Time Series Land Cover Research Articles

Spatiotemporal Evolution and Risk Analysis of Land Use in the Coastal Zone of the Yangtze River Delta Region of China

The extensive accumulation of big data, along with the development of a high-performance platform, bridge the gap between the previous inability to provide long-term time series and broad-scale coastal zone monitoring and risk warnings with remote sensing techniques. Based on 20 years of Landsat images from the Google Earth Engine platform, the time series land cover in the coastal zone of the Yangtze River Delta in China was classified. Then, a spatiotemporal clustering method based on grid segmentation was proposed to analyze the spatiotemporal evolution details of artificial surface expansion and the risks of cropland loss and ecological degradation caused by this. The results showed that significant changes have taken place in the quantitative structure and spatial morphology of coastal land use in the past 20 years. The artificial surface maintained a growth trend, increasing by 229%, while cropland decreased by 19%. Natural land showed a fluctuation pattern of “up→down→up”. The spatiotemporal details of land use obtained through 1km grid segmentation and clustering analysis were more significant. The artificial surface mainly underwent a progressive spatial expansion along the central urban area and important transportation axes (types III and IV), with the most dramatic changes occurring from 2010 to 2013. Type III cropland loss was the most significant, falling from 75.02% in 2000 to 38.23% in 2020. At the same time, the change in type III water body corresponds to the newly increased area of reclamation, which has decreased by 17% in the past 20 years, indicating that the degradation of coastal natural wetlands was significant. This paper provided a comprehensive diagnosis of coastal land use change, which could help policy makers and implementers to propose more targeted and differentiated coastal development and protection policies.

A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models

Abstract. The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992–2020) of annual land cover maps at 300 m resolution, allowing for a detailed study of land change dynamics over the contemporary era. Because models need two-dimensional parameters rather than two-dimensional land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first-generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but that lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300 m resolution for 1992–2020, created by fusing the 300 m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method. In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300 m resolution indicate the percentage cover (0 %–100 %) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e.g. re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models – Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and the Joint UK Land Environment Simulator (JULES) – to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare-soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks. The dataset is accessible at https://doi.org/10.5285/26a0f46c95ee4c29b5c650b129aab788 (Harper et al., 2023).

Environmental drivers of spatio-temporal dynamics in floodplain vegetation: grasslands as habitat for megafauna in Bardia National Park (Nepal)

Abstract. Disturbance-dependent grasslands, often associated with hydromorphological and fire dynamics, are threatened, especially in subtropical climates. In the Nepalese and Indian Terai Arc Landscape at the foot of the Himalayas, natural and cultural grasslands serve a viable role for greater one-horned rhinoceros (Rhinoceros unicornis) and for grazers that form prey of the Royal Bengal tiger (Panthera tigris). The grasslands are vulnerable to encroachment of forest. We aimed to establish the effects of environmental drivers, in particular river discharge, river channel dynamics, precipitation and forest fires, on the spatio-temporal dynamics of these grasslands. The study area is the floodplain of the eastern branch of the Karnali River and adjacent western part of Bardia National Park. We created annual time series (1993–2019) of land cover with the use of field data, remotely sensed LANDSAT imagery and a supervised classification model. Additionally, we analysed the pattern of grassland patches and aerial photographs of 1964. Between 1964 and 2019, grassland patches decreased in abundance and size due to encroachment of forest. Outside the floodplain, conversion of grassland to bare substrate coincides with extreme precipitation events. Within the floodplain, conversion of grassland to bare substrate correlates with the magnitude of the annual peak discharge of the bifurcated Karnali River. Since 2009, however, this correlation is absent due to a shift of the main discharge channel to the western branch of the Karnali River. Consequently, alluvial tall grasslands (Saccharum spontaneum dominant) have vastly expanded between 2009 and 2019. Because the hydromorphological processes in the floodplain have become more static, other sources of disturbances – local flooding of ephemeral streams, anthropogenic maintenance, grazing and fires – are more paramount to prevent encroachment of grasslands. Altogether, our findings underscore that a change in the environmental drivers impact the surface area and heterogeneity of grassland patches in the landscape, which can lead to cascading effects for the grassland-dependent megafauna.

