Moderate Resolution Satellite Imagery Research Articles

Investigating the interplay between urban dynamics and environmental factors in Thimphu, Bhutan: a satellite remote sensing approach.

The ability of the land surface temperature (LST) and normalized difference vegetation index (NDVI) to examine land surface change is regarded as an important climate variable. However, no significant systematic examination of urbanization concerning environmental variables has been undertaken in the narrow valley of Thimphu, Bhutan. Therefore, this study investigated the impact of land use/land cover (LULC) dynamics on LST, NDVI, and elevation, using Moderate Resolution Imaging Spectroradiometer (MODIS) data collected in Thimphu, Bhutan, from 2000 to 2020. The results showed that LSTs varied substantially among different land use types, with the highest occurring in built-up areas and the lowest occurring in forests. There was a strong negative linear correlation between the LST and NDVI in built-up areas, indicating the impact of anthropogenic activities. Moreover, elevation had a noticeable effect on the LST and NDVI, which exhibited very strong opposite patterns at lower elevations. In summary, LULC dynamics significantly influence LST and NDVI, highlighting the importance of understanding spatiotemporal patterns and their effects on ecological processes for effective land management and environmental conservation. Moreover, this study also demonstrated the applicability of relatively low-cost, moderate spatial resolution satellite imagery for examining the impact of urban development on the urban environment in Thimphu city.

VedgeSat: An automated, open‐source toolkit for coastal change monitoring using satellite‐derived vegetation edges

AbstractPublic satellite platforms offer regular observations for global coastal monitoring and climate change risk management strategies. Unfortunately, shoreline positions derived from satellite imagery, representing changes in intertidal topography, are noisy and subject to tidal bias that requires correction. The seaward‐most vegetation boundary reflects a change indicator which shifts on event–decadal timescales, and informs coastal practitioners of storm damage, sediment availability and coastal landform health. We present and validate a new open‐source tool VedgeSat for identifying vegetation edges (VEs) from high (3 m) and moderate (10–30 m) resolution satellite imagery. The methodology is based on the CoastSat toolkit, with streamlined image processing using cloud‐based data management via Google Earth Engine. Images are classified using a newly trained vegetation‐specific neural network, and VEs are extracted at subpixel level using dynamic Weighted Peaks thresholding. We performed validation against ground surveys and manual digitisation of aerial imagery across eroding and accreting open coasts and estuarine environments at a site in Scotland. Smaller‐than‐pixel vegetation boundary detection was achieved across 83% of Sentinel‐2 imagery (Root Mean Square Error of 9.3 m). An overall RMSE of 19.0 m was achieved across Landsat 5 & 8, Sentinel‐2 and PlanetScope images. Performance varied by coastal geomorphology, with highest accuracies across sandy open coasts owing to high spectral contrast and less false positives from intertidal vegetation. The VedgeSat tool can be readily applied in tandem with waterlines near‐globally, to support adaptation decisions with historic coastal trends across the whole shoreface, even in normally data‐scarce areas.

Evidence of Abrupt Transitions Between Sea Ice Dynamical Regimes in the East Greenland Marginal Ice Zone

AbstractSea ice modulates the energy exchange between the atmosphere and the ocean through its kinematics. Marginal ice zone (MIZ) dynamics are complex and are not well resolved in routine observations. Here, we investigate sea ice dynamics in the Greenland Sea MIZ using in situ and remote sensing Lagrangian drift datasets. These datasets provide a unique view into ice dynamics spanning spatial scales. We find evidence of tidal currents strongly affecting sub‐daily ice motion. Velocity anomalies show abrupt transitions aligned with gradients in seafloor topography, indicating changes in ocean currents. Remote‐sensed ice floe trajectories derived from moderate resolution satellite imagery provide a view of small‐scale variability across the Greenland continental shelf. Ice floe trajectories reveal a west‐east increasing velocity gradient imposed by the East Greenland Current, with maximum velocities aligned along the continental shelf edge. These results highlight the importance of small scale ocean variability for ice dynamics in the MIZ.

