Resolution Landsat Research Articles

Early Modeling of the Upcoming Landsat Next Constellation for Soybean Yield Prediction Under Varying Levels of Water Availability

The upcoming Landsat Next will provide more frequent land surface observations at higher spatial and spectral resolutions that will greatly benefit the agricultural sector. Early modeling of the upcoming Landsat Next products for soybean yield prediction is essential for long-term satellite monitoring strategies. In this context, this article evaluates the contribution of Landsat Next’s improved spectral resolution for soybean yield prediction under varying levels of water availability. Ground-based hyperspectral data collected over five cropping seasons at the Brazilian Agricultural Research Corporation were resampled to Landsat Next spectral resolution. The spectral dataset (n = 384) was divided into calibration and external validation datasets and investigated using three strategies for soybean yield prediction: (1) using the reflectance from each spectral band; (2) using existing and new vegetation indices developed based on three general equations: Normalized Difference Vegetation Index (NDVI-like), Band Ratio Vegetation Index (RVI-like), and Band Difference Vegetation Index (DVI-like), replacing the traditional spectral bands by all possible combinations between two bands for index calculation; and (3) using a partial least squares regression (PLSR) model composed of all Landsat Next spectral bands, in comparison to PLSR models using Landsat OLI and Sentienel-2 MSI bands. The results show the distribution of the new spectral bands over the most prominent changes in leaf reflectance due to water deficit, particularly in the visible and shortwave infrared spectrum. (1) Band 18 (centered at 1610 nm) had the highest correlation with yield (R2 = 0.34). (2) A new vegetation index, called Normalized Difference Shortwave Vegetation Index (NDSWVI), is proposed and calculated from bands 19 and 20 (centered at 2028 and 2108 nm). NDSWVI showed the best performance (R2 = 0.37) compared to traditional existing and new vegetation indices. (3) The PLSR model gave the best results (R2 = 0.65), outperforming the Landsat OLI and Sentinel-2 MSI sensors. The improved spectral resolution of Landsat Next is expected to contribute to improved crop monitoring, especially for soybean crops in Brazil, increasing the sustainability of the production systems and strengthening food security in Brazil and globally.

Machine Learning Algorithms for Lithological Mapping Using Landsat 9 Data in Central Western Highlands of Yemen

This research designed the lithological units of the Central Western Highlands of Yemen (encompassing parts of Dhamar, Raymah, Sana’a, and northern Ibb) using Landsat 9 imagery. The area's complex geological features, characterized by units of the Yemen Volcanic Group from the Tertiary and Quaternary eras, Tertiary granite intrusions, and limestone, sandstone, metamorphic rocks, and Quaternary deposits, pose challenges for traditional field mapping techniques. By leveraging the spectral resolution of Landsat 9, this study aims to achieve accurate classification and mapping of lithological units. ENVI 5.6 software was used for image processing, applying a supervised classification approach represented by the two most common methods: Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC), based on training samples for each lithological class. The accuracy assessment of the classification was validated through an error matrix. The overall accuracy of SVM reached 85.3% with a Kappa coefficient of 0.8, while the overall accuracy of MLC reached 83.3% with a Kappa coefficient of 0.8, indicating a high degree of consistency and reliability in the classification process. This signifies a highly reliable classification outcome. The findings of this study highlight the significant advantages of utilizing Landsat 9 for detailed geological mapping of complex terrains, demonstrating a notable improvement in efficiency and accuracy over traditional methodologies. It can be relied upon to classify lithological units in other areas.