Spatiotemporal graph-based analysis of land cover evolution using remote sensing time series data

Earth observation technology has improved the detection of land cover changes. However, current pixel-based change detection methods cannot adequately describe the evolutionary process and spatiotemporal association of geographic entities. Therefore, we developed a method for analyzing the processes and patterns of land cover evolution based on spatiotemporal graphs. First, a spatiotemporal graph was generated from a time series of land cover maps according to the spatial and temporal relationships between land cover objects, as defined by spatial adjacency and temporal transition, respectively. Subsequently, structural characteristics, such as the spatial roles, adjacency type, temporal transitions and evolution trajectories, were derived from the spatiotemporal graph to describe and analyze the evolution of land cover. Finally, this method was applied to analyze land cover evolution in Fujian Province, China, from 2001 to 2019. The proposed method not only completely preserves the spatial adjacency and temporal transition details among land cover objects in a spatiotemporally unified graph framework but also extracts evolution-related spatiotemporal structural characteristics. This study provides a reliable scientific basis for analyzing the consistency of long-term land cover dynamics and has practical value for other geographic applications.

Using Bayesian multitemporal classification to monitor tropical forest cover changes in Kalimantan, Indonesia

ABSTRACT Significant areas of native forest in Kalimantan, on the island of Borneo, have been cleared for the expansion of plantations of oil palm and rubber. In this study multisource remote sensing was used to develop a time series of land cover maps that distinguish native forest from plantations. Using a study area in east Kalimantan, Landsat images were combined with either ALOS PALSAR or Sentinel-1 images to map four land cover classes (native forest, oil palm plantation, rubber plantation, non-forest). Bayesian multitemporal classification was applied to increase map accuracy and maps were validated using a confusion matrix; final map overall accuracy was >90%. Over 18 years from 2000 to 2018 nearly half the native forests in the study area were converted to either non-forest or plantations of either rubber or oil palm, with the highest losses between 2015 and 2016. Trending upwards from 2008 large areas of degraded or cleared forests, mapped as non-forest, were converted to oil palm plantation. Conversion of native forests to plantation mainly occurred in lowland and wetland forest, while significant forest regrowth was detected in degraded peatland. These maps will help Indonesia with strategies and policies for balancing economic growth and conservation.

Determining of peri-urban in rice production centers area: Case study in Karawang Regency, Indonesia

In the last decade, the Jakarta-Bandung Mega Urban (JBMUR) corridor has experienced rapid urbanization, including the Karawang Regency. In addition, Karawang has become a national center (rice barn), with an area of rice fields in 2019 reaching 95,000 ha and covering 54% of its regency. The socio-economic spatial context of this region provides a new notion of peri-urban characteristics in the rice production center area. This study aims to identify the peri-urban areas and explain the peri-urban rice production center areas. The approach is a case study using geographic information system analysis (intersect, clip, feature to point) and data tabulation. The data are land cover time series maps from the Ministry of Environment and Forestry in 2019, village government maps (population and civil records 2019), and village potential data for 2011 and 2018. The analysis results show that the number of villages in the urban area is 49 villages, peri-urban 30 villages, and rural area 223 villages. Various variables were compared, showing urban area, peri-urban and rural area. Information on the characteristics of agricultural (rice field) centers can provide input for local governments in planning policy making.

Flood Vulnerability Evaluation and Prediction Using Multi-temporal Data: A Case in Tangerang, Indonesia

Land-use change has an impact on growing physical flood vulnerability. Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) approaches are increasingly being used for flood vulnerability assessments. However, none has used time-series land cover data for evaluation and rainfall over various return periods for prediction simultaneously, especially in Indonesia. Therefore, this study aims to evaluate and predict physical flood vulnerability using time-series land cover data and rainfall data over various return periods. Eight criteria were considered in the assessment: elevation, topographic wetness index, slope, distance to the river, distance downstream, soil type, rainfall, and land cover. The criteria weights were determined using the AHP method based on expert judgment. The multi-criteria model was built and validated using flood inundation data. Based on the validated model, the effect of land cover changes on flood vulnerability was evaluated. The flood vulnerability changes were also predicted based on rainfall over various return periods. The evaluation and prediction models have shown reliable findings. The criterion elevation and distance to the river significantly influenced the physical flood vulnerability by 41% and 20%. The evaluation model showed a strong correlation between the built-up area and the area with high flood vulnerability (r2 = 0.96). Furthermore, the model predicted an inundation area expansion for rainfall over various return periods. Further research using spatial data with higher resolution and more advanced validation techniques is needed to improve the model accuracy.