Continuous monitoring and sub-annual change detection in high-latitude forests using Harmonized Landsat Sentinel-2 data

Spatially and temporally precise monitoring of forest resources is becoming increasingly vital in rapidly changing environmental and economic conditions. However, recent developments in moderate resolution satellite imagery have led to spectrally consistent and temporally dense datasets capable of capturing both seasonal variation and long-term trajectories. Previous studies have demonstrated the ability of continuous monitoring algorithms to detect transitional changes such as timber harvesting at sub-annual scales. However, less is known about the ability to use these algorithms in areas such as high-latitude forests which have large data gaps over winter due to prolonged snow cover. This study investigated the use of a time series of 7-day Harmonized Landsat Sentinel-2 (HLS) composites along with the Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) algorithm for change detection and monitoring in two managed forest areas in Canada. On average, both sites had no data periods (over winter) lasting ∼200 days. Despite this, BEAST successfully detected change, with overall accuracies of 88.7% and 97.3% for both sites when compared to an independent validation dataset derived from visual interpretation of PlanetScope images. A simulation of change detection in near-real time revealed that approximately 75% of changes were detected within 7 and 11 valid images for each site. Furthermore, monitoring the trend slope of the spectral index showed that areas with a new declining slope were typically about twice as likely to occur in photointerpreted polygons of non-stand replacing disturbance, indicating that such a technique could be used as an early warning signal of fine-scale changes such as those due to insect defoliation or drought. Overall, this study demonstrated the efficacy of a continuous change detection and monitoring framework, which could be used to provide insights at previously unavailable temporal and spatial scales, thereby allowing forest managers the ability to quickly react to changes in uncertain future conditions.

Detecting canopy damage caused by Uromycladium acaciae on South African Black Wattle forest compartments using moderate resolution satellite imagery

Uromycladium acaciae, also known as wattle rust, is a rust fungus that has adversely impacted black wattle (Acacia mearnsii) in South Africa. This study assessed the potential of the Landsat 8 multispectral sensor to detect canopy damage caused by wattle rust on two plantation farms near Richmond, KwaZulu-Natal. The Landsat 8 bands and vegetation indices detected forest canopy damage caused by Uromycladium acaciae with an accuracy of 88.24% utilising seven bands and the Partial Least Squares Discriminate Analysis (PLS-DA) algorithm. Additionally, the model was optimised using the Variable Importance in Projection (VIP) method which only selected the most influential bands in the model. The coastal aerosol band (430nm-450nm), red band (640nm-670nm), near infrared (850nm-880nm) and NDVI were exclusively used in the optimised model and an accuracy of 82.35% was produced. The study highlighted the potential of remote sensing to detect canopy damage caused by a rust fungus and contributes towards a monitoring framework for analysing trends using freely available Landsat 8 imagery

A Global Analysis of the Spatial and Temporal Variability of Usable Landsat Observations at the Pixel Scale

The Landsat program has the longest collection of moderate-resolution satellite imagery, and the data are free to everyone. With the improvements of standardized image products, the flexibility of cloud computing platforms, and the development of time series approaches, it is now possible to conduct global-scale analyses of time series using Landsat data over multiple decades. Efforts in this regard are limited by the density of usable observations. The availability of usable Landsat Tier 1 observations at the scale of individual pixels from the perspective of time series analysis for land change monitoring is remarkably variable both in space (globally) and time (1985–2020), depending most immediately on which sensors were in operation, the technical capabilities of the mission, and the acquisition strategies and objectives of the satellite operators (e.g., USGS, commercial company) and the international ground receiving stations. Additionally, analysis of data density at the pixel scale allows for the integration of quality control data on clouds, cloud shadows, and snow as well as other properties returned from the atmospheric correction process. Maps for different time periods show the effect of excluding observations based on the presence of clouds, cloud shadows, snow, sensor saturation, hazy observations (based on atmospheric opacity), and lack of aerosol optical depth information. Two major discoveries are: 1) that filtering saturated and hazy pixels is helpful to reduce noise in the time series, although the impact may vary across different continents; 2) the atmospheric opacity band needs to be used with caution because many images are removed when no value is given in this band, when many of those observations are usable. The results provide guidance on when and where time series analysis is feasible, which will benefit many users of Landsat data.