Quantification of Green Carbon Changes in Relation with LULC Changes using Remote Sensing - GIS Technology in Kuantan River Basin

Land use land cover changes (LULCC) is a process that is influenced by anthropogenic effect. The growth of population is increasing, which leads to a demand on metropolitan regions. However, these urbanization and development are significant contributors to the expansion of LULCC conflict at an alarming rate to fulfil the human needs. LULCC has derived huge effects towards the environment which is biomass and green carbon changes. LULCC has caused vegetation covers of the land being degraded which reducing the total biomass of the area and implemented to that, the carbon stock for the area is increasing causing climate change and global warming. Therefore, the estimation of total carbon is important to be conducted. The existing map of LULCC is not update, the inventories information about the total carbon in Kuantan is not well covered, people in this region are lack of awareness about the harmful of carbon in the air, and conventional method is hard to be applied for carbon estimation are the problems that have derived for this study to be conducted. This study is focused on quantification of total carbon in Kuantan district to raise the awareness towards the local communities about the danger of high amount of carbon. The objective of this study is to classify LULC types in Kuantan district using geospatial artificial intelligent (GeoAI) approach on satellite images, to relate soil types, vegetation index and satellite – based total biomass in Kuantan River basin and to map green carbon changes in Kuantan based on quantified biomass using remote sensing – GIS technology. Therefore, this study is focused on Kuantan district to quantify the total biomass and carbon in this area by using selected multispectral satellites images with high spatial resolution Landsat 8 OLI, and Landsat 5 TM.

A dynamic surface water extent service for Africa developed through continental-scale collaboration

Spatially explicit, near real time information on surface water dynamics is critical for understanding changes in water resources, and for long-term water security planning. The distribution of surface water across the African continent since 1984 and updated as every new Landsat scene becomes available is presented here, and validated for the continent for the first time. We applied the Water Observations from Space (WOfS) algorithm, developed and well-tested in Australia, to every Landsat scene acquired over Africa since the mid 1980s to provide spatial information on surface water dynamics over the past 30+ years. We assessed the accuracy of WOfS using aerial and satellite imagery. Four regional geospatial organisations, coordinated through the Digital Earth Africa Product Development Task Team, conducted the validation campaign and provided both the regional expertise and experience required for a continental-scale validation effort. We assessed whether the point was wet, dry, or cloud covered, for each of the 12 months in 2018, resulting in 34,800 labelled observations. As waterbodies larger than 100 km2 are easy to identify with Landsat resolution data and can thus boost accuracy, these were masked out. The resulting overall accuracy of the water classification was 82%. WOfS in Africa is expected to be used by ministries and departments of agriculture and water across the continent, by international organisations, academia, and the private sector. A large-scale collaborative effort, which included regional and technical skills spanning two continents was required to create a service that is regionally accurate and is both hosted on, and implemented operationally from, the African continent.

Multi-mission virtual monitoring station for streamflow monitoring and hydrodynamic model calibration

A comprehensive understanding of the streamflow dynamic along the river system is one of the significant components for preserving biodiversity and ensuring sustainable ecological processes. Understanding this requirement, streamflow estimation using remote sensing (RS) has evolved as an alternate approach in the hydrologic literature during the last decade due to its extensive spatiotemporal coverage. However, the existing RS-based approaches have limited applications for deriving the continuous time series in tropical river reaches due to narrow water widths during the low flow and frequent cloud covers during the high flow periods. Therefore, this study proposed frameworks to establish a virtual monitoring station (VMS) where multi-mission satellites are being used to derive continuous streamflow time series. From the multi-mission satellites, the optical RS images are used to develop a Copula-based fusion (CFUS) model by integrating the Frank copula with the 30m × 1-day resolution synthetic Landsat images, derived from the fusion of 250m × 1-day resolution MODIS images and 30m × 16-days resolution Landsat images; whereas the water levels are retrieved from the altimeters to derive discharge using the rating curve. Additionally, the potential utility of the established VMS for hydrodynamic model (MIKE11-NAM-HD) calibration under data-scarce conditions is also demonstrated. Finally, a coupled RS-hydrodynamic (RS-HD) framework is also proposed to establish VMS both at semi-gauged and ungauged sections of the rivers. The efficacy of the aforementioned framework was tested along the lower Brahmani river reach. The results reveal that the advocated framework could perform satisfactorily with the Nash-Sutcliffe efficiency (NSE) and Kling-Gupta efficiency (KGE) ≥0.8. This approach has the potential to be upscaled to other river reaches as one of the next-generation hydrometry for daily streamflow monitoring using high-frequent observation of multi-mission satellites.

