Landsat Scenes Research Articles

Application of remote sensing and GIS techniques for monitoring water volume variations in inaccessible reservoirs

ABSTRACT Change detection processes using remote sensing and Geographic Information System (GIS) techniques were applied to monitor and estimate the water volume in the Mosul Dam reservoir during a period in which it was inaccessible due to ISIS occupation of the Province of Mosul. A total of 17 scenes of LANDSAT 8 images have been captured and the calculated areas have been used to estimate the water level (WL) and water volume (V) in the reservoir using the volume–area–elevation curve that was developed in 2011. The results indicated that WL ranged between a minimum of 308.5 m and a maximum of 321 m, while V ranged between 3.95 and 6.8 km3 for the minimum and maximum WL, respectively. For the blackout period with missing data, the monthly reservoir inflows (RIs) from the drainage watershed and the mean inflows were calculated and found to be compatible with the mean inflows of Mosul reservoir for the years 1986–2011.

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.

A Surface Water Extraction Method Integrating Spectral and Temporal Characteristics

Remote sensing has been applied to observe large areas of surface water to obtain higher-resolution and long-term continuous observation records of surface water. However, limitations remain in the detection of large-scale and multi-temporal surface water mainly due to the high variability in water surface signatures in space and time. In this study, we developed a surface water remote sensing information extraction model that integrates spectral and temporal characteristics to extract surface water from multi-dimensional data of long-term Landsat scenes to explore the spatiotemporal changes in surface water over decades. The goal is to extract open water in vegetation, clouds, terrain shadows, and other land cover backgrounds from medium-resolution remote sensing images. The average overall accuracy and average kappa coefficient of the classification were verified to be 0.91 and 0.81, respectively. Experiments applied to China’s inland arid area have shown that the method is effective under complex surface environmental conditions.

Seeing the floods through the trees: Using adaptive shortwave infrared thresholds to map inundation under wooded wetlands

AbstractAccurate information about the extent, frequency and duration of forest inundation is required to inform ecological, biophysical and hydrological models and enables floodplain managers to quantify the efficacy of flood mitigation/modification activities. Open water classifiers derived from optical remote sensing typically underestimate or fail to detect floodplain forest inundation. This paper presents a new method for detecting forest inundation dynamics using freely available Landsat and Sentinel 2 data, referred to as short‐wave infrared mapping under vegetation. The method uses a dynamic threshold that accounts for the additional shortwave infrared reflectance caused by the presence of tree canopies over floodwater. The method is demonstrated at five Ramsar listed River Red Gum floodplain forest wetlands in southeastern Australia. Accuracy assessment based on independent drone imagery from a wide range of vegetated wetlands showed an absolute accuracy of 67%–70% and a fuzzy accuracy of 81%–83%. We found the method is conservative, and underestimates inundation (16%–18%) but very rarely misclassifies dry pixels as inundated (0.3%–0.6%). When compared to river gauge data, the method shows similar trends to an open water classifier (i.e., the area of inundated vegetation increases with increasing river height). The method is conservative compared to lidar‐based floodplain inundation models but can be applied wherever cloud‐free scenes of Landsat or Sentinel 2 have been acquired, thereby enabling floodplain managers with the ability to quantify changes in inundation dynamics in places/time‐periods where lidar is unavailable.

Effect of intra-year Landsat scene availability in land cover land use classification in the conterminous United States using deep neural networks

The Landsat archive having consistent revisit times, near global extent and extensive multi-decadal temporal coverage offers a unique opportunity for land cover land use product generation. Along with this vast volume of freely available data, new classification methods based on deep learning have improved modeling capabilities. This manuscript investigates the effect of intra-annual Landsat scene availability in the accuracy of land cover land use classification in the conterminous United States. More specifically, we seek to quantify the effect of: i) increased monthly scene availability, and ii) specific months that may result in higher classification accuracy across different classes. Identifying specific months with comparable classification accuracy to the entire time series could offer significant computational gains for large-scale mapping. Our experiment incorporated deep learning classifiers and a wide range of reference data across the continental United States. Results were contrasted between five large U.S. climatic regions to further differentiate this intra-annual effect. Our findings indicate that the total number of months can have a highly variable effect in the classification accuracy ranging from minor (a few percentage points in terms of class F1 accuracy) to extremely beneficial (approaching 50% F1 improvement moving from four to twelve month observations). The benefit of increased month observations varied among climatic regions and classes: when all climate regions were combined, the grass/shrub and cultivated classes improved their F1 accuracy up to 30%, while the water class saw the least improvement of about 5%, partially due to its limited room for improvement. The effect of specific month combinations was also examined, where the total number of months was kept constant and the included months varied. The difference between the best month combination and the median combination value was estimated to be as high as about 30% for the four monthly observations scenario and the grass/shrub class. Further validation of the month selection importance comes from an example implementation scenario where F1 improvements can be as high as 10%. Our work demonstrated that month selection may offer such benefits that in some classes and climatic regions this time selection optimization is an inevitable choice due to large accuracy improvements. Also, the potential data reduction with targeted month selection would be particularly appealing to large-scale classification tasks. Due to the large extent of the climatic regions further studies are needed to quantify a more localized effect along with explanation of potential drivers.