Interpreting Mangrove Habitat and Coastal Land Cover Change in the Greater Bay Area, Southern China, from 1924 to 2020 Using Historical Aerial Photos and Multiple Sources of Satellite Data

Coastal habitat dynamics and ecosystem function in response to human-induced disturbance, especially urbanization, are of increasing concern. However, how changes in landscape composition as well as habitat quantity and quality may affect the long-term sustainability of rapidly urbanizing coasts remains unclear. This study aimed to quantify the extent, change rate, patterns, change process and interrelationships of mangrove habitats, impervious surfaces, and other land cover types in Deep Bay in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), China, the world’s largest megalopolis, from 1924 to 2020. We processed historical aerial photos (1924–2020) and multiple sources of satellite data (1973–2020) for different types of land cover mapping. Post-classification analysis, including correlation analysis and change detection analysis, was conducted based on the long time-series land cover classification results. Mangrove habitats increased in Deep Bay from 1924 to 2020, except for a large area decrease from 1954 to 1964 due to the construction of tidal aquaculture ponds. Mudflat areas contributed most to the expansion of mangrove habitats of about 275 ha from 1987 to 2020. During this period, reclamation and urbanization for the construction of the megacity of Shenzhen turned large areas of water and mudflat (about 4000 ha) on the northern shore into impervious surface and urban vegetation. Overall, the landscape pattern of mangrove habitats in Deep Bay showed increasing connectivity and decreasing degree of fragmentation from 1987 to 2020. These changes have significant implications for the ecosystem services, e.g., supporting migratory waterbirds, supported by these wetlands.

Estimating Time of Urbanization with Moderate-Resolution Sensors

The continuous recording of images of the Earth’s surface through Earth observation satellites has enabled the study of large-scale dynamical process, such as urbanization. Mapping the urban land cover at a given time is a first-order problem that has been largely addressed, and for which several acceptable solutions exist. A second-order problem is the analysis of change through time, which has been generally addressed through change vector or change matrix analyses, but they tend to neglect the spatial dimensions in favor of the temporal one. In this study, we propose the estimation of the time of urbanization across space as a useful indicator of the spatiotemporal structure of the urbanization process. A map of the time of urbanization can be considered a third-order product, as it compresses time series of urban land cover in one image that can effectively help identify areas of accelerated development and the time when they occurred. We apply linear spectral unmixing to quarterly composites of Landsat 8 imagery over the metropolitan corridor in central Mexico, from which a time series of urban percentage coverage is generated for the period between 2013 and 2021. Time filters were then applied to the time series and the time of urbanization was detected as the time when an increase of 25 percent of urbanization or larger first occurred. The accuracy assessment of the urbanization time showed a good correspondence with reference points that were randomly selected in the area of study.

Time-series land cover mapping and urban expansion analysis using OpenStreetMap data and remote sensing big data: A case study of Guangdong-Hong Kong-Macao Greater Bay Area, China

• Optimized training samples from OpenStreetMap facilitate large-scale land cover mapping. • Sample optimization and transfer, and consistency modification strategies improve mapping results. • The proposed workflow achieves satisfactory performance and is transferable to other regions. Accurate land cover mapping provides important scientific support for ecological environment protection and sustainable urban development. However, given the high expense of acquiring training samples and the difficulty of fully utilizing remote sensing big data, large-scale time-series land cover mapping remains to be a challenge. To address the issue, we proposed a novel time-series large-scale mapping approach that obtains high-quality training samples from OpenStreetMap volunteered data and transfers them for land cover mapping in historical years. Relying on the data archived on the Google Earth Engine platform, we constructed a discriminating feature set that contains spectrum, texture, and backscatter coefficient, among others. Taking the Guangdong-Hong Kong-Macao Greater Bay Area as a case area, the annual land cover maps after spatio-temporal consistency modification from 1986 to 2021 were obtained. The validation samples proved that the derived land cover classification results have high accuracy. We verified the reliability and superiority of the proposed approach by comparing our results with existing land cover products. The proposed mapping approach owns great transferability, and the mapping results provide a valid decision-making basis for urban planning.