Extending Canadian forest disturbance history maps prior to 1985

AbstractAn accurate depiction of wildfire, harvesting, and insect outbreak disturbances is essential for sustainable ecosystem management of forests in Canada. Even though the advent of temporally consistent 30‐m resolution Landsat data has enabled the detailed mapping of forest disturbances in Canada from 1985 onward, the disturbance record prior to 1985 remains sparse. This study aimed to extend the existing pre‐1985 disturbance history record by mapping wildfire, harvest, and insect outbreaks in Canadian forests between 1965 and 1984. Our geospatial data processing methodology relied on multilayer perceptrons (MLP) trained on spectral recovery signatures to map and age these disturbances. Our model detected approximately 4.8, 7.3, and 3.8 million ha of burnt, harvested, and insect‐ravaged forest areas, respectively, that were absent from national and provincial disturbance databases and forest inventories. Results were validated using both internal and external validation datasets. Our disturbance detection methodology was highly effective, with an internal validation kappa score of 0.91 and an external score of 0.81. The fire and harvest age disturbance MLPs, whose predictions can also be used as a proxy of forest stand age, performed adequately on the internal (fire R2 = 0.675; root mean squared error [RMSE] = 4.42; harvest R2 = 0.723; RMSE = 3.17) and external validation datasets (fire R2 = 0.242; RMSE = 4.69; harvest R2 = 0.257; RMSE = 5.46), outperforming existing forest age disturbance products. Finally, we relied on several open data products, such as provincial forest inventories, to correct our disturbance type and year prediction whenever these more reliable, but incomplete, data sources were available. Specific years were not assigned to insect outbreaks due to the lack of dependable training and validation data. We also illustrate how extending the existing forest disturbance record by 20 years may provide a more in‐depth understanding of landscape‐disturbance dynamics with a case study of the 2023 Canadian wildfire season.

A long-term (1984–2021) wetland classification dataset for the Yangtze River Basin from continuous Landsat image collections

The wetlands along the Yangtze River Basin (YRB) have provided a variety of ecological and economic benefits. However, the lack of a long-term wetland classification dataset with comprehensive wetland categories has created in a barrier to evaluating the long-term variations of YRB wetlands for their habitat health, carbon storage, greenhouse gas emissions, and ecosystem service capacity. Therefore, this study aimed to generate a wetland classification dataset for the YRB covering the time period from 1984 to 2021. The dataset named Long-Term Wetland Classification Dataset for YRB (LTWCD_YRB) was created using a Random Forest machine learning classifier on Google Earth Engine with 30 m resolution Landsat 5, 7, 8 muti-spectral images. The maps of LTWCD_YRB demonstrated the spatial distribution, annual variability, and seasonal cycle of nine wetland categories in the extent, and the total validation accuracy can reach 85 %. The LTWCD_YRB indicates that the total wetland area of the YRB in 2021 was larger than that in 1984, with a constant increase in human-made wetlands and fluctuating natural wetlands. Aquaculture ponds expanded the most by 4,987 km2, while inland marshes in the source region exhibited the most fluctuations. Seasonal changes in wetlands were prominent in the Poyang Lake Basin, Dongting Lake Basin, and YRB source region. Human activities were found to be more dominant than natural driving forces in affecting wetlands. The LTWCD_YRB offers a consistent agreement of wetland area variations with the other satellite-based wetland datasets in the YRB, making it valuable for researchers, policymakers, and stakeholders to better understand the YRB wetlands and to support sustainable wetland management practices.

Three-dimensional dynamic monitoring of crevasses based on deep learning and surface elevation reconstruction methods