Assessing forest cover changes in Dak Lak province (Central Highlands of Vietnam) from multi-temporal Landsat data and machine learning techniques

Dak Lak is a province in the Central Highlands region of Vietnam, with a large area of forests and forestry land. However, the forest cover has changed dramatically in recent times due to the influence of human activities and climate change. This article presents the results of assessing forest cover changes in Dak Lak province from Landsat satellite image data for the period 2000 – 2020. Three Landsat satellite image scenes, including Landsat 5 TM images taken in March 2000 and February 2010 and Landsat 8 OLI image taken in February 2020 are used to classify forest cover. Three common machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART) and the traditional maximum likelihood classification algorithm are used to classify forest cover in the study area, thereby choosing the method with the highest accuracy. The results show that the RF algorithm has the highest accuracy in classifying forest cover from multi-temporal Landsat images by comparing the overall accuracy value and the Kappa coefficient. The obtained results are used to build forest cover change maps in the period 2000 - 2010, 2010 - 2020 and 2000 - 2020. The results received in the study provide information to help managers in monitoring and protecting forest resources.

Long-term Change of Water Transparency in Lake Singkarak Observed from Remote Sensing Data

The water quality data for supporting lake management in Indonesia are very limited due to financial constraints. Satellite data has a big potential as a source for providing data to retrieve water quality information. In this study, we used Landsat TM/ETM+ remote sensing data and applied an empirical model for estimating the water transparency (Zsd) to represent water quality in Lake Singkarak. We used 230 scenes of pre-processed Landsat TM/ETM+ images to generate a long-term SD database for Lake Singkarak from 1987 to 2020. The visual and statistical analysis shows the change of Zsd in Lake Singkarak. In the period before 2000, the Zsd were generally lower than 2 meters (m). From 2000 to 2005 the Zsd continuously increased from 1.5m to 4m. Lastly, from 2005 until 2020 Zsd were steady: 2m > Zsd < 4m. The satellite-based Zsd estimations captured the three regimes of water quality in Lake Singkarak. These estimations will thus provide useful data for lake managers and policymakers.

Agricultural water accounting: Complementing a governance monitoring schema with remote sensing calculations at different scales

Water use requires monitoring and quantification at different spatial scales to enhance water security, especially in regions facing water scarcity and threats to food security. Consequently, water metering has been implemented in various countries as part of water governance frameworks. This study aims to evaluate the implementation of a water metering network within the Chilean water governance system, which is based on the commoditisation of water through water rights. Additionally, it assesses the potential of supplementing the water metering network with remote sensing-based estimates of actual evapotranspiration (AET) and discusses the need to integrate these estimates into an appropriate water governance scheme. To conduct this study, publicly available water use reports were obtained from the Water Resources Directorate and subsequently processed to eliminate anomalies in the withdrawal time series. Water withdrawal data was supplemented with information on granted water rights to provide additional insights and contrast water allocations with actual withdrawals. AET estimates from the Mapping EvapoTranspiration at high Resolution with Internalised Calibration (METRIC) model using Landsat scenes were also acquired for the period from 2019 to 2022 to compare withdrawals and water demand in the agricultural sector. It was found that only a small fraction of water rights ( ∼ 2%) is currently being metered. Actual reported withdrawals, on average, amount to approximately one fifth to one fourth of the volumes granted through water rights. However, water extractions vary depending on geographical locations and usage categories. Remote sensing-based AET demonstrates a good correlation with withdrawals, suggesting its potential in auditing water withdrawal records provided by water users and calculating water availability and withdrawals at aggregated scales within an adaptive water governance framework. While different applications were explored within the Chilean context, these have a broader application in global water governance, particularly in regions experiencing similar challenges in water resource management.

DEVELOPING A MULTI-VARIABLE FOREST FIRE RISK MODEL AND FIRE RISK ZONE MAPPING

Abstract. Forest steppe and taiga regions with a dry climate, small fires occur frequently between large fires, and therefore groups of trees of various ages and structures are formed in these forests, which has the effect of increasing fire exposure and "fuel reserves". The main goal of this research is to develop a multi-variable forest fire risk model and to mapping forest fire risk. The study area is the forest community in Bulgan province. Forest cover maps are essential for current research of community. We used in this research Landsat OLI and Digital Elevation Model (DEM) data. Soil moisture index and land surface temperature data from Landsat satellite data and elevation data (DEM) were used to determine slope and aspect. A multi-variable forest fire risk model was developed in this research. The final output delineated fire risk zones in the study area in four categories that include high-risk, moderate-risk, low-risk, and non-fire risk zones. Accuracy assessments on the two Landsat scenes indicates that forest/non-forest maps derived using the forest index (FI) have high accuracy.

Landsat sub-pixel land cover dynamics in the Brazilian Amazon

The Brazilian Amazon land cover changes rapidly due to anthropogenic and climate drivers. Deforestation and forest disturbances associated with logging and fires, combined with extreme droughts, warmer air, and surface temperatures, have led to high tree mortality and harmful net carbon emissions in this region. Regional attempts to characterize land cover dynamics in this region focused on one or two anthropogenic drivers (i.e., deforestation and forest degradation). Land cover studies have also used a limited temporal scale (i.e., 10–15 years), focusing mainly on global and country-scale forest change. In this study, we propose a novel approach to characterize and measure land cover dynamics in the Amazon biome. First, we defined 10 fundamental land cover classes: forest, flooded forest, shrubland, natural grassland, pastureland, cropland, outcrop, bare and impervious, wetland, and water. Second, we mapped the land cover based on the compositional abundance of Landsat sub-pixel information that makes up these land cover classes: green vegetation (GV), non-photosynthetic vegetation, soil, and shade. Third, we processed all Landsat scenes with &amp;lt;50% cloud cover. Then, we applied a step-wise random forest machine learning algorithm and empirical decision rules to classify intra-annual and annual land cover classes between 1985 and 2022. Finally, we estimated the yearly land cover changes in forested and non-forested ecosystems and characterized the major change drivers. In 2022, forest covered 78.6% (331.9 Mha) of the Amazon biome, with 1.4% of secondary regrowth in more than 5 years. Total herbaceous covered 15.6% of the area, with the majority of pastureland (13.5%) and the remaining natural grassland. Water was the third largest land cover class with 2.4%, followed by cropland (1.2%) and shrubland (0.4%), with 89% overall accuracy. Most of the forest changes were driven by pasture and cropland conversion, and there are signs that climate change is the primary driver of the loss of aquatic ecosystems. Existing carbon emission models disregard the types of land cover changes presented in the studies. The twenty first century requires a more encompassing and integrated approach to monitoring anthropogenic and climate changes in the Amazon biome for better mitigation, adaptation, and conservation policies.

Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS

This study documents the changes in the Land Use/Land Cover (LULC) in the region of saline lakes in north Tunisia, Sahara Desert. Remote sensing data are a valuable data source in monitoring LULC in lacustrine landscapes, because variations in the extent of lakes are visible from space and can be detected on the images. In this study, changes in LULC of the salt pans of Tunisia were evaluated using a series of 12 Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared (TIRS) images. The images were processed with the Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The study area included four salt lakes of north Tunisia in the two regions of the Gulf of Hammamet and Gulf of Gabès: (1) Sebkhet de Sidi el Hani (Sousse Governorate), (2) Sebkha de Moknine (Mahdia Governorate), (3) Sebkhet El Rharra and (4) Sebkhet en Noual (Sfax). A quantitative estimate of the areal extent analysed in this study is 182 km × 185 km for each Landsat scene in two study areas: Gulf of Hammamet and Gulf of Gabès. The images were analysed for the period 2017–2023 on months February, April and July for each year. Spatio-temporal changes in LULC and their climate–environmental driving forces were analysed. The results were interpreted and the highest changes were detected by accuracy assessment, computing the class separability matrices, evaluating the means and standard deviation for each band and plotting the reject probability maps. Multi-temporal changes in LULC classes are reported for each image. The results demonstrated that changes in salt lakes were determined for winter/spring/summer months as detected changes in water/land/salt/sand/vegetation areas. The accuracy of the classified images was evaluated using pixel rejection probability values, which were filtered out using the ‘r.mapcalc’ module of GRASS GIS. The confidence levels were computed and visualised with a series of maps along with the error matrix and measured convergence level of classified pixels. This paper contributes to the environmental monitoring of Tunisian landscapes and analysis of climate effects on LULC in landscapes of north Africa.