Combining Tabular and Satellite-Based Datasets to Better Understand Cropland Change

In recent years, regulatory agencies in the USA and Europe have begun to require documentation that land used to produce crops and biofuels has not been converted from carbon-capturing grasslands or forests. Precise measurement of these land cover changes, however, has proven difficult. Analysis to date has focused primarily on moderate resolution (30 m) satellite imagery, which has not provided the land cover granularity or accuracy needed. These studies have estimated large-scale land conversion to crops in the USA. This study analyzed the satellite datasets but included tabular datasets and aerial imagery of the USA to determine whether the combination of datasets, focusing on more detailed analysis in these locations, could more accurately identify potential locations of land use change. Analyses of satellite imagery data from 1985 to 2020 found that much of the land that 2008 to 2020 satellite datasets classified as natural-to-crop land change was idle cropland. The results indicate a dynamic landscape of marginal land moving in and out of cropland. Approximately as much land was allowed to go fallow (6145 hectares) as land going into crop (7901 hectares) from 1985 to 2020. The results from this study indicate regulatory agencies could more accurately measure the impacts of conversion of natural lands to crop if long-term historical land cover/land use was also analyzed.

Detection of Tree Decline (Pinus pinaster Aiton) in European Forests Using Sentinel-2 Data

Moderate-resolution satellite imagery is essential to detect conifer tree decline on a regional scale and address the threat caused by pinewood nematode (PWN), (Bursaphelenchus xylophilus. This is a quarantine organism responsible for pine wilt disease (PWD), which has caused substantial ecological and economic losses in the maritime pine (Pinus pinaster) forests of Portugal. This study describes the first instance of a pre-operational algorithm applied to Sentinel-2 imagery to detect PWD-compatible decline in maritime pine. The Random Forest model relied on a pre-wilting and an in-season image, calibrated with data from a 24-month long field campaign enhanced with Worldview-3 data and the analysis of biological samples (hyperspectral reflectance, pigment quantification in needles, and PWN identification). Independent validation results attested to the good performance of the model with an overall accuracy of 95%, particularly when decline affects more than 30% of the 100 m2 pixel of Sentinel-2. Spectral angle mapper applied to hyperspectral measurements suggested that PWN infection cannot be separated from other drivers of decline in the visible-near infrared domain. Our algorithm can be employed to detect regional decline trends and inform subsequent aerial and field surveys, to further investigate decline hotspots.

Aboveground biomass patterns across treeless northern landscapes

ABSTRACT Aboveground vegetation biomass in northern treeless landscapes – peatlands and Arctic tundra – has been modelled with spectral information derived from optical remote sensing in several studies. However, synthesized overviews of biomass patterns across circumpolar sites have been limited. Based on data from eight study sites in Europe, Siberia and Canada, we ask (1) how biomass is divided between plant functional types (PFTs) and (2) how well biomass patterns can be detected with widely available, moderate spatial resolution (3–10 m) satellite imagery and topographic data. We explain biomass patterns using random forest regressions with the predictors being spectral bands and indices calculated from multi-temporal Sentinel-2 and PlanetScope imagery and topographic information calculated from ArcticDEM data. Our results indicate that there are notable differences in vegetation composition between northern landscapes with mosses, graminoids and deciduous shrubs being the most dominant PFTs. Remote sensing data detects biomass patterns, but regression performance varies between sites (explained variance 36–70%, normalized root mean square error 9–19%). There is also variability between sites whether Sentinel-2 or PlanetScope data is more suitable to detect biomass patterns and which the most important predictors are. Topographic information has a minor or negligible importance in most of the sites. Our results suggest that there is no easily generalizable relationship between satellite-derived vegetation greenness and biomass.

