Landsat Sensors Research Articles

Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products

Abstract Maintaining consistent datasets of Surface Reflectance (SR) is an important challenge to ensure long-term quality of Climate Data Records. The Landsat 5 and 7 archives offer a unique data source to monitor globally the land surface at high spatial resolution. The Landsat-5 TM and Landsat-7 ETM + SR products, derived from the on-demand processing Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS), require periodic evaluation to check the data consistency. Two evaluation approaches are presented in this paper. The first approach used the Aerosol Robotic Network (AERONET) data set for the period 2000 to 2013 over 489 sites with 3600 Landsat-5 TM and Landsat-7 ETM + scenes selected. For each scene, 10 × 10 km subsets of LEDAPS-derived Landsat SR and AERONET-derived SR are compared. The latter are computed using Landsat top of atmosphere reflectance, AERONET measurements of atmospheric parameters, and the 6S radiative transfer model. Second, we introduce a methodology to cross-compare Landsat data and MODIS data acquired on the same day. The analysis is based on 4000 random Landsat scenes globally distributed from 2000 to 2013. This method includes: (i) a surface anisotropy adjustment, based on the VJB Bidirectional Reflectance Distribution Function (BRDF) method, to adjust Terra and Aqua MODIS data to Landsat 5 and 7 sun-view geometry, (ii) a spectral adjustment based on an artificial neural network trained with the PROSAIL vegetation radiative transfer model, to adjust MODIS data to TM and ETM + spectral responses. The overall results of both approaches show a good match in over 80% of the scenes, i.e. the TM and ETM + SR uncertainty remained within the SR specification, defined as 0.05 × SR + 0.005. The worst results are found in the blue band used in LEDAPS to adjust the Aerosol Optical Thickness (AOT). The MODIS-Landsat SR cross-comparison confirms the utility of a BRDF adjustment method to decrease the scattering between Landsat sensors and MODIS sensors (Terra and Aqua). The spectral adjustment removes part of the biases related to spectral response differences. Global analysis is used to identify AOT retrieval issues over specific scenes, mostly over bright surfaces. From 2000 to 2013, no significant temporal variation of the performance is detected, which enhanced the consistency of LEDAPS-derived surface reflectance data set.

Performance of Landsat 8 Operational Land Imager for Mapping Ice Sheet Velocity.

Landsat imagery has long been used to measure glacier and ice sheet surface velocity, and this application has increased with increased length and accessibility of the archive. The radiometric characteristics of Landsat sensors, however, have limited these measurements generally to only fast-flowing glaciers with high levels of surface texture and imagery with high sun angles and cloud-free conditions, preventing wide-area velocity mapping at the scale achievable with synthetic aperture radar (SAR). The Operational Land Imager (OLI) aboard the newly launched Landsat 8 features substantially improves radiometric performance compared to preceding sensors: enhancing performance of automated Repeat-Image Feature Tracking (RIFT) for mapping ice flow speed. In order to assess this improvement, we conduct a comparative study of OLI and the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) performance for measuring glacier velocity in a range of surface and atmospheric conditions. To isolate the effects of radiometric quantization and noise level, we construct a model for simulating ETM+ imagery from OLI and compare RIFT results derived from each. We find that a nonlinearity in the relationship between ETM+ and OLI radiances at higher brightness levels results in a particularly large improvement in RIFT performance over the low-textured interior of the ