Tasseled Cap Wetness Index Research Articles

The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

Spatiotemporal Dynamics of Ecological Condition in Qinghai-Tibet Plateau Based on Remotely Sensed Ecological Index

The eco-system in the Qinghai-Tibet Plateau (QTP) is extremely fragile, and highly vulnerable to climate change. Knowledge of the changes in the ecological conditions is vital to mitigate the impact of climate change. In this study, we investigated the trend of ecological conditions of the QTP using the remotely sensed ecological index (RSEI), which is the first PCA (principal component analysis) axis of the four indexes derived from the MODIS (Moderate resolution Imaging Spectroradiometer) images captured in the growing season of 2000–2020. The four indexes, i.e., NDVI (normalized difference vegetation index), heat (land surface temperature, LST), wetness (tasseled cap wetness index, WET) and dryness (normalized difference impervious surface index, NDBSI), were calculated on the Google Earth Engine platform. Using land use cover change (LUCC) data, long-term meteorological records and the supplementary annual livestock production, we explored the drivers of spatiotemporal changes in the RSEI. The results show the following points: (1) the ecological conditions of the QTP have remarkable spatiotemporal variations. There were two ecological degradation periods, one of them occurred in the central region during 2005–2010, mainly attributed to the rising temperatures and decreasing precipitation. The other occurred during 2015–2020, driven primarily by overgrazing in the southwest. From 2000 to 2005, it was a period of rapid ecological restoration in the ecologically fragile northeast region. (2) The contribution rate of pc1 was stable at 60%, while the contribution rate of pc2 remained below 40%, indicating that pc1 demonstrated most of the characteristics of the four indexes. The correlation coefficients between NDVI and WET with pc1 are both positive, while LST and NDBSI have negative correlation coefficients, i.e., negative effects. This is consistent with the actual situation. (3) Overgrazing caused grass degradation in the southwest area of the QTP, which might be the main reason for the poor ecological conditions (i.e., low RSEI value) during 2015–2020. (4) Temperature and precipitation showed an increasing trend during the study period. A warmer and wetter climate is expected to have profound impacts on the ecosystems in QTP and practices should be concentrated on identifying climate-sensitive ecosystem components and designating adaptative options.

Development of a Multi-Index Method Based on Landsat Reflectance Data to Map Open Water in a Complex Environment

Mapping surface water extent is important for managing water supply for agriculture and the environment. Remote sensing technologies, such as Landsat, provide an affordable means of capturing surface water extent with reasonable spatial and temporal coverage suited to this purpose. Many methods are available for mapping surface water including the modified Normalised Difference Water Index (mNDWI), Fisher’s water index (FWI), Water Observations from Space (WOfS), and the Tasseled Cap Wetness index (TCW). While these methods can discriminate water, they have their strengths and weaknesses, and perform at their best in different environments, and with different threshold values. This study combines the strengths of these indices by developing rules that applies an index to the environment where they perform best. It compares these indices across the Murray-Darling Basin (MDB) in southeast Australia, to assess performance and compile a heuristic rule set for accurate application across the MDB. The results found that all single indices perform well with the Kappa statistic showing strong agreement, ranging from 0.78 for WOfS to 0.84 for TCW (with threshold −0.035), with improvement in the overall output when the index best suited for an environment was selected. mNDWI (using a threshold of −0.3) works well within river channels, while TCW (with threshold −0.035) is best for wetlands and flooded vegetation. FWI and mNDWI (with threshold 0.63 and 0, respectively) work well for remaining areas. Selecting the appropriate index for an environment increases the overall Kappa statistic to 0.88 with a water pixel accuracy of 90.5% and a dry pixel accuracy of 94.8%. An independent assessment illustrates the benefit of using the multi-index approach, making it suitable for regional-scale multi-temporal analysis.

A novel surface water index using local background information for long term and large-scale Landsat images