Environmental Armed Conflict Assessment Using Satellite Imagery

Abstract Armed conflicts not only affect human populations but can also cause considerable damage to the environment. Its consequences are as diverse as its causes, including; water pollution from oil spills, land degradation due to the destruction of infrastructure, poisoning of soils and fields, destruction of crops and forests, over-exploitation of natural resources and paradoxically and occasionally reforestation. In this way, the environment in the war can be approached as beneficiary, stage, victim or/and spoil of war. Although there are few papers that assess the use of remote sensing methods in areas affected by warfare, we found a gap in these studies, being both outdated and lacking the correlation of remote sensing analysis with the causes-consequences, biome features and scale. Thus, this paper presents a methodical approach focused on the assessment of the existing datasets and the analysis of the connection between geographical conditions (biomes), drivers and the assessment using remote sensing methods in areas affected by armed conflicts. We aimed to find; weaknesses, tendencies, patterns, points of convergence and divergence. Then we consider variables such as biome, forest cover affectation, scale, and satellite imagery sensors to determine the relationship between warfare drivers with geographical location assessed by remote sensing methods. We collected data from 44 studies from international peer-reviewed journals from 1998 to 2019 that are indexed using scientific search engines. We found that 62% of the studies were focused on the analysis of torrid biomes as; Tropical Rainforest, Monsoon Forest / Dry Forest, Tree Savanna and Grass Savanna, using the 64% Moderate-resolution satellite imagery sensors as; Landsat 4-5 TM and Landsat 7 ETM+. Quantitative analysis of the trends identified within these areas contributes to an understanding of the reasons behind these conflicts.

A paradigm of GIS and remote sensing for crop water deficit assessment in near real time to improve irrigation distribution plan

This study comprehensively used Remote Sensing (RS) and GIS for independently monitoring crop water adequacy in response to existing irrigation management system in Bari Doab, Pakistan. Canals’ rotation plan is developed before start of each cropping season to distribute water on fixed turn basis. The study combines the moderate (Landsat-8) and low resolution (MODIS) satellite imagery to assess near real time crop water deficit in canal command areas (CCAs) of the study area. Landsat imagery were used to develop crop classification and determine cropping pattern using time series of NDVI. MODIS’ NDVI product was employed to monitor the current crop health and develop annual reference NDVI cycle based on last 13 years’ imagery. Climatic data of WMO stations combined with DEM have also been used to determine reference evapotranspiration based on Penman-Monteith method. These inputs were combined to estimate average crop water deficit in CCAs for ongoing cropping season on 08-day interval, the interval parallel to the canal rotation plan. RS based reflectance crop coefficients (KCR) of current and reference crop cycle were developed to calculate potential and actual evapotranspiration, and then translated their difference into crop water deficit. Lag time of 08–10 days is limitation of the study due to satellites overpass time, but it can still prove very helpful considering the crop season spanned over 160–180 days. Results revealed that maximum crop water deficit (up to 9 mm) occurred in CCAs during crop growing (mid) stage. Crop water deficit showed inverse relation when plotted against intense rainfall months. Crop water deficit results could be helpful for irrigation managers to modify canals’ rotation plan in near real time according to crop health and growing stage. It would not only help in achieving equitable water distribution but also assuring supplies when required by the crops.

High-Resolution Surface Water Classifications of the Xingu River, Brazil, Pre and Post Operationalization of the Belo Monte Hydropower Complex

We describe a new high spatial resolution surface water classification dataset generated for the Xingu river, Brazil, from its confluence with the Iriri river to the Pimental dam prior to construction of the Belo Monte hydropower complex, and after its operationalization. This river is well-known for its exceptionally high diversity and endemism in ichthyofauna. Pre-existing datasets generated from moderate resolution satellite imagery (e.g., 30 m) do not adequately capture the extent of the river. Accurate measurements of water extent are important for a range of applications utilizing surface water data, including greenhouse gas emission estimation, land cover change mapping, and habitat loss/change estimates, among others. We generated the new classifications from RapidEye imagery (5 m pixel size) for 2011 and PlanteScope imagery (3 m pixel size) for 2019 using a Geographic Object Based Image Analysis (GEOBIA) approach.

Comparison of pixel unmixing models in the evaluation of post-fire forest resilience based on temporal series of satellite imagery at moderate and very high spatial resolution

In Mediterranean fire-prone ecosystems, shifts in fire regime as a consequence of global change could modify the resilience of vegetation communities. In this paper, we aim to compare the efficiency of high and moderate spatial resolution satellite imagery in the evaluation of resilience in a fire-prone landscape under different fire regime categories using two pixel unmixing techniques. A time series of Landsat (ETM+ and OLI; spatial resolution of 30 m) and WorldView-2 (spatial resolution of 2 m) imagery collected between 2011 (pre-fire conditions) and 2016 were used to estimate the temporal variation of fractional vegetation cover (FVC) as a quantitative measure of forest resilience. For this time series, FVC was computed under four fire-regime categories of recurrence and severity using two approaches: dimidiate pixel model and multiple endmember spectral mixture analysis (MESMA). The dimidiate pixel model was computed using NDVI as spectral response for the case of Landsat imagery and NDVI and red-edge NDVI (RENDVI) for WorldView-2. MESMA was applied to unmix WorldView-2 and Landsat imagery into four fraction images: photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), soil and shade. The PV shade normalized fraction corresponds to the FVC. In summer of 2016 we established 85 30 × 30 m field plots and 360 2 × 2 m field plots to measure the percentage of total vegetation cover in order to validate the FVC estimates made from remote sensing data. The FVC time series showed the same general pattern with both spatial scales and modeling approaches, high fire recurrence categories registering the highest resilience. The accuracy of the dimidiate pixel model was significantly higher for WorldView-2 based estimates (RMSE: 5–10%) than for Landsat (RMSE: 10–15%). The dimidiate pixel model computed from NDVI for both Landsat and WorldView-2 underestimated FVC at high field-sampled vegetation cover, while MESMA estimations were accurate for the entire range of vegetation cover for both satellites. The fraction of photosynthetic vegetation calculated using WorldView-2 had a higher performance (RMSE: 4–6%) than that quantified from Landsat (RMSE: 6–8%). The linear relationships assumed for validation purposes were statistically significant for both sensors and modeling approaches. Our study demonstrates the highest performance of very high spatial resolution satellite imagery and MESMA models in the quantitative estimation of FVC as a measure of post-fire resilience.

Improved Detection of Inundation below the Forest Canopy using Normalized LiDAR Intensity Data

To best conserve wetlands and manage associated ecosystem services in the face of climate and land-use change, wetlands must be routinely monitored to assess their extent and function. Wetland extent and function are largely driven by spatial and temporal patterns in inundation and soil moisture, which to date have been challenging to map, especially within forested wetlands. The objective of this paper is to investigate the different, but often interacting effects, of evergreen vegetation and inundation on leaf-off bare earth return lidar intensity within mixed deciduous-evergreen forests in the Coastal Plain of Maryland, and to develop an inundation mapping approach that is robust in areas of varying levels of evergreen influence. This was achieved through statistical comparison of field derived metrics, and development of a simple yet robust normalization process, based on first of many, and bare earth lidar intensity returns. Results demonstrate the confounding influence of forest canopy gap fraction and inundation, and the effectiveness of the normalization process. After normalization, inundated deciduous forest could be distinguished from non-inundated evergreen forest. Inundation was mapped with an overall accuracy between 99.4% and 100%. Inundation maps created using this approach provide insights into physical processes in support of environmental decision-making, and a vital link between fine-scale physical conditions and moderate resolution satellite imagery through enhanced calibration and validation.