Simulating thaw-induced land cover change in discontinuous permafrost landscapes

Permafrost thaw is causing a rapid evolution of the lowland discontinuous permafrost regions of the Taiga Plains in Northern Canada and elsewhere in the Northern Hemisphere. Notably, this thaw is changing the spatial distribution of the dominant hydrologic land cover types (permafrost plateaus, fens, and isolated bogs) in parts of the Northwest Territories (NWT), Canada. Here, we develop a multinomial time series land cover model (TSLCM) to simulate historical land cover transitions, model spatial patterns of transition, and predict the long term evolution of land cover in the areas surrounding the Scotty Creek Research Station (SCRS), NWT, and similar discontinuous permafrost landscapes. The machine learning-based TSLCM is informed by a set of observed spatio-temporal variables. The independent variables represent driving factors of change, and include the estimated summertime land surface temperature anomaly (LST), the distance and a custom cost distance to land cover interfaces, time increment between initial and final states, and time-accumulated temperature; the dependent variable is classified land cover maps from 1970 to 2008. First, we applied both random forest (RF) and Multinomial Log-Linear Regression (MLR) methods to train a synthetic data model; the model which improves the performance of the TSLCM in extrapolating time series change by adding new data instances to the initial data set. We boosted the initial data by combining the predicted land cover change maps from synthetic data model and the real data set. Then, we evaluated a MLR, RF, and an extreme gradient boosting (XGBoost) model in their ability to simulate land cover change.The final results of this study show that the Ensemble Learning (EL) based approaches are capable of effectively representing historical land cover change and can produce physically consistent and plausible future land cover scenarios. Deterministic predictions from the TSLCM indicate that the permafrost plateaus’ coverage will continue to decrease, with corresponding decreases in isolated bogs’ coverage and their secondary runoff contributing areas.

Spatiotemporal Pattern Analysis of Land Use Functions in Contiguous Coastal Cities Based on Long-Term Time Series Remote Sensing Data: A Case Study of Bohai Sea Region, China

The long-term accumulated remote sensing data and the emerging cloud-based geospatial processing platform Google Earth Engine (GEE) enable the mining of the spatiotemporal pattern of land-use (LU) functional changes in the contiguous area of large coastal cities. This study proposes a spatiotemporal pattern mining technique for land use function in a large area, which consists of two parts: (1) long-term time series land cover mapping based on the random forest (RF) classification algorithm in the GEE platform and a pixel-by-pixel temporal consistency correction, and (2) spatiotemporal pattern mining based on the constructed spatial temporal cubes (STCs). Specifically, for each LU functional series, we constructed the STC and applied change point detection, time series clustering, and emerging hot spot analysis to mine the spatiotemporal change patterns of LU functions. The study shows that (1) the construction land in the Bohai Sea region from 1990 to 2020 expanded significantly, with the development intensity increasing from 2.08% to 9.77%, having formed a contiguous area of large cities; at the same time, the arable land area decreased significantly, from 57.94% to 47.83%; (2) the emerged construction land experienced three periods: fluctuation, rise, and decline, with 2004 and 2014 being the change points during the period; and (3), the spatial and temporal pattern of the expansion of construction land shows a spatial gradient change in the scale and rate of expansion along the central cities and major axes. This study demonstrates the potential of using long-term time series remote sensing data towards cognizing the generation mechanisms of contiguous coastal big cities.

Sentinel-2 Satellite Image Time-Series Land Cover Classification with Bernstein Copula Approach

A variety of remote sensing applications call for automatic optical classification of satellite images. Recently, satellite missions, such as Sentinel-2, allow us to capture images in real-time of the Earth’s scenario. The classification of this large amount of data requires increasingly precise and fast methods, which must take into account not only the spectral features dependence of each individual image but also that of the temporal ones. Copulas are an excellent statistical tool, able to model joint distributions between even random variables. In this paper, we propose a new approach for Satellite Image Time-Series (SITS) land cover classification, which combines the matrix factorization to reduce the dimensionality of the data and the use of copulas distribution to model the dependencies. We will show how the use of particular copulas can improve the accuracy of classification compared to the latest methodologies used for the classification task, such as those using Neural Networks. Experiments were conducted at a study site located on Reunion Island, using Sentinel-2 SITS data. Results are compared to those achieved by several approaches commonly used to address SITS-based land cover mapping and show that the use of copulas, in combination with the matrix factorization, achieved the highest classification yield compared to competing approaches.