Surface water plays a vital role in natural environment and human development. The research of water extraction method using remote sensing image is a hot topic, which has been widely developed in water index, classification, subpixel, and other aspects. Compared with other methods, a water-index based method has the advantages of fast speed and convenience. The characteristics of surface water, such as wide coverage and instability, make the water index stand out in monitoring large area of surface water. However, land surface in water environment is complex, and the main factors that reduce water extraction accuracy are also different, such as shadow in urban areas and water leakage in unshaded areas. The current index is bound to weaken the information in the water body when suppressing shadows, and vice versa. To address these issues, contrast difference water index (CDWI) and shadow difference water index (SDWI) are proposed in this paper by improving the modified normalized difference water index (MNDWI). CDWI is used to enhance water information, which is suitable for areas without building shadows. SDWI is used to eliminate the shadow of buildings, which is suitable for urban areas. Moreover, background difference water index (BDWI) was proposed by combining the advantages of CDWI and SDWI through a background regularizer B, which is used to extract surface water under complex background. The regularizer B represents the similarity between local background features of the image and the reference urban area, which is used to locally weight SDWI and CDWI, so that BDWI can automatically enhance the water body in the shadowless area and eliminate the shadow of buildings. The water extraction results of BDWI, MNDWI, the tasseled cap wetness index (TCW), the automatic water extraction index (AWEInsh, AWEIsh), and the water index 2015 (WI2015) were used for comparison. Other methods tend to perform well only in built-up areas or non-built-up areas, while the BDWI can extract surface water under various backgrounds with high accuracy and stability. The overall accuracy produced by the BDWI was 91.58–97.57%, CDWI was 84.85–97.09%, SDWI was 81.63–94.40%, MNDWI was 80.19–95.64%, TCW was 82.33–95.98%, AWEIsh was 87.50–96.37%, AWEInsh was 80.59–98.78%, and WI2015 was 78.24–98.38%. Combining water index with image local information is helpful to improve the accuracy of water extraction in large and complex environment. Finally, surface water in Jiangsu Province, China was extracted through BDWI and the changes in 1985, 2000, and 2015 were analyzed.

Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation

In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q2–Q3: 0.63–0.71; NDMI Q2–Q3: 0.10–0.19) and the TCW index had negative values (Q2–Q3: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.

Feasibility of tundra vegetation height retrieval from Sentinel-1 and Sentinel-2 data

The quantification of vegetation height for the circumpolar Arctic tundra biome is of interest for a wide range of applications, including biomass and habitat studies as well as permafrost modelling in the context of climate change. To date, only indices from multispectral data have been used in these environments to address biomass and vegetation changes over time. The retrieval of vegetation height itself has not been attempted so far over larger areas. Synthetic Aperture Radar (SAR) holds promise for canopy modeling over large extents, but the high variability of near-surface soil moisture during the snow-free season is a major challenge for application of SAR in tundra for such a purpose. We hypothesized that tundra vegetation height can be derived from multispectral indices as well as from C-band SAR data acquired in winter (close to zero liquid water content). To test our hypothesis, we used C-band SAR data from Sentinel-1 and multi-spectral data from Sentinel-2. Results show that vegetation height can be derived with an RMSE of 44 cm from Normalized Difference Vegetation Index (NDVI) and 54 cm from Tasseled Cap Wetness index (TC). Retrieval from C-band SAR shows similar performance, but C-VV is more suitable than C-HH to derive vegetation height (RMSEs of 48 and 56 cm respectively). An exponential relationship with in situ height was evident for all tested parameters (NDVI, TC, C-VV and C-HH) suggesting that the C-band SAR and multi-spectral approaches possess similar capabilities including tundra biomass retrieval. Errors might occur in specific settings as a result of high surface roughness, high photosynthetic activity in wetlands or high snow density. We therefore introduce a method for combined use of Sentinel-1 and Sentinel-2 to address the ambiguities related to Arctic wetlands and barren rockfields. Snow-related deviations occur within tundra fire scars in permafrost areas in the case of C-VV use. The impact decreases with age of the fire scar, following permafrost and vegetation recovery. The evaluation of masked C-VV retrievals across different regions, tundra types and sources (in situ and circumpolar vegetation community classification from satellite data) suggests pan-Arctic applicability to map current conditions for heights up to 160 cm. The presented methodology will allow for new applications and provide advanced insight into changing environmental conditions in the Arctic.

Flood hazard assessment of August 20, 2016 floods in Satna District, Madhya Pradesh, India

Floods are one of the most destructive and land sculpting phenomena around the globe and different aspects of them are being studied at different spatial and temporal scales. Magnitude and frequency of floods, the two important components in flood studies, have been studied with enhanced accuracy and quickness because of commencement and advancement in geospatial technology. Accentuation in the studies of floods and hazardous impacts thereof, at different temporal and spatial scales, have been achieved due to increased temporal and spatial resolution of remotely sensed satellite images. Advancement in computational power has also helped us to perform such analysis quickly. Even after such enhanced capabilities and facilities, flood studies at local levels (village or town) are not very common. Lack of such studies poses a serious challenge to hydrologists, climate scientists, geoscientists, planners and policy makers. The present work proceeds to achieve twofold objectives to ease the challenge faced: 1) to assess the flood hazard of August 20, 2016 flood event of Satna district of Madhya Pradesh at village/town level; and 2) to devise a flood hazard index (FHI) using linear combination of only two parameters i.e. flooded area and qualitative turbidity. The flooded areas have been derived from Landsat 8 OLI-TIRS image of August 20, 2016 using tasseled cap wetness index and modified normalised difference water index (MNDWI). The values of MNDWI have been taken as a proxy for qualitative turbidity. Association of FHI with flood-affected arable land and built-up area is positive and statistically significant at 0.01 level that justified the method of FHI computation. As compared to the four parameters-based FHI, devised by Kumar (2016), this study found that the FHI computed with only two parameters is also effective, easy to compute and this approach may be tested in other flood-affected parts of the globe.