Transitioning from change detection to monitoring with remote sensing: A paradigm shift

The use of time series analysis with moderate resolution satellite imagery is increasingly common, particularly since the advent of freely available Landsat data. Dense time series analysis is providing new information on the timing of landscape changes, as well as improving the quality and accuracy of information being derived from remote sensing. Perhaps most importantly, time series analysis is expanding the kinds of land surface change that can be monitored using remote sensing. In particular, more subtle changes in ecosystem health and condition and related to land use dynamics are being monitored. The result is a paradigm shift away from change detection, typically using two points in time, to monitoring, or an attempt to track change continuously in time. This trend holds many benefits, including the promise of near real-time monitoring. Anticipated future trends include more use of multiple sensors in monitoring activities, increased focus on the temporal accuracy of results, applications over larger areas and operational usage of time series analysis.

Urban Built-Up Area Estimation and Change Detection of the Occupied West Bank, Palestine, Using Multi-temporal Aerial Photographs and Satellite Images

The study aims to investigate the urban change in the West Bank between the years 1997 to 2016, under complicated geopolitical conditions. High-resolution aerial photographs (1.25 m and 0.1 m) and moderate-resolution satellite imagery (30 m resolution Landsat data) were used in this study. The results of the study showed that the Palestinians suffer from serious limitations imposed by the Israeli occupation due to the urban expansion, especially in the so-called area C, which represents 61% of the West Bank. The Israeli occupation controls this area. On the other hand, the Israeli occupation provides an opportunity to the Israeli settlers to build more houses and buildings for various purposes. The study also shows that most of the Palestinian urban expansion comes at the expense of agricultural lands. The study recommends that the international community has to place more pressure on Israel to give the Palestinians their freedom to plan for their future, and use their lands freely.

Extensive land cover change across Arctic-Boreal Northwestern North America from disturbance and climate forcing.

A multitude of disturbance agents, such as wildfires, land use, and climate-driven expansion of woody shrubs, is transforming the distribution of plant functional types across Arctic-Boreal ecosystems, which has significant implications for interactions and feedbacks between terrestrial ecosystems and climate in the northern high-latitude. However, because the spatial resolution of existing land cover datasets is too coarse, large-scale land cover changes in the Arctic-Boreal region (ABR) have been poorly characterized. Here, we use 31years (1984-2014) of moderate spatial resolution (30m) satellite imagery over a region spanning 4.7×106 km2 in Alaska and northwestern Canada to characterize regional-scale ABR land cover changes. We find that 13.6±1.3% of the domain has changed, primarily via two major modes of transformation: (a) simultaneous disturbance-driven decreases in Evergreen Forest area (-14.7±3.0% relative to 1984) and increases in Deciduous Forest area (+14.8±5.2%) in the Boreal biome; and (b) climate-driven expansion of Herbaceous and Shrub vegetation (+7.4±2.0%) in the Arctic biome. By using time series of 30m imagery, we characterize dynamics in forest and shrub cover occurring at relatively short spatial scales (hundreds of meters) due to fires, harvest, and climate-induced growth that are not observable in coarse spatial resolution (e.g., 500m or greater pixel size) imagery. Wildfires caused most of Evergreen Forest Loss and Evergreen Forest Gain and substantial areas of Deciduous Forest Gain. Extensive shifts in the distribution of plant functional types at multiple spatial scales are consistent with observations of increased atmospheric CO2 seasonality and ecosystem productivity at northern high-latitudes and signal continental-scale shifts in the structure and function of northern high-latitude ecosystems in response to climate change.