Livelihood impacts of iron ore mining-induced land change in Sierra Leone: A time series analysis

This study explores the role of industrial iron ore mining on land change and local livelihoods in the Tonkolili mineral rich region of northeastern Sierra Leone. The study integrates time series of land use and land cover (LULC), statistical modeling, and qualitative analytical framework to provide a comprehensive assessment of livelihood response to iron ore mining induced land change over an 8-year period (2010–2018). Results of the study demonstrated that the establishment of industrial iron ore mines in 2011 resulted in gradual urban land expansion (average of 7.5%) and exponential population growth (600%) in search of employment through 2018. Due to the inability of the iron ore mining companies to absorb the excess influx of labor, residents resorted to alternative livelihood activities of which artisanal gold mining became prominent. Growth in industrial iron ore mining footprint reduced farming livelihood (- 48%) within the mining concession area, generated several lakes and ponds which polluted fertile swamplands outside the mining area with negative implications for farming, the dominant livelihood activity in the study area. Between 2011 and 2018, the iron ore mining industry directly provided jobs for some people and enhanced livelihoods in its ancillary industries. Notwithstanding, the negative livelihood impacts of iron ore mining on agricultural productivity and forest products was far higher than the positive impacts.

Effects of Climate and Anthropogenic Drivers on Surface Water Area in the Southeastern United States

AbstractSurface water is the most readily accessible water resource and provides an array of ecosystem services, but its availability and access are stressed by changes in climate, land cover, and population size. Understanding drivers of surface water dynamics in space and time is key to better managing our water resources. However, few studies estimating changes in surface water account for climate and anthropogenic drivers both independently and together. We used 19 years (2000–2018) of the newly developed Dynamic Surface Water Extent Landsat Science Product in concert with time series of precipitation, temperature, land cover, and population size to statistically model maximum seasonal percent surface water area as a function of climate and anthropogenic drivers in the southeastern United States. We fitted three statistical models (linear mixed effects, random forests, and mixed effects random forests) and three groups of explanatory variables (climate, anthropogenic, and their combination) to assess the accuracy of estimating percent surface water area at the watershed scale with different drivers. We found that anthropogenic drivers accounted for approximately 37% more of the variance in the percent surface water area than the climate variables. The combination of variables in the mixed effects random forest model produced the smallest mean percent errors (mean −0.17%) and the highest explained variance (R2 0.99). Our results indicate that anthropogenic drivers have greater influence when estimating percent surface water area than climate drivers, suggesting that water management practices and land‐use policies can be highly effective tools in controlling surface water variations in the Southeast.

VALIDATION OF LAND DEGRADATION CARDS ON THE BASIS OF GEOSPATIAL DATA

Today there is a lot of satellite data and products based on it in the public domain. By integrating them with heterogeneous socio-economic information and soil maps, model biophysical data using modern machine learning methods and modern approaches to geospatial data processing, it becomes possible to create maps of land degradation. Considering that classification maps, productivity maps and deforestation maps are the main intellectual components to create a degradation map, it is these products that affect the overall reliability of the results. For their validation, the necessary quality metrics are determined in the work, and the corresponding calculations are made. To evaluate the land cover map, independent test data were used to build a confusion matrix, and the obtained areas of the main crops were compared with statistical data. Agricultural land productivity was estimated using time series land cover classification maps and Crop Growth Modeling System (CGMS) biophysical plant development, as well as biophysical plant growth parameters using satellite data and biophysical plant development models. The LAI Map Accuracy Assessment (based on CGMS) is based on the comparison of Leaf Area Index (LAI) values modeled using the CGMS software framework with LAI ground measurement data collected through ground surveys. Numerous experiments were carried out to assess the quality of models and the results of deforestation maps on an independent test sample, which was not used at the neural network training stage. Degradation maps for several years were also analyzed and their validation was carried out with respect to productivity, in particular for the region that has undergone significant changes for the territory of Ukraine.

Combining pixel- and object-level information for land-cover mapping using time-series of Sentinel-2 satellite data

ABSTRACT In this letter, we propose a new methodology for Satellite Image Time Series (SITS) land cover mapping, named Two Branches Convolutional Neural Network (TwoBCNN). The main objective of the proposed methodology is to combine pixel- and object-level multi-variate time-series information in the classification process. Experiments were carried out on a study site located in the south-west of France, namely, Dordogne leveraging Sentinel-2 SITS data. Results are compared to those obtained by several standard used approaches to deal with SITS-based land cover mapping. Results demonstrate that TwoBCNN, based on a combination of pixel- and object-based information, achieved the highest classification performance with respect to the competing approaches.