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

Vegetation Dynamics in the Upper Guinean Forest Region of West Africa from 2001 to 2015

The Upper Guinea Forest (UGF) region of West Africa is one of the most climatically marginal and human-impacted tropical forest regions in the world. Research on the patterns and drivers of vegetation change is critical for developing strategies to sustain ecosystem services in the region and to understand how climate and land use change will affect other tropical forests around the globe. We compared six spectral indices calculated from the 2001–2015 MODIS optical-infrared reflectance data with manually-interpreted measurements of woody vegetation cover from high resolution imagery. The tasseled cap wetness (TCW) index was found to have the strongest association with woody vegetation cover, whereas greenness indices, such as the enhanced vegetation index (EVI), had relatively weak associations with woody cover. Trends in woody vegetation cover measured with the TCW index were analyzed using Mann–Kendall statistics and were contrasted with trends in vegetation greenness measured with EVI. In the drier West Sudanian Savanna and Guinean Forest-Savanna Mosaic ecoregions, EVI trends were primarily positive, and TCW trends were primarily negative, suggesting that woody vegetation cover was decreasing, while herbaceous vegetation cover is increasing. In the wettest tropical forests in the Western Guinean Lowland Forest ecoregion, declining trends in both TCW and EVI were indicative of widespread forest degradation resulting from human activities. Across all ecoregions, declines in woody cover were less prevalent in protected areas where human activities were restricted. Multiple lines of evidence suggested that human land use and resource extraction, rather than climate trends or short-term climatic anomalies, were the predominant drivers of recent vegetation change in the UGF region of West Africa.

Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades

Information regarding the spatial extent and timing of flooding in the world's major wetlands is important to a wide range of research questions including global methane models, water management, and biodiversity assessments. The Florida Everglades is one of the largest wetlands in the US, and is subject to substantial development and pressures that require intensive hydrological modeling and monitoring. The Moderate Resolution Imaging Spectrometer (MODIS) is a global sensor with high frequency repeat coverage and significant potential for mapping wetland extent and dynamics at moderate spatial resolutions. In this study, empirical models to predict surface inundation in the Everglades were estimated using MODIS data calibrated to water stage data from the South Florida Water Management District for the calendar year 2004. The results show that hydropatterns in the Florida Everglades are strongly correlated to a Tasseled Cap wetness index derived from MODIS Nadir Bidirectional Reflectance Function Adjusted Reflectance data. Several indices were tested, including the Normalized Difference Wetness Index and the diurnal land surface temperature difference, but the Tasseled Cap wetness index showed the strongest correlation to water stage data across a range of surface vegetation types. Other variables included in the analysis were elevation and percent tree cover present within a pixel. Using logistic regression and ensemble regression trees, maps of water depth and flooding likelihood were produced for each 16-day MODIS data period in 2004. The results suggest that MODIS is useful for dynamic monitoring of flooding, particularly in wetlands with sparse tree cover.

Estimation of managed loblolly pine stand age and density with Landsat ETM+ data

We analyzed the relationship between Landsat ETM+ reflectance values and commercially managed loblolly pine ( Pinus taeda L.) stand characteristics in east Texas. Multivariate regression techniques were used to predict age and tree density for all stands. A linear combination of NDVI, ETM4/ETM3 and the tasseled cap wetness index was a better predictor of stand age ( R 2 = 78%) than other combinations of original bands and derived indices. However, models involving transformed bands did not improve the overall predictability of stand density ( R 2 = 60%). Results from the principal component analyses (PCA) conducted on mature stands (age > 18 years) yielded valuable information about the relationship between stand structure and reflectance values recorded by the ETM+ sensor. The first principal component was interpreted as a measure of stand complexity. A linear regression model with infrared bands 4 and 5 as independent variables was able to account for 76% of the variability in stand structure. A second model with transformed bands did not increase the amount of variability in stand structure. Results obtained from this study demonstrate the relationship between loblolly pine stand characteristics and ETM+ reflectance values and the utility of certain transformed bands. Forest managers could use ETM+ data for gaining insights about stand characteristics and this information would be also useful for generating maps required for developing forest management plans.