Landsat Surface Reflectance Research Articles

MODELING PINUS ELLIOTTII GROWTH WITH MULTITEMPORAL LANDSAT DATA: A STUDY CASE IN SOUTHERN BRAZIL

Abstract Remote sensing data are a key proxy to forest monitoring and management at local, regional and global scales. Considering the hypothesis that NDVI and EVI can be used at least during one decade to monitor Pinus elliottii in Southern Brazil, the objective of this study was to identify saturation time after planting of these vegetation indices in a Pinus elliottii plantation and the most suitable index by adjusting theoretical functions to each one of them. Based on Landsat Surface Reflectance Higher-Level Data Products, 32 scenes were selected between 1984 to 2015. A set of theoretical polynomial, gaussian and logistic mathematical functions were applied to fit the experimental data on vegetation indices. The determination coefficient (R²) and RMSE at 95% probability were also used. Finally, EVI efficiency was tested by changing the L parameter. The logistic model was the one that best explained the data resulting from NDVI and EVI over time. NDVI was more effective than EVI for this forest monitoring, identifying the forest growth pattern until its 18 years of age. EVI may have been saturated after 14 years and the L factor may be set to near to zero to achieve a higher coefficient of determination.

Yield Prediction Model for Potato Using Landsat Time Series Images Driven Vegetation Indices

Crop growth monitoring and its yield forecasting is a pre-requisite task to ensure food security and to assess the economic return from agriculture. The application of remote sensing techniques in agricultural sector enhanced the potentiality of crop growth monitoring and estimation of crop yields. Over the last few years, potato cultivation in Bangladesh has promisingly increased. On the contrary, proper distribution capability and the storage capacity is still under the desirable condition due to lack of management system. In this context, remote sensing image analysis techniques were employed in this study to predict potato yield before harvesting to improve management system. This study used 16-day high resolution (~ 30 m) Landsat surface reflectance data for the year of 2010–2011 to detect the maximum normalized difference vegetation index (NDVI) value of a potato growing season. The NDVI will be the maximum after the 63 days of the plantation for Munshiganj District of Bangladesh. Afterward, six satellite images were selected from six growing seasons: 2010–2011, 2011–2012, 2012–2013, 2013–2014, 2014–2015, and 2015–2016 based on the day of the maximum NDVI. A total of 35 potato fields were identified from the filed investigation over the study area to extract the NDVI values from satellite images. Regression analysis was performed between NDVI values and filed level potato yields. The maximum coefficient of correlation (R2) of yield prediction equation was found to be 0.81 between the mean NDVI and potato yield. The equation was validated by using data from 2016 to 2017 growing season, and the result revealed that the difference between predicted and actual filed yield is about 10.4%. It has been found that the high-resolution Landsat images can be an effective means for early estimation of potato yield.

Landsat time series-based multiyear spectral angle clustering (MSAC) model to monitor the inter-annual leaf senescence of exotic saltcedar

In the western United States, the rapid expansion of exotic saltcedar along riparian corridors has drastically altered landscape structures and ecosystem functions. Monitoring the geographical distribution and spatio-temporal dynamics of this invasive species is essentially critical to conduct the systematic restoration of affected riparian ecosystems. Previous studies indicated that the leaf senescence stage is the optimal time window to remotely monitor saltcedar distributions. Yet due to climate variability and anthropogenic forcing, the timing of saltcedar leaf senescence varies over space and time. Given that the saltcedar leaf senescence stage only lasts for a short temporal window (i.e., three or four weeks), pinpointing the appropriate Landsat image across years and locations without the expert knowledge is challenging. Remotely sensed phenological time series analysis provides a practical means to locate the leaf senescence date on a per-pixel basis. However, affected by temporal revisit rates and cloud contamination, Landsat time series can only capture very limited temporal segments of vegetation phenology. The lack of Landsat imagery throughout the year makes the conventional time series analysis difficult. In this study, we developed a multiyear spectral angle clustering (MSAC) model to monitor the inter-annual leaf senescence of saltcedar with limited Landsat imagery on a per-pixel basis. The MSAC model leverages the Landsat images across years to temporally predict the fall phenology of plant species in a single year, and constructs the synthesized time series of spectral signatures to estimate critical phenological transition dates. Results indicated that the MSAC model could guide the construction of the composite Landsat surface reflectance image to accommodate spatial and inter-annual variation in the timing of plant leaf senescence. The phenology-guided composite image from the MSAC model achieved a greater saltcedar mapping accuracy than any single Landsat image in 2004. The proposed MSAC model provides new insights in estimating phenological transition dates of plant species with limited Landsat imagery. It opens up unique opportunities to guide the selection of representative remotely sensed imagery on a per-pixel basis for repetitive saltcedar mapping over wide geographical regions.

Monitoring of playa evaporites as seen with optical remote sensing sensors: case of Chott El Jerid, Tunisia, from 2003 to present

Chott El Jerid in the Zone of Chotts of Tunisia is one of the largest endorheic basins in the world. During the dry period, from May to August, it is generally covered by continental evaporites which result from the desiccation of the lake formed after a flooding event. This lake comprises runoff water from the surrounding relief and also water resurgence. In order to map and monitor these evaporitic surfaces (mineral composition and evolution in space and time), optical multisource, multispectral, and multidate satellite data have been used. Landsat 4–5 TM, Landsat 8 OLI, SPOT 6, and Landsat Surface Reflectance (LSR) constitute the main data set. The central part of the Chott, north and south of the road crossing the Chott over 70 km from Tozeur to Kebili, has been particularly studied, because it corresponds to the major evaporite accumulation zone. These evaporites precipitated as concentric layers (a relatively rare pattern), mainly north of the road, after several recent flooding events during the last 15 years. Winds can play a significant role in the development of the evaporite layers. Image interpretation associated with field data shows that after the final desiccation of the playa lake, the mineralogy of the salt crust comprised an assemblage dominated by halite south of the road and by gypsum north of the road. Halite and gypsum are the only minerals to be identified using satellite remote sensing data. Sulfates such as gypsum can be identified thanks to a drop in reflectance in the short-wave infrared (SWIR) range caused by vibrations of the SO4 group. Gypsum crusts are more widely distributed than halite crusts. LSR data are particularly suitable for multitemporal comparison because they are calibrated and atmospherically corrected. The classical bull’s eye pattern characterizing evaporitic deposits (from carbonates along the rims to halite, gypsum, and finally potassium-magnesium minerals in the center of the basin) is deeply disturbed by the road crossing Chott El Jerid.

Sea surface temperatures and seagrass mortality in Florida Bay: Spatial and temporal patterns discerned from MODIS and AVHRR data

Two major episodes of seagrass mortality have occurred in Florida Bay in the past 30 years: The first occurred between 1987 and 1991 and the second began in 2015. In both episodes, dense beds of turtle grass (Thalassia testudinum) were decimated. Elevated water temperature and hypersalinity have been implicated as contributing factors in both mortality events. During both die-off events seagrass beds on shallow (Z≈0.3 m) mudbanks in western Florida Bay were disproportionately affected when compared to neighboring seagrass in deeper basins (Z≈1.5 m). A network of in situ monitoring stations has measured sea surface temperature and salinity at hourly intervals in 16 basins in Florida Bay from August 2009 to the present day and these data aided in diagnosing the 2015–2016 die-off event. However, very few in situ observations of sea surface temperature have been collected on Florida Bay's shallow mudbanks as most are inaccessible by boat. As a result, our understanding of the role of elevated SST in shaping the spatial patterns of seagrass mortality is hindered by the spatial distribution of the in situ monitoring data and its record length. Therefore, we turn to remotely sensed SST data to expand our spatial coverage to include the entire Florida Bay ecosystem and to extend the record length to include the 1987–1991 die-off event. 1 km MODIS SST shows that shallow mudbanks were consistently warmer (by up to 6°C) than nearby deeper basins. While water depth is likely the primary driver of spatial variability in SST, Landsat-8 surface reflectance data suggest that shallow seagrass beds could have suffered from the added influence of low surface reflectance, which might have further contributed to their thermal stress. Daily 0.25° AVHRR SST shows that the August maximum SST has increased by 1°C from 1981 to 2016, which is a cause for concern for the future of seagrass in Florida Bay. Correlation of monthly AVHRR SST anomalies with the multivariate ENSO index shows that ENSO can only partially explain the anomalous temperatures. When viewed together, the in situ and remotely sensed SST suggests that both extended exposure to anomalously warm temperatures and large, rapid changes in temperature could have contributed to seagrass mortality during both events.