Land Cover Changes after the Massive Rohingya Refugee Influx in Bangladesh: Neo-Classic Unsupervised Approach

The Rohingya refugee influx to Bangladesh in 2017 was a historical incident; the number of refugees was so massive that significant impacts to local communities was inevitable. The Bangladesh government provided land in a preserved area for constructing makeshift camps for the refugees. Previous studies have revealed the land cover changes and impacts of the refugee influx around campsites, especially with regard to local forest resources. Our aim is to establish a convenient approach of providing up-to-date information to monitor holistic local situations. We employed a classic unsupervised technique—a combination of k-means clustering and maximum likelihood estimation—with the latest rich time-series satellite images of Sentinal-1 and Sentinal-2. A combination of VV and normalized difference water index (NDWI) images was successful in identifying built-up/disturbed areas, and a combination of VH and NDWI images was successful in differentiating wetland/saltpan, agriculture /open field, degraded forest/bush, and forest areas. By doing this, we provided annual land cover classification maps for the entire Teknaf peninsula for the pre- and post-influx periods with both fair quality and without prior training data. Our analyses revealed that on-going impacts were still observed by May 2021. As a simple estimation of the intervention consequence, the built-up/disturbed areas increased 6825 ha (compared with the 2015–17 period). However, while the impacts on the original forest were not found to be significant, the degraded forest/bush areas were largely degraded by 4606 ha. These cultivated lands would be used for agricultural activities. This is in line with the reported farmers’ increased income, despite local people with other occupations that are all equally facing the decreases in income. The convenience of our unsupervised classification approach would help keep accumulating a time-series land cover classification, which is important in monitoring impacts on local communities.

A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping

The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change detection and characterization at local or global scales. However, the reliable, rapid, and reproducible collection of training samples have become a challenge for time series land cover classification at a large scale. To meet the challenge, we proposed an automatic phenology learning (APL) method with the assumption that the temporal profiles of samples within the same land cover type are the same or similar at a local scale to generate evenly distributed training samples automatically. We designed the method to build land cover patterns for each category based on consensus samples derived from multiple existing scientific datasets including LANDFIRE's (LF) Existing Vegetation Type (EVT), USGS National Land Cover Database (NLCD), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL), and National Wetlands Inventory (NWI). Then we calculated the Time-Weighted Dynamic Time Warping (twDTW) distance between any undefined samples and land cover patterns in the same geographical region as prior knowledge. Finally, we selected the optimal land cover category for each undefined sample from the land cover products based on the designed criteria iteratively using the twDTW distance as an indicator. The method was applied in the footprint of 10 selected Landsat Analysis Ready Data (ARD) tiles in the eastern and western conterminous United States (CONUS) to produce annual land cover maps from 1985 to 2017. The accuracy assessment and visual comparison revealed that the APL method can generate reliable training samples without any manual interpretation, producing better land cover results especially for the grass/shrub and wetland land cover classes. Applying the APL method, the overall accuracy of the annual land cover maps was improved by 2% over the accuracy of Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products in the research regions. Our results also indicate that the APL method provides an approach for best use of different land cover products and meets the requirement of intensive sampling for training data collection.

Land use and land cover changes in São Paulo Macro Metropolis and implications for water resilience under climate change

The São Paulo Macro Metropolis (MMP) is a geographical arrangement that brings together the most significant Brazilian socioeconomic figures and faces numerous challenges, such as heavy pressure on natural resources. Through compilation, spatialization and cross-referencing of data, this work assessed time series of land use and land cover of the last decades, the water supply status in an urban environment and climate data projection for MMP. Municipalities with different profiles were identified: those with positive final balances and those with negative final balances about the maintenance of their natural areas. Furthermore, more than half of the municipalities of the MMP have low water supply assurance, which can be aggravated due to climate change, as predicted in several climate models. The characterization of municipalities based on this information allowed identifying which of them are the most vulnerable, and these results indicate paths for coordinated actions at local and regional levels to increase water resilience in the macro-region.