The evolution of crevasses on ice shelves plays a pivotal role in maintaining their stability. Investigating the developmental pattern of crevasses on ice shelf surfaces necessitates long-term dynamic monitoring, high-precision automatic extraction algorithms, and quantitative analysis tools. Accordingly, based on characteristics such as the long span of crevasses and their meandering distribution, we proposed Strip_Unet for the identification of crevasses. Within this, the Strip_block module utilizes strip convolution in conjunction with traditional convolution to capture the contextual information from the surrounding area, and the ASPP module is used to enhance the receptive field. Leveraging this model, we successfully identified crevasses within the damaged area of the Amery Ice Shelf from 2003 through 2023, utilizing 15-meter resolution Landsat imagery. Subsequently, we retrieved the depth details of these crevasses by applying ATL06 data, allowing us to quantitatively assess the evolution of crevasses. Our study represents the first investigation into the development of the damage area on the Amery Ice Shelf through long-term time-series analysis. Our findings indicate that crevasses originate from specific areas and follow recurring propagation cycles, exhibiting a consistent annual increase in total length. Additionally, changes in the width of these crevasses coincide with alterations in their depth. Notably, the downstream regions of the damaged area demonstrate higher crevasses density and the fastest movement speed. The proposed automatic identification of crevasses and three-dimensional dynamic monitoring hold significant potential for application throughout the Antarctic region, providing valuable insights into the long-term stability of ice shelves.

Tropical Peatland Water Table Estimations From Space

AbstractTropical peatlands store copious amounts of carbon (C) and play a critical role in the global C cycle. However, this C store is vulnerable to natural and anthropogenic disturbances, leading these ecosystems to become weaker C sinks or even net C sources. Variabilities in water table (WT) greatly influence the magnitude of greenhouse gas flux in these biomes. Despite its importance in C cycling, observations of the spatiotemporal dynamics of tropical peatland WT are limited in spatial extent and length. Here, we use in situ WT measurements from tropical peatlands in Indonesia, Malaysia, and Peru to evaluate the satellite‐based Optical Trapezoid Model (OPTRAM). The model uses the pixel distribution in the shortwave infrared transformed reflectance and normalized difference vegetation index (NDVI) space to calculate indices that are then compared against in situ WT data. 30‐m resolution Landsat 7 and Landsat 8 images were utilized for model parameterization. We found OPTRAM to best capture tropical peatland WT dynamics in minimally forested and non‐forested areas (low to intermediate NDVI) (0.7 < R < 1) using the “best pixel” approach (the pixel with the highest Pearson‐R correlation value). In areas with relatively higher NDVI, OPTRAM index did not correlate with WT (average R of −0.04 to 0.24), likely due to trees being less sensitive to WT fluctuations. OPTRAM shows potential for reliably estimating tropical peatland WT without the need for direct measurements, which is challenging due to site remoteness and harsh conditions.

Remote sensing monitoring of changes in forest cover in the Volyn region: a cross section for the first two decades of the 21st century

The aim of the article. This article highlights the significance of forest cover as an important indicator of the state of the environment. It discusses the findings of the Food and Agriculture Organization of the United Nations (FAO) Forest Resources Assessment (FRA) 2020 report, which states that the world's forest area has decreased by 178 million hectares since 1990. The case study of Volyn region shows how cloud processing and vegetation classification can help quantify forest dynamics from 2000 to 2020, allowing local authorities and decision makers to monitor and analyze trends in near real time. Overall, this work provides insights into the importance of monitoring forest dynamics and the potential for remote sensing technology to facilitate this process. Data & Methods. Remote sensing is an effective tool for monitoring forest ecology and management, and Google Earth Engine (GEE) is an online platform that combines data from various agencies to analyze environmental data. The article presents a case study of the Volyn region and how cloud processing and vegetation classification were used to assess forest dynamics from 2000 to 2020. The study used data from Landsat 7 Collection 1 Tier 1 composites and the CART algorithm for binary decision tree building. The study was based on information provided by the Main Department of Statistics in the Volyn region on the area of forests and areas where logging was carried out during the specified period. Research results. It is interesting to note that despite the decrease in logging activities, there is an increase in forest cover loss within forest ranges. This could be due to various reasons, such as illegal logging or natural disturbances like fires or disease outbreaks. The use of machine learning methods like CART classification can help to identify and monitor these changes, which can then be used to inform policy decisions and management practices to reduce forest cover loss. In general, in the Volyn region, there is a gradual decrease in the areas where various kinds of logging are carried out from 524 km2 in 2003 to 239 km2 in 2020. In contrast, forest cover loss within forest ranges increased rapidly from 37.85 km2 in 2015 to 84.01 km2 in 2017 and beyond from 5.53 km2 to 10.80 km2 in 2015 and 2017 respectively. In this study, the accuracy assessment was performed using 30% of the control points obtained initially, based on data on the reliability of the land cover. The manufacturer's accuracy and user accuracy were calculated to evaluate error omissions and possibilities of a pixel being categorized in a certain category. The spatial resolution of Landsat 7 data used in this study was 30 m, with a minimum calculation area of 0.337 hectares. The overall accuracy and the coefficient κ are the most representative measures of accuracy, with an average accuracy of classification of OAav=98.82% and κav=0.9764.

Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation

Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes.

Large multi-decade beaver ponding changes in the subarctic Hudson Bay Lowlands, Canada observed using satellite remote sensing

Beavers strongly impact hydrology and ecosystems through their widespread dam building that creates ponds and wetlands. Monitoring the relative abundance of beavers and their waterbodies is needed to assess these effects and factors influencing population levels. However, the ability to do this over vast, remote regions is limited with conventional aerial or field-based surveying. To address this challenge, we developed a satellite remote sensing method to track beaver ponding changes over multiple decades and applied it to a 5127 km2 region of the coastal Hudson Bay Lowlands in Manitoba, Canada. Annual, sub-pixel surface water mapping using 30 m resolution Landsat satellite data, combined with a spatial database of beaver dams, permitted the mapping of 37 year (1985–2021) beaver ponding dynamics. We identified 1714 beaver dams and 1085 beaver pond complexes covering 31 km2, indicating that beavers have an important influence on stream hydrology in this high subarctic landscape. The total area of ponding decreased by 53% from 1986–1989 and by 80% by 1995, and then gradually recovered to initial levels by 2015. The early, steep drop in beaver ponding corresponded to a 13% decline in regional surface water area, while a similar wetness decline during 2015–2018 resulted in little change in beaver ponding. We suggest that strong beaver ponding dynamics were likely caused by the interaction between streamflow levels and beaver populations living near their northern range limit and cold tolerance. The pond mapping method can be applied to other regions if the long-term distribution of beaver dams is known, and ponds are large enough to be identified using a Landsat sub-pixel approach.

Efficient Representation Learning of Satellite Image Time Series and Their Fusion for Spatiotemporal Applications

Satellite data bolstered by their increasing accessibility is leading to many endeavors of automated monitoring of the earth's surface for various applications. Such applications demand high spatial resolution images at a temporal resolution of a few days which entails the challenge of processing a huge volume of image time series data. To overcome this computing bottleneck, we present PatchNet, a bespoke adaptation of beam search and attention mechanism. PatchNet is an automated patch selection neural network that requires only a partial spatial traversal of an image time series and yet achieves impressive results. Satellite systems face a trade-off between spatial and temporal resolutions due to budget/technical constraints e.g., Landsat-8/9 or Sentinel-2 have high spatial resolution whereas, MODIS has high temporal resolution. To deal with the limitation of coarse temporal resolution, we propose FuSITSNet, a twofold feature-based generic fusion model with multimodal learning in a contrastive setting. It produces a learned representation after fusion of two satellite image time series leveraging finer spatial resolution of Landsat and finer temporal resolution of MODIS. The patch alignment module of FuSITSNet aligns the PatchNet processed patches of Landsat-8 with the corresponding MODIS regions to incorporate its finer resolution temporal features. The untraversed patches are handled by the cross-modality attention which highlights additional hot spot features from the two modalities. We conduct extensive experiments on more than 2000 counties of US for crop yield, snow cover, and solar energy prediction and show that even one-fourth spatial processing of image time series produces state-of-the-art results. FuSITSNet outperforms the predictions of single modality and data obtained using existing generative fusion models and allows for monitoring of dynamic phenomena using freely accessible images, thereby unlocking new opportunities.