Changing northern vegetation conditions are influencing barren ground caribou (Rangifer tarandus groenlandicus) post‐calving movement rates

AbstractAimTo quantify changes in vegetation productivity over the past three decades across five barren ground caribou (Rangifer tarandus groenlandicus) herd ranges and assess how these changes are influencing caribou movement rates.LocationNorthwest Territories and Nunavut, Canada.MethodsAs an indicator of vegetation productivity, the enhanced vegetation index (EVI) was calculated on newly developed cloud‐free, gap‐free, Landsat surface reflectance image composites representing 1984–2012. Changes in EVI were assessed on a pixel basis using Theil‐Sen's nonparametric regression and compared across herd ranges and land cover types using generalized least squares regression. Animal movement velocity was calculated from caribou telemetry data and generalized additive mixed models were used to link movement rates with vegetation productivity during the post‐calving phase of the year (July and August).ResultsVegetation productivity increased across the five caribou herd ranges examined. The largest productivity increase occurred over the ranges of the most western herds, with the largest observed changes in grassland or shrub habitats. Caribou tended to move more slowly through tundra habitats with elevated levels of productivity to a point, while grasslands movement rates decreased linearly with increasing productivity. Movement velocities peaked at intermediate productivity levels in shrub habitats.Main conclusionsOver the three decades of collected data, barren ground caribou habitats have become more productive, which is consistent with other studies that have documented increases in Arctic vegetation productivity. The more western herds, whose ranges are also closest to the Arctic Ocean, experienced the largest increases in productivity. Finally, we demonstrate that barren ground caribou movement patterns will likely change as a result of changing vegetation productivity in complex manners depending on herd, habitat type and the magnitude of change in vegetation productivity.

Mapping pasture management in the Brazilian Amazon from dense Landsat time series

Intensification of cattle ranching has the potential to reduce deforestation rates in the Brazilian Amazon by decreasing the demand for new agricultural land. Explicit spatial knowledge on where, when and how pastures are managed and intensification takes place is needed to better estimate potentials of more sustainable management. Monitoring the frequency of management practices like burning of pasture land and tillage treatment with adequate spatial resolution therefore offers novel indicators for describing land use intensity. With dense time series of Landsat data, it appears possible to quantify land use intensity also in heterogeneous landscapes where fine-scale processes cannot be monitored with previously available datasets.Our overarching goal is to describe the occurrence or absence of extensive or intensive management regimes over time. For this study, we focused on detecting fire and tillage events in the region of Novo Progresso, Pará, Brazil, where deforested land is mostly used for cattle ranching by both largeholders and smallholders. We used a dense time series of Landsat-7 and Landsat-8 surface reflectance data to mitigate the problem of varying cloud cover. For each acquisition date, we extracted a temporal sequence of three subsequent clear observations at pixel level. The temporal variation in each clear observation sequence was characterized by a stack of spectral and temporal features. These feature stacks were classified with a random forest to identify the management events. We aggregated the classification results based on the random forest class probabilities and derived normalized annual class scores for land management events. The yielded class score maps show burned and tilled areas on a yearly basis and provide a measure of model confidence.We detected burned pastures with area adjusted user accuracies of 80–98%, burned secondary regrowth with 63–80% and tilled pastures with 74–78%. Our approach was able to discriminate management events even when they succeeded very fast. This way, the mapping approach with clear observation sequences allows us to contextualize management events directly from dense time series of high resolution satellite data. This opens new pathways to disentangle how management practices between smallholders and agribusinesses vary in the Brazilian Amazon. With sensor constellations from Sentinel-2 and Landsat data becoming a unified source for much denser time series soon, our method bears great potential to better understand and map pasture dynamics in the Amazon.

A Framework for Estimating the 30 m Thermal‐Infrared Broadband Emissivity From Landsat Surface Reflectance Data

AbstractThe land surface thermal‐infrared broadband emissivity (BBE) is a vital variable for estimating land surface radiation budgets (SRBs). We develop a framework for retrieving the 30 m BBE from Landsat surface reflectance data to estimate SRBs at finer scales and validate coarse resolution data. In the developed framework, the land surface is classified as bare soils and vegetated surfaces to allow different algorithms to be used for the BBE estimation. We propose a downscaling algorithm that uses the empirical relationship between the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) BBE and Landsat surface reflectance at 90 m to retrieve the 30 m BBE over bare soils. A look‐up table (LUT)‐based algorithm is proposed for vegetated surfaces. The BBE is interpolated from a LUT that is constructed from the 4SAIL radiative transfer model with inputs of the leaf BBE, the soil background BBE, and the leaf area index (LAI). Ground measurements that were collected at 11 relatively homogeneous sandy sites during three independent field campaigns are used to validate the proposed algorithm over bare soils. The average difference between the retrieved and field‐measured BBEs is 0.012. We produce the land surface BBE of China in 2008 by using the developed framework and composited winter and summer seasonal BBE maps. The composited seasonal BBE maps are compared to the seasonal BBE maps derived from the ASTER emissivity product. The bias is within ±0.005 over bare soils and ranges from 0.012 to 0.019 over vegetated surfaces. Combined with the validated results in this study and published references, the comparison results demonstrate the good performance of the developed framework. This study provides a new perspective on estimating BBEs from sensors with only a thermal‐infrared channel.

Modeling and Simulation of Deciduous Forest Canopy and Its Anisotropic Reflectance Properties Using the Digital Image and Remote Sensing Image Generation (DIRSIG) Tool

Extraction of biophysical information from forest canopies using temporal analysis of multispectral and hyperspectral data can be significantly improved by understanding its anisotropic reflectance properties. However, limitations on the accessibility and data collection techniques in the field reduce the availability of high-resolution bidirectional reflectance measurements (BRDF) to a few datasets. These limitations can be mitigated in a virtual environment and this paper presents an approach to model the spectral BRDF of a forest canopy using the Digital Image and Remote Sensing Image Generation (DIRSIG) tool. The three-dimensional geometries of the trees were modeled using forest inventory data and OnyxTree, while the spectral properties of the geometric elements were assigned based on the field collected spectra and PROSPECT inversion model. The DIRSIG tool was used as a virtual goniometer to measure the BRDF observations for varying sun-view geometries and a full hemispherical BRDF model was constructed by fitting the measurements to a semiempirical BRDF model. This paper discusses the methods involved in modeling the forest canopy scene, sensitivity of the radiative transfer, BRDF sampling and modeling strategies, model accuracy and its effect on real-world simulations. The model fit results indicate a root mean square error of less than 5% relative to the forests reflectance in the VIS-NIR-SWIR region. The simulated BRDF matched to within 2% of the Landsat-8 surface reflectance product in the red and NIR bands. The results can be used directly to evaluate BRDF modeling algorithms and the proposed method can be easily extended for other biomes.

Detection of cropland field parcels from Landsat imagery

A slowdown in global agricultural expansion, spurred by land limitations, improved technologies, and demand for specific crops has led to increased agricultural intensification. While agricultural expansion has been heavily scrutinized, less attention has been paid to changes within cropland systems. Here we present a method to detect individual cropland field parcels from temporal Landsat imagery to improve cropland estimates and better depict the scale of farming across South America. The methods consist of multi-spectral image edge extraction and multi-scale contrast limited adaptive histogram equalization (CLAHE) and adaptive thresholding using Landsat Surface Reflectance Climate Data Record (CDR) products. We tested our methods across a South American region with approximately 82% of the 2000/2001 total cropland area, using a Landsat time series composite with a January 1, 2000 to August 1, 2001 timeframe. A thematic accuracy assessment revealed an overall cropland f-score of 91%, while an object-based assessment of 5480 fields showed low geometric errors. The results illustrate that Landsat time series can be used to accurately estimate cropland in South America, and the low geometric errors of the per-parcel estimates highlight the applicability of the proposed methods over a large area. Our approach offers a new technique of analyzing agricultural changes across a broad geographic scale. By using multi-temporal Landsat imagery with a semi-automatic field extraction approach, we can monitor within-agricultural changes at a high degree of accuracy, and advance our understanding of regional agricultural expansion and intensification dynamics across South America.