Evaluation of Land Cover Change and Agricultural Protection Sites: A GIS and Remote Sensing Approach for Ho Chi Minh City, Vietnam

Ho Chi Minh City (HCMC), economic center and most populous city of Vietnam faces a strong structural change since its market liberalization in the late 1980s. Big challenges occur in the form of uncontrolled urban sprawl due to rapid population growth with encroachment of agricultural land, which leads to environmental and climatic issues like urban heat island effects, air pollution and flooding events. Remote Sensing and Geographic Information Systems (GIS) provide new computer-based technologies for urban planners which can greatly ease the monitoring of agricultural loss as well as improve decision making for future land management. In the first part of this study, land cover change dynamics are thoroughly assessed using moderate and high spatial resolution satellite imagery (Landsat and SPOT) over the period 2010–2017 in 22 districts of HCMC. In the second part, the land cover classification results of 2017 provide the initial map for a GIS-based Multi-Criteria-Decision-Analysis (MCDA) of potential agricultural protection sites. Therefore, criteria of climate adaptation and ecological service are established and embedded in the GIS-compatible Compromise Programming Model (CP). With the use of Analytic Hierarchy Process (AHP) by Thomas L. Saaty and additional expert knowledge, appropriate weighting factors have been affiliated. The results show that agricultural land decreased by more than two thirds in the period considered. However, particularly the western rural districts Bình Chánh and Hóc Môn still offer a great amount of valuable agricultural land suitable for protection. The proposed method can serve as a scientific framework for planning departments of fast growing cities to zone agricultural land for protection on an early planning stage in order to ensure sustainable land use development in the future.

Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine

The Prairie Pothole Region of North America is characterized by millions of depressional wetlands, which provide critical habitats for globally significant populations of migratory waterfowl and other wildlife species. Due to their relatively small size and shallow depth, these wetlands are highly sensitive to climate variability and anthropogenic changes, exhibiting inter- and intra-annual inundation dynamics. Moderate-resolution satellite imagery (e.g., Landsat, Sentinel) alone cannot be used to effectively delineate these small depressional wetlands. By integrating fine spatial resolution Light Detection and Ranging (LiDAR) data and multi-temporal (2009–2017) aerial images, we developed a fully automated approach to delineate wetland inundation extent at watershed scales using Google Earth Engine. Machine learning algorithms were used to classify aerial imagery with additional spectral indices to extract potential wetland inundation areas, which were further refined using LiDAR-derived landform depressions. The wetland delineation results were then compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and existing global-scale surface water products to evaluate the performance of the proposed method. We tested the workflow on 26 watersheds with a total area of 16,576 km2 in the Prairie Pothole Region. The results showed that the proposed method can not only delineate current wetland inundation status but also demonstrate wetland hydrological dynamics, such as wetland coalescence through fill-spill hydrological processes. Our automated algorithm provides a practical, reproducible, and scalable framework, which can be easily adapted to delineate wetland inundation dynamics at broad geographic scales.

Downscaling MODIS-derived water maps with high-precision topographic data in a shallow lake

ABSTRACTRemotely sensed imagery is the most efficient and widely used data source to monitor the water area changes. However, a trade-off always exists between temporal resolution and spatial resolution for satellite images. Taking the southern Dongting Lake as an example, this study was conducted to develop a method of downscaling the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived coarse spatial resolution water maps in shallow lakes with high-precision digital elevation model. The main principle of the method is to identify and adjust the horizontal location errors of the waterlines extracted from coarse-resolution data by analysing and modifying the elevation leaps using finer-scale topography information. Moving average filter was used to smooth the errors of waterlines caused by the geometric inaccuracies and classification uncertainties of the coarse data. The optimal local window size of the moving average filter was selected automatically using an exponential decay function model and a curvature algorithm for each pixel in the waterlines. In reference to Landsat Thematic Mapper data, the accuracy of the downscaling result is distinctly higher than that of the original MODIS normalized difference water index-derived water maps. The presented method is proved to be an effective tool for acquiring water maps of shallow lake with high spatio-temporal resolution using coarse- or moderate-resolution satellite imagery and high-precision topographic data.