Landsat imagery. First, the training and validating datasets were auto-generated by a joint strategy. Then, a DL network was designed, and the IS growth was predicted in temporal windows. Finally, the results from multi-temporal windows are combined to generate the IS growth map. The data around the core of Beijing, China, is tested, and the result shows that the proposed method could: 1) efficiently model the IS growth; 2) map IS growth with less salt-and-pepper noise and false alarm compared to existing products; and 3) be extended to future data easily.

Tracking vegetation degradation and recovery in multiple mining areas in Beijing, China, based on time-series Landsat imagery

ABSTRACT Monitoring of the vegetation change trajectory in mining areas is crucial to understand ecosystem degradation induced by mining activities and to evaluate the effectiveness of ecological restoration. Beijing, the capital of China, is rich in mineral resources and has had many small-scale mine sites. In this study, we aimed to propose a new method to automatically characterize vegetation degradation and recovery trajectory for multiple mine sites. First, the method constructs temporal composites of the Normalized Differential Vegetation Index (NDVI) on a pixel-by-pixel basis using Landsat satellite observations on the Google Earth Engine platform; second, the Pettitt test and Sen+Mann–Kendall analysis are used to identify the year of change and pre- and post-change trends; then, the time-series NDVI is classified into five vegetation trajectory types [recovery ( R ), degradation ( D ), degradation–recovery ( D–R ), recovery–degradation ( R–D ), and no change ( NC )] and 13 subtypes; and finally, the recovery status of the mine sites is analyzed. The method was applied to track vegetation change in 500 mine sites in the Beijing mountainous area and compared to widely used the LandTrendr algorithm. The results showed that our method achieved satisfactory accuracies in trajectory type classification with an overall accuracy of 91.10%. The year of change detected by the Pettitt test was generally consistent with historical Google Earth high resolution imagery. Compared to the LandTrendr algorithm, our method yielded much less omission and commission errors of R, D, R–D, and D–R types and had better capability in identifying gradual recovery or degradation. As the method required few parameters, it is suitable for automatic trajectory monitoring of multiple mine sites. In the study area, 1469.07 ha out of 3746.25 ha of the mine sites have been recovered from 2000 to 2019. The recovery mainly occurred during 2009–2013, around the same time when the Green Mine Construction Campaign was launched in Beijing. 622.62 ha of the mine sites have experienced degradation probably due to illegal mining activities. Our results are expected to support evaluation of mine restoration effects and detection of illegal mine sites.

Time-Series Landsat Data for 3D Reconstruction of Urban History

Accurate quantification of vertical structure (or 3D structure) and its change of a city is essential for understanding the evolution of urban form, and its social and ecological consequences. Previous studies have largely focused on the horizontal structure (or 2D structure), but few on 3D structure, especially for long time changes, due to the absence of such historical data. Here, we present a new approach for 3D reconstruction of urban history, which was applied to characterize the urban 3D structure and its change from 1986 to 2017 in Shenzhen, a megacity in southern China. This approach integrates the contemporary building height obtained from the increasingly available data of building footprint with building age estimated based on the long-term observations from time-series Landsat imagery. We found: (1) the overall accuracy for building change detection was 87.80%, and for the year of change was 77.40%, suggesting that the integrated approach provided an effective method to cooperate horizontal (i.e., building footprint), vertical (i.e., building height), and temporal information (i.e., building age) to generate the historical data for urban 3D reconstruction. (2) The number of buildings increased dramatically from 1986 to 2017, by eight times, with an increased proportion of high-rise buildings. (3) The old urban areas continued to have the highest density of buildings, with increased average height of buildings, but there were two emerging new centers clustered with high-rise buildings. The long-term urban 3D maps allowed characterizing the spatiotemporal patterns of the vertical dimension at the city level, which can enhance our understanding on urban morphology.

Analysis of Temporal and Spatial Characteristics of Urban Expansion in Xiaonan District from 1990 to 2020 Using Time Series Landsat Imagery

With the rapid development in the global economy and technology, urbanization has accelerated. It is important to characterize the urban expansion and determine its driving force. In this study, we used the Xiaonan District in Hubei Province, China, as an example to map and quantify the spatiotemporal dynamics of urban expansion from the two perspectives of built-up area and urban land in 1990–2020 by using remote sensing images. The location of rivers was found to be a primary limiting factor for spatial patterns and expansion of the built-up area. The transfer of the city center and the main direction of expansion generally corresponded well to the topography, policies, and development strategies. The built-up area expanded faster than the urban population in 1995–2020, which caused a waste in land resources. The results showed that the urban expansion first decreased and then increased during the research period. The increase in the proportion of the secondary industry was the main driving force of the urban expansion. Based on the characteristics of urban expansion in the past three decades, we conclude that the urban land of Xiaonan District will expand quickly in the future and will occupy vast agricultural land. The government must deploy control measures to balance the benefits and costs of urban expansion.