Improving river water quality monitoring using satellite data products and a genetic algorithm processing approach

Abstract Adequate river quality monitoring is of major importance for riverine environmental sustainability. This study develops a methodology for real-time water quality measurement in a river at 30 m spatial and 1 day temporal scales using the satellite remote sensing technique to support daily water quality monitoring usually done at gauges. Considering the limited spatio-temporal resolutions of all the current satellite products, this study integrates the corrected band-specific Landsat and Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance values, identified by a physically-based approach, with the observed pollutant concentrations. A combination of regression analysis and genetic algorithm (GA) based multivariate nonlinear formulations among the Landsat versus MODIS surface reflectances and Landsat surface reflectance versus in-situ pollutant concentration is used to estimate eight water quality parameters. All the possible combinations of the Landsat and MODIS satellite bands containing the spectral signature of pollutants are selected as independent variables. Linear and nonlinear regression analysis is carried out for these combinations using the SPSS software to get the best (significant) correlated relations which are, further, enhanced using the GA. This formulation is applied and tested in the Brahmani River located in eastern India’s Odisha state for its real-time application; and water quality mapping is carried out for a typical river reach of the Brahmani River. A Monte-Carlo simulation based uncertainty and sensitivity analysis of the used algorithms reveal that the methods have the potential to be used in ungauged river reaches.

Using Landsat Surface Reflectance Data as a Reference Target for Multiswath Hyperspectral Data Collected Over Mixed Agricultural Rangeland Areas

Low-cost flight-based hyperspectral imaging systems have the potential to provide important information for ecosystem and environmental studies as well as aide in land management. To realize this potential, methods must be developed to provide large-area surface reflectance data allowing for temporal data sets at the mesoscale. This paper describes a bootstrap method of producing a large-area, radiometrically referenced hyperspectral data set using the Landsat surface reflectance (LaSRC) data product as a reference target. The bootstrap method uses standard hyperspectral processing techniques that are extended to remove uneven illumination conditions between flight passes, allowing for radiometrically self-consistent data after mosaicking. Through selective spectral and spatial resampling, LaSRC data are used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from two hyperspectral flights over the same managed agricultural and unmanaged range land covering approximately 5.8 km2 acquired on June 21, 2014 and June 24, 2015 are presented. Data from a flight over agricultural land collected on June 6, 2016 are compared with concurrently collected ground-based reflectance spectra as a means of validation.

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R2 = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R2 = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R2 = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data.

Quantifying suspended sediment concentration in subglacial sediment plumes discharging from two Svalbard tidewater glaciers using Landsat-8 and in situ measurements

ABSTRACTMarine-terminating outlet glaciers discharge mass through iceberg calving, submarine melting, and meltwater run-off. While calving can be quantified by in situ and remote-sensing observations, meltwater run-off, the subglacial transport of meltwater, and submarine melting are not well constrained due to inherent difficulties observing the subglacial and proglacial environments at tidewater glaciers. Remote-sensing and in situ measurements of surface sediment plumes, and their suspended sediment concentration (SSC), have been used as a proxy for glacier meltwater run-off. However, this relationship between satellite reflectance and SSC has predominantly been established using land-terminating glaciers. Here, we use two Svalbard tidewater glaciers to establish a well-constrained relationship between Landsat-8 surface reflecance and SSC and argue that it can be used to measure relative meltwater run-off at tidewater glaciers throughout a summer melt season. We find the highest correlation between SSCs and Landsat-8 surface reflectance by using the red + NIR band combination (r2 = 0.76). The highest correlation between SSCs and in situ field spectrometer measurements is in the 740–800 nm wavelength range (r2 = 0.85), a spectral range not currently measured by Landsat. Additionally, we find that in situ and Landsat-8 measurements for surface reflectance of SSCs are not interchangeable and therefore establish a relationship for each detection method. We then use the Landsat-8 relationship to calculate total surface sediment load, finding a strong correlation between total surface sediment load and a proxy for meltwater run-off (r2 ≥ 0.89). Our results establish a new metric to calculate SSCs from Landsat-8 surface reflectance and demonstrate how the SSC of subglacial sediment plumes can be used to monitor relative seasonal meltwater discharge at tidewater glaciers.

Satellite observation of coastline change around Hay Point, Australia before and after tropical cyclone “Debbie”

On March 28, 2017, tropical cyclone “Debbie” made landfall on the coast of the state of Queensland in Australia. This was the most powerful tropical cyclone to hit Australia in the past 6 years. Debbie has resulted in at least five deaths and caused rail damage, power outages and flooding according to relevant reports. This cyclone halted railway transportation and several mines in Queensland had to be closed, including the coal exports terminals of Abbot Point, Dalrymple Bay and Hay Point. In a short period, Debbie resulted in a strong hydro-dynamical response. Subsequently, Debbie caused considerable coastline movement and significantly modified the coastal topography. The position of a coastline is controlled by tide level and coastal topography. Hence, we should take tide level variation into consideration to avoid its impact on the determination of coastline position. To locate the position of coastline and assess its temporal change around Hay Point, we collected tide level data from Rosslyn Bay station near the study area. Based on the tide level data, two Landsat-8 Operational Land Imager (OLI) images showing similar tide levels before and after Debbie were obtained from the United States Geological Survey (USGS) Earth Explorer website. Landsat scenes used in this paper from Landsat Surface Reflectance Climate Data Records (CDR) had been pre-processed to L1TP level (Precision and Terrain Correction). The widely used Normalized Difference Water Index (NDWI) was used to enhance water information. This water index is able to enlarge the discrimination between surface water and other types of land cover. The optimal threshold was manually determined using the frequency histogram of the NDWI image. We applied the optimal threshold to produce a land-water map. Then, inland water was filled to remove its effect on the land-ocean map. Finally, manual checking and editing were implemented in ArcGIS10.2 to ensure the quality of land-ocean maps before and after Debbie. We used the produced land-ocean map to represent the position of the coastline. Two land-ocean maps were overlaid and subtracted to generate the coastline change map. The net change of land area per 1 km×1 km cell along the coastline was calculated to characterize the spatial pattern of coastline change after cyclone Debbie. Our results suggest that the coastline significantly expanded to the ocean after the landfall of Debbie with a net land gain of 54.5 km2 along the coast around Hay Point, Australia. In our study area, 98.2% of the coastline exhibited expansion to the ocean and only 1.8% of the coastline retreated to the land. Note that we may slightly underestimate the net land gain because some regions in the study area were covered by cloud/shadow during observations. We also observed a significant increase of suspended sediment concentration around some coasts using visual judgement, which is likely to be associated with the coastline expansion around the coastal area near Hay Point. Timely and accurate information on coastline position and its change is essential to the management of sudden siltation of ports and waterways, and the subsequent resumption of the operation of wharves. Such datasets can also be helpful to develop and validate storm surge-wave-sediment numerical models. This paper used a simple application to demonstrate the great potential of satellite technology for natural disaster monitoring and assessment. In the future, more attention should be paid to the use of multi-source remotely sensed data to conduct continuous monitoring of coastline dynamics with higher spatial resolution and temporal frequency.

Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

ABSTRACTLeaf area index (LAI) is a key vegetation biophysical parameter and is extensively used in modelling of phenology, primary production, light interception, evapotranspiration, carbon, and nitrogen dynamics. In the present study, we attempt to spatially characterize LAI for natural forests of Western Ghats India, using ground based and Landsat-8 Operational Land Imager (OLI) sensor satellite data. For this, 41 ground-based LAI measurements were carried out across a gradient of tropical forest types, viz. dry, moist, and evergreen forests using LAI-2200 plant canopy analyser, during the month of March 2015. Initially, measured LAI values were regressed with 15 spectral variables, including nine spectral vegetation indices (SVIs) and six Landsat-8 surface reflectance (ρ) variables using univariate correlation analysis. Results showed that the red (ρred), near-infrared (ρNIR), shortwave infrared (ρSWIR1, ρSWIR2) reflectance bands (R2 > 0.6), and all SVIs (R2 > 0.7) except simple ratio (SR) have the highest and second highest coefficient of determination with ground-measured LAI. In the second step, to select significant (high R2, low root mean square error (RMSE), and p-level < 0.05) SVIs to determine the best representative model, stepwise multiple linear regression (SMLR) was implemented. The results indicate that the SMLR model predicted LAI with better coefficient of determination (R2 = 0.83, RMSE = 0.78) using normalized difference vegetation index, enhanced vegetation index, and soil-adjusted vegetation index variables compared to the univariate approach. The predicted SMLR model was used to estimate a spatial map of LAI. It is desirable to evaluate the stability and potentiality of regional LAI models in natural forest ecosystems against the operationally accepted Moderate Resolution Imaging Spectroradiometer (MODIS) global LAI product. To do this, the Landsat-8 pixel-based LAI map was resampled to 1 km resolution and compared with the MODIS derived LAI map. Results suggested that Landsat-8 OLI-based VIs provide significant LAI maps at moderate resolution (30 m) as well as coarse resolution (1 km) for regional climate models.

A disaggregation approach for estimating high spatial resolution broadband emissivity for bare soils from Landsat surface reflectance

ABSTRACTHigh spatial resolution land surface broadband emissivity (BBE) is not only useful for surface energy balance studies at local scales, but also can bridge the gap between existing coarser resolution BBE products and point-based field measurements. This study proposes a disaggregation approach that utilizes the established BBE–reflectance relationship for estimating high spatial resolution BBE for bare soils from Landsat surface reflectance data. The disaggregated BBE is compared to the BBE calculated from spatial–temporal matched Advanced Spaceborne Thermal Emission and Reflectance Radiometer emissivity product. Comparison results show that better agreement is achieved over relative homogeneous areas, but deteriorated over heterogeneous and cloud-contaminated areas. In addition, field-measured emissivity data over large homogeneous desert were also used to validate the disaggregated BBE, and the bias is 0.005. The comparison and validation results indicated that the disaggregation approach can obtain high spatial resolution BBE with better accuracy for homogeneous area.

Development of an empirical model for chlorophyll-a and Secchi Disk Depth estimation for a Pampean shallow lake (Argentina)

Shallow Pampean lakes are located in the most productive plain of Argentina. They are highly variable in salinity, turbidity and surface area. Laguna Chascomús has been monitored as a representative example of them. We developed a linear model based on satellite images validated against field measurements (2001–2011 period). A vegetation index and Landsat Surface Reflectance (Band 4) produced the best correlations with chlorophyll-a (Chl-a) and Secchi Disk Depth (SDD), respectively. In a second instance, a retrospective analysis (1986–2013) was performed. As a result, significant positive trends were observed for SDD and Chl-a. In addition, both variables displayed trends related to rainfall and site depth.

Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards

Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are too sparse to fully capture the spatial variability of vine conditions. In addition, traditional grape yield prediction methods are time consuming and require large amount grape samples. Remote sensing data provide detailed spatial and temporal information regarding vine development that is useful for vineyard management. In this study, Landsat surface reflectance products from 2013 and 2014 were used to map satellite-based Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) over two Vitis vinifera L. cv. Pinot Noir vineyards in California, USA. The spatial correlation between grape yield maps and the interpolated daily time series (LAI and NDVI) was quantified. NDVI and LAI were found to have similar performance as a predictor of spatial yield variability, providing peak correlations of 0.8 at specific times during the growing season, and the timing of this peak correlation differed for the two years of study. In addition, correlations with maximum and seasonal-cumulative vegetation indices were also evaluated, and showed slightly lower correlations with the observed yield maps. Finally, the within-season grape yield predictability was examined using a simple strategy in which the relationship between grape yield and vegetation indices were calibrated with limited ground measurements. This strategy has a strong potential to improve the accuracy and efficiency of yield estimation in comparison with traditional approaches used in the wine grape growing industry.

Landsat Identification of Tornado Damage by Land Cover and an Evaluation of Damage Recovery in Forests

AbstractMultispectral satellite imagery provides a spaceborne perspective on tornado damage identification; however, few studies have explored how tornadoes alter the spectral signature of different land-cover types. In part 1 of this study, Landsat surface reflectance is used to explore how 17 tornadoes modify the spectral signature, NDVI, and “Tassled Cap” parameters inside forest (N = 16), grassland (N = 10), and urban (N = 17) land cover. Land cover influences the magnitude of change observed, particularly in spring/summer imagery, with most tornado-damaged surfaces exhibiting a higher median reflectance in the visible and shortwave infrared, and a lower median reflectance in the near-infrared spectral ranges. These changes result in a higher median Tasseled Cap brightness, lower Tasseled Cap greenness and wetness, and lower NDVI relative to unaffected areas. Other factors affecting the magnitude of change in reflectance include season, vegetation condition, land-cover heterogeneity, and tornado strength. While vegetation indices like NDVI provide a quick way to identify damage, they have limited utility when monitoring recovery because of the cyclical seasonal vegetation cycle. Since tornado damage provides an analogous spectral signal to that of forest clearing, NDVI is compared with a forest disturbance index (DI) across a 5-yr Landsat climatology surrounding the 27 April 2011 tornado outbreak in part 2 of this study. Preoutbreak DI values remain relatively stable across seasons. In the five tornado-damaged areas evaluated, DI values peak within 6 months followed by a decline coincident with ongoing recovery. DI-like metrics provide a seasonally independent mechanism to fill the gap in identifying damage and monitoring recovery.