Land cover dataset of the China Central-Asia West-Asia Economic Corridor from 1993 to 2018

Land Cover (LC) maps offer vital knowledge for various studies, ranging from sustainable development to climate change. The China Central-Asia West-Asia Economic Corridor region, as a core component of the Belt and Road initiative program, has been experiencing some of the most severe LC change tragedies, such as the Aral Sea crisis and Lake Urmia shrinkage, in recent decades. Therefore, there is a high demand for producing a fine-resolution, spatially-explicit, and long-term LC dataset for this region. However, except China, such dataset for the rest of the region (Kyrgyzstan, Turkmenistan, Kazakhstan, Uzbekistan, Tajikistan, Turkey, and Iran) is currently lacking. Here, we constructed a historical set of six 30-m resolution LC maps between 1993 and 2018 at 5-year time intervals for the seven countries where nearly 200,000 Landsat scenes were classified into nine LC types within Google Earth Engine cloud computing platform. The generated LC maps displayed high accuracies. This publicly available dataset has the potential to be broadly applied in environmental policy and management.

Long-term quantification of pre and post-monsoon surface water area of Bangladesh

Since the 1990s, about 70 percent of the world's wetland ecosystem have been lost, despite the acknowledged contribution of surface waters to human well-being. Surface water is a prerequisite for agriculture, fisheries, and other human activities. It also significantly impacts the groundwater reserve, soil conditions, the environment, and the surrounding area's ecology. Bangladesh's population and economic expansion have increased the need for water resources. Therefore, monitoring the spatial and temporal distribution of surface water resources is essential. The present study used more than 2700 Landsat scenes from 1990 to 2020 to quantify the total surface water area utilizing the strength of cloud computing in the Google Earth Engine platform. Mann-Kendall trend analysis was performed to understand the trend of the surface water area. In 1990 the total pre-monsoon and post-monsoon surface water areas were 7994.91 km2 and 11453.43 km2 which is 5.38% and 7.71% of the total countries area, respectively. In 2010 and 2020, the water area was 10748.61 km2 (7.24%) and 10313.49 km2 (6.94%), respectively, in the pre-monsoon; and 10794.05 km2 (7.27%) and 15570.36 km2 (10.48%) respectively in the post-monsoon season. The overall change between 2020 and 1990 is 7994.91 km2 (1.89%) in the pre-monsoon season and 4116.93 (2.77%) in the post-monsoon season. The maximum average surface water area in the post-monsoon season was found in Sylhet (22.68%), followed by Khulna (14.52%). Rangpur was found to have the least average surface water area in both seasons (2.85% and 3.57%). Coastal regions showed a significant increasing trend in the surface water area in the pre-monsoon and post-monsoon seasons. These findings have significant implications for sustainable surface water management decisions, priority interventions, legal frameworks, and public policies to conserve valuable surface water resources in Bangladesh.

Exploring Bangladesh's Soil Moisture Dynamics via Multispectral Remote Sensing Satellite Image

Accurate monitoring and mapping of soil moisture are essential for sustainable agricultural practices, water resource management, and climate studies. This study aims to explore the mapping of soil moisture in Bangladesh using multispectral remote-sensing satellite images. The purpose of this study is to prepare a map of soil moisture aiming to help government authorities in developing agricultural activities to accelerate the sustainable development of the rural economy in Bangladesh. A total of 14 Landsat scenes of paths 135-139 and rows 42-46 covers the entire Bangladesh. Thus, a set of Landsat imagery (a total of 14 scenes) for the year 2022 was used in this study to map the soil moisture of Bangladesh through the application of Geographical Information System (GIS) and Remote Sensing. Satellite Image preprocessing, correction, and analysis were done with ENVI software (version 5.1, developed by Research Systems, Inc., USA) and the ArcGIS software (version 10.6, developed by Environmental Systems Research Institute, USA). For the study of the long-term variation of soil moisture over Bangladesh and its seasonal characteristics, a soil moisture map can be used. In addition, to improve the climate model over Bangladesh, an up-to-date soil moisture map will be very helpful. The objective of this study is to provide accurate and detailed up-to-date spatial soil moisture information at reduced cost and time which is essential for environment modeling, risk assessment, decision-making for different government agencies and development partners, and help toward socio-economic development. In this study, the map shows soil moisture as very wet, wet, dry, and very dry soils of Bangladesh. The overall land cover classification accuracy was 92.56%, with a Kappa value 0.90 for Random Forest and the overall soil classification accuracy was 87.27%, with a Kappa value 0.858 for maximum likelihood classification indicating good consistency.