Mapping proglacial headwater streams in High Mountain Asia using PlanetScope imagery

Headwater streams transport nutrients, sediment, and mineral-rich groundwater downstream. In High Mountain Asia (HMA), headwater streams also funnel glacier and snow melt to sustain continuous water supply for the downstream region. These channels remain poorly mapped because of their inaccessibility and because they are smaller than the resolution of Landsat (30 m) and Sentinel-2 (10 m). In this study, we assessed the ability of 3 m resolution PlanetScope imagery to detect the proglacial headwaters downstream of all high-altitude glaciers larger than 5 km2 in HMA. We created 3000 manually labeled image tiles to train and evaluate computer vision (CV) against techniques common in the hydrologic remote sensing literature, specifically normalized difference water index (NDWI) thresholding and random forests (RF). Results indicate that CV best detects the headwater streams with >0.60 F1-scores, nearly 0.20 points higher than RF and 0.45 points higher than thresholding. We also assessed how errors in CV propagate to derived hydrologic information, exemplified by the biogeochemically critical measurement of stream surface area. We found that CV classifications produced surface areas with 0.98 R2, 0.01 km2 MAE, and 0.02 km2 RMSE against manually labeled surface areas. We also observed the best CV performance during the spring season with 30% more skillful classification performance than in summer and fall. Our results prove the ability of PlanetScope imagery to detect and map headwater streams accurately and at scale, and that classification errors stemming from the imagery or the CV methods do not greatly impair our ability to quantify stream surface area meaningful for biogeochemical exchange and hydrology studies.

Cropland abandonment between 1986 and 2018 across the United States: spatiotemporal patterns and current land uses

Knowing where and when croplands have been abandoned or otherwise removed from cultivation is fundamental to evaluating future uses of these areas, e.g. as sites for ecological restoration, recultivation, bioenergy production, or other uses. However, large uncertainties remain about the location and time of cropland abandonment and how this process and the availability of associated lands vary spatially and temporally across the United States. Here, we present a nationwide, 30 m resolution map of croplands abandoned throughout the period of 1986–2018 for the conterminous United States (CONUS). We mapped the location and time of abandonment from annual cropland layers we created in Google Earth Engine from 30 m resolution Landsat imagery using an automated classification method and training data from the U.S. Department of Agriculture Cropland Data Layer. Our abandonment map has overall accuracies of 0.91 and 0.65 for the location and time of abandonment, respectively. From 1986 to 2018, 12.3 (±2.87) million hectares (Mha) of croplands were abandoned across CONUS, with areas of greatest change over the Ogallala Aquifer, the southern Mississippi Alluvial Plain, the Atlantic Coast, North Dakota, northern Montana, and eastern Washington state. The average annual nationwide abandoned area across our study period was 0.51 Mha per year. Annual abandonment peaked between 1997 and 1999 at a rate of 0.63 Mha year−1, followed by a continuous decrease to 0.41 Mha year−1 in 2009–2011. Among the abandoned croplands, 53% (6.5 Mha) changed to grassland and pasture, 18.6% (2.28 Mha) to shrubland and forest, 8.4% (1.03 Mha) to wetlands, and 4.6% (0.56 Mha) to non-vegetated lands. Of the areas that we mapped as abandoned, 19.6% (2.41 Mha) were enrolled in the Conservation Reserve Program as of 2020. Our new map highlights the long-term dynamic nature of agricultural land use and its relation to various competitive pressures and land use policies in the United States.

Validating Landsat Analysis Ready Data for Nearshore Sea Surface Temperature Monitoring in the Northeast Pacific