Utilizing LANDSAT data and the Maximum Likelihood Classifier for Analysing Land Use Patterns in Shimoga, Karnataka

The loss of natural resources has been linked to rapid and invasive urbanization, which in turn worsens the local environment’s scenery and conditions. Preparation of a land use land cover(LULC) map is one of the methods to observe the changes in the geological structure of the study area. The LULU map gives an idea of changes that are occurring during the specified period which will in turn helps in suggesting the measures to be taken to prevent the chances of natural disasters that might occur because of these changes. This study uses a collection of LANDSAT images to evaluate changes in LULC in the Shimoga district for the years 2010, 2015, and 2020. For the classification and creation of LULC maps for the chosen periods, a supervised technique using a Maximum Likelihood Classifier(MLC) has been used. Waterbodies, urban areas, forest areas, and agricultural land have been recognized as the main classes of LULC. The overall accuracy of these maps has been evaluated while taking into account ground facts from Google Earth Pro. The overall accuracy for classification obtained is 85.03% for 2010, 85.27% for 2015, and 85.61% for 2020. The classifier created using LANDSAT scenes and the MLC approach performs well for the research area, as seen by the Kappa index values of 0.8, 0.8, and 0.81 for the years 2010, 2015, and 2020, respectively. The study’s findings indicate that over ten years, the proportion of built-up areas has expanded from 2.8% to 5.4%. When a 2.49% increase occurs in just 10 years, it is necessary to be concerned given the rise of only 1.6% over the previous 40 years. It can also be observed that the proportion of agricultural land has expanded while the fraction of forests has diminished in the study area. The findings of this study are useful in determining that LULC changes are one of the causes of natural disasters including landslides, floods, and forest fires.

Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.

Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat

Burn-area products from remote sensing provide the backbone for research in fire ecology, management, and modelling. Landsat imagery could be used to create an accurate burn-area map time series at ecologically relevant spatial resolutions. However, the low temporal resolution of Landsat has limited its development in wet tropical and subtropical regions due to high cloud cover and rapid burn-area revegetation. Here, we describe a 34-year Landsat-based burn-area product for wet, subtropical Hong Kong. We overcame technical obstacles by adopting a new LTS fire burn-area detection pipeline that (1) Automatically uniformized Landsat scenes by weighted histogram matching; (2) Estimated pixel resemblance to burn areas based on a random forest model trained on the number of days between the fire event and the date of burn-area detection; (3) Iteratively merged features created by thresholding burn-area resemblance to generate burn-area polygons with detection dates; and (4) Estimated the burn severity of burn-area pixels using a time-series compatible approach. When validated with government fire records, we found that the LTS fire product carried a low area of omission (11%) compared with existing burn-area products, such as GABAM (49%), MCD64A1 (72%), and FireCCI51 (96%) while effectively controlling commission errors. Temporally, the LTS fire pipeline dated 76.9% of burn-area polygons within two months of the actual fire event. The product represents the first Landsat-based burn-area product in wet tropical and subtropical Asia that covers the entire time series. We believe that burn-area products generated from algorithms like LTS fire will effectively bridge the gap between remote sensing and field-based studies on wet tropical and subtropical fire ecology.