In the face of global ocean warming, monitoring essential climate variables from space is necessary for understanding regional trends in ocean dynamics and their subsequent impacts on ecosystem health. Analysis Ready Data (ARD), being preprocessed satellite-derived products such as Sea Surface Temperature (SST), allow for easy synoptic analysis of temperature conditions given the consideration of regional biases within a dynamic range. This is especially true for SST retrieval in thermally complex coastal zones. In this study, we assessed the accuracy of 30 m resolution Landsat ARD Surface Temperature products to measure nearshore SST, derived from Landsat 8 TIRS, Landsat 7 ETM+, and Landsat 5 TM thermal bands over a 37-year period (1984–2021). We used in situ lighthouse and buoy matchup data provided by Fisheries and Oceans Canada (DFO). Excellent agreement (R2 of 0.94) was found between Landsat and spring/summer in situ SST at the farshore buoy site (>10 km from the coast), with a Landsat mean bias (root mean square error) of 0.12 °C (0.95 °C) and a general pattern of SST underestimation by Landsat 5 of −0.28 °C (0.96 °C) and overestimation by Landsat 8 of 0.65 °C (0.98 °C). Spring/summer nearshore matchups revealed the best Landsat mean bias (root mean square error) of −0.57 °C (1.75 °C) at 90–180 m from the coast for ocean temperatures between 5 °C and 25 °C. Overall, the nearshore image sampling distance recommended in this manuscript seeks to capture true SST as close as possible to the coastal margin—and the critical habitats of interest—while minimizing the impacts of pixel mixing and adjacent land emissivity on satellite-derived SST.

Assessing Shoreline Changes in Fringing Salt Marshes from Satellite Remote Sensing Data

Salt marshes are highly important wetlands; however, external pressures are causing their widespread deterioration and loss. Continuous monitoring of their extent is paramount for the preservation and recovery of deteriorated and threatened salt marshes. In general, moderate-resolution satellite remote sensing data allow for the accurate detection of salt marsh shorelines; however, their detection in narrow and fringing salt marshes remains challenging. This study aims to evaluate the ability of Landsat-5 (TM), Landsat-7 (ETM+), and Sentinel-2 (MSI) data to be used to accurately determine the shoreline of narrow and fringing salt marshes, focusing on three regions of the Aveiro lagoon in Mira, Ílhavo and S. Jacinto channels. Shorelines were determined considering the Normalized Difference Vegetation Index (NDVI), and the accuracy of this methodology was evaluated against reference shorelines by computing the Root Mean Square Error (RMSE). Once validated, the method was used to determine historical salt marsh shorelines, and rates of change between 1984 and 2022 were quantified and analyzed in the three locations. Results evidence that the 30 m resolution Landsat data accurately describe the salt marsh shoreline (RMSE~15 m) and that the accuracy is maintained when increasing the spatial resolution through pan-sharpening or when using 10 m resolution Sentinel-2 (MSI) data. These also show that the salt marshes of the Ílhavo and S. Jacinto channels evolved similarly, with salt marsh shoreline stability before 2000 followed by retreats after this year. At the end of the four decades of study, an average retreat of 66.23 ± 1.03 m and 46.62 ± 0.83 m was found, respectively. In contrast to these salt marshes and to the expected evolution, the salt marsh of the Mira Channel showed retreats before 2000, followed by similar progressions after this year, resulting in an average 2.33 ± 1.18 m advance until 2022.

Extraction of the bare rock rate in counties of typical karst areas based on unmanned aerial vehicle images and satellite data, China

AbstractRocky desertification is a land degradation process. The bare rock rate (BRR) is an important index. Satellite‐based methods for BRR estimation can generate BRR datasets, but the spatial resolution and accuracy are low. Unmanned aerial vehicle (UAV) data can be used for BRR extraction with much higher resolution and higher accuracy, but the data are laborious and costly to collect. This study proposed a stepwise upscaling method employing the UAV‐extracted BRR as the ground truth to establish a multiband regression model and calibrated the BRR derived from 30‐m resolution LANDSAT 8 data, which was then used to calibrate 500‐m resolution moderate resolution imaging spectroradiometer (MODIS) data. We compared the results with the satellite‐based BRR and the results of UAV‐MODIS upscaling methods. The results showed that (1) the UAV provides high‐resolution data, and exposed rocks can be efficiently extracted from UAV images. This method could replace ground surveys. (2) The accuracy of the stepwise upscaling method was higher than that of the nonstepwise upscaling method in large areas. R2 increased by 64.9%, and the root mean square error (RMSE) decreased by 82.9%. (3) Compared with that of the satellite‐based BRR, the accuracy of the multiband regression model‐extracted BRR was higher. R2 increased by 21.5%, and the RMSE decreased by 62.2%. This study demonstrated that the stepwise upscaling method and multiband regression can be combined to extract high‐precision and large‐scale BRR data, which can be used to serve ecological engineering monitoring and ecological governance.

Detecting the long-term spatiotemporal crop phenology changes in a highly fragmented agricultural landscape

Trends in crop phenometrics reveal the influence of climate variability and change on crop growth and development. However, the trends are less clear in fragmented tropical smallholder landscapes, because there is high spatial and temporal variability in crop phenology. Frequent historical and high spatial resolution (≤30 m) Earth observations are needed to track changes in crop phenology in fragmented landscapes but are often unavailable. The spatial–temporal gap can be closed by integrating infrequent high spatial resolution Earth observations with low spatial/high temporal resolution observations through data fusion. We fused 30 m resolution Landsat and 250 m resolution MODIS imagery to investigate trends in crop phenology from 2000 to 2020 in a fragmented agricultural landscape of Ethiopia. We used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) that had recently been modified for application in fragmented agricultural landscapes. We used the non-parametric Mann–Kendall test for crop phenology trend analysis. Crop phenology based on Landsat–MODIS fusion was compared to MODIS-based crop phenology without fusion. We found data fusion yielded a smaller magnitude of changes in the start of season (SOS: -0.2-day-y−1) and end of season (EOS: -0.50-day-y−1) compared to MODIS SOS (-0.5-day-y−1) and EOS (1.38-day-y−1) due to MODIS-related mixing with the surrounding natural vegetation in the fragmented agricultural landscape. EOS showed a faster rate of change compared to SOS over the 21-years. The Landsat and MODIS fusion captured spatial variation in the timing and magnitude of change specific to crops and their growing environment, which has implications for adaptation strategies. Our results highlight the importance of long-term data fusion to improve the spatial dimension of crop phenology time series analysis. Integrating time series land cover maps into the data fusion processing chain could further improve long-term data fusion for crop phenology trend analysis.

Integrating active fire behavior observations and multitemporal airborne laser scanning data to quantify fire impacts on tree growth: A pilot study in mature Pinus ponderosa stands

Methods that integrate pre-, active-, and post-fire measurements to quantify fire effects across multiple spatial scales are needed to improve our understanding of ecological effects following fire and for informing natural resource management decisions that rely on post-fire growth and yield estimates. Given growth and yield modeling systems require tree level measurements to parameterize diameter and height distributions, effective datasets require both tree and stand level characterization. However, most stand-to-landscape scale fire effects studies use optical multispectral data (e.g., 30 m spatial resolution Landsat data) which are too coarse to quantify tree-level effects and is limited in its ability to quantify changes in forest structure. Most studies also fail to integrate active fire behavior observations, such as heat flux, limiting their ability to identify mechanisms of tree injury and mortality and/or predict fire effects. Combining active fire observations and structural measurements derived from multitemporal airborne laser scanning (ALS) data has been proposed to quantify fire effects on tree structure and growth but has yet to be tested. In this pilot study, we used a combination of fire behavior and heat flux metrics, including Fire Radiative Power per unit area (FRP: W m−2) and Fire Radiative Energy per unit area (FRE: J m−2), along with multitemporal field and ALS measurements to quantify fire intensity impacts on mature tree height growth. Prescribed fires were conducted in 2014 in thinned and unthinned mature Pinus ponderosa stands and plot-scale fire behavior and heat flux metrics were quantified using standard videography methods and tower-mounted infrared radiometers. Tree height growth was quantified using multitemporal field and ALS data and included pre-fire measurements and post-fire measurements up to eight years post-fire. Results show that trees exposed to the surface fire treatments had height growth that was less than unburned trees. The results also show that height growth 5–8 years post-fire is reduced in trees exposed to greater fire intensities, in terms of maximum FRP per unit area and rate of spread. There was no significant relationship between height growth and other fire behavior metrics (FRE per unit area, average flame length, flame residence time), although height growth decreased with greater FRE per unit area and increased with greater flame residence time. These findings, taken together with similar sapling-, mature tree- and landscape-scale studies, suggest that an integrated active-fire behavior and ALS-data approach may provide a quantitative, scalable method for assessing fire effects on tree structure and growth.