Inversion of river discharge from remotely sensed river widths: A critical assessment at three-thousand global river gauges

Accurately estimating river discharge from satellite-derived river hydraulic variables (e.g., width, height, and slope) is the overarching goal of the remote sensing of discharge (RSQ) community. Numerous past studies have developed and intercompared different RSQ algorithms to demonstrate their feasibility, yet relatively few have focused on assessing how the RSQ algorithms are adapted to a wide range of rivers globally. As the community is now ready to expand to the global scale given advances in computing power, sensors, and the launch of the Surface Water and Ocean Topography (SWOT) satellite mission, a much broader geographic view of RSQ accuracy should be prioritized toward “better generalizability” instead of “higher accuracy at limited places”. To help close this gap, we extracted multi-temporal river widths from >350 K Landsat scenes at >3 K river reaches globally, and used them to estimate discharge using the Bayesian AMHG-Manning (BAM) algorithm and the geomorphologically-enhanced variant (geoBAM). We use this framework to demonstrate how to apply an ‘off the shelf’ RSQ algorithm and test it globally without methodological intervention and answer: does it live up to its promise? Our daily discharge inversions (1984–2019) showed positive Kling-Gupta Efficiency (KGE) at 27% of the gauges for BAM and 39% for geoBAM, and this percentage increased to 46–65% after feeding richer priors on flow seasonality and monthly variability, amounting to 1400–2000 successful inversions. Exploratory analyses showed that the inversion is the most sensitive to a channel geomorphological parameter b and climate aridity, where the optimal conditions are high-b, humid environments, as well as moderate width variability, leaf area index (LAI), and river width. Although specific to BAM/geoBAM, constraining the factors to their optimal ranges led to a median KGE of 0.33 for >600 gauges, which highlights the promising potential for global RSQ. We further discussed the optimal configuration of the RS/priors by analyzing results derived from different information content in priors. Overall, our critical assessment of BAM/geoBAM reveals a successful global implementation of an existing algorithm that SWOT will improve. We suggest similar large-scale assessments for other RSQs be prioritized to identify the emerging challenges as we move into a new era with global river monitoring capability from space.

Variability in Tree-ring Width and NDVI Responses to Climate at a Landscape Level

Inter-annual climatically driven growth variability of above-ground biomass compartments (for example, tree stems and foliage) controls the intensity of carbon sequestration into forest ecosystems. However, understanding the differences between the climatic response of stem and foliage at the landscape level is limited. In this study, we examined the climate-growth response of stem and leaf biomass and their relationship for Pinus sylvestris (PISY) and Picea abies (PCAB) in topographically complex landscapes. We used tree-ring width chronologies and time series of the normalized difference vegetation index (NDVI) derived from high-resolution Landsat scenes as proxies for stem and leaf biomass, respectively. We then compared growth variability and climate-growth relationships of both biomass proxies between topographical categories. Our results show that the responses of tree rings to climate differ significantly from those found in NDVI, with the stronger climatic signal observed in tree rings. Topography had distinct but species-specific effects: At moisture-limited PISY stands, stem biomass (tree rings) was strongly topographically driven, and leaf biomass (NDVI) was relatively insensitive to topographic variability. In landscapes close to the climatic optimum of PCAB, the relationship between stem and leaf biomass was weak, and their correlations with climate were often inverse, with no significant effects of topography. Different climatic signals from NDVI and tree rings suggest that the response of canopy and stem growth to climate change might be decoupled. Furthermore, our results hint toward different prioritizations of biomass allocation in trees under stressful conditions which might change allometric relationships between individual tree compartments in the long term.

A Comparison between Supervised Classification Methods: Study Case on Land Cover Change Detection Caused by a Hydroelectric Complex Installation in the Brazilian Amazon

The Volta Grande do Xingu (VGX) in the Amazon Forest of Brazil was chosen to analyze the land use and land cover changes (LULCC) from 2000 to 2017, with the aim of assessing the most suitable classification method for the area. Three parametric (Mahalanobis distance, maximum likelihood and minimum distance) and three non-parametric (neural net, random forest and support vector machine) classification algorithms were tested in two Landsat scenes. The accuracy assessment was evaluated through a confusion matrix. Change detection of the landscape was analyzed through the post-classification comparison method. While maximum likelihood was more capable of highlighting errors in individual classes, support vector machine was slightly superior when compared with the other non-parametric options, these being the most suitable classifiers within the scope of this study. The main changes detected in the landscape were from forest to agro-pasture, from forest/agro-pasture to river, and from river to non-river, resulting in rock exposure. The methodology outlined in this research highlights the usefulness of remote sensing tools in follow-up observations of LULCC in the study area (with the possibility of application to the entire Amazon rainforest). Thus, it is possible to carry out adaptive management that aims to minimize unforeseen or underestimated impacts in previous stages of environmental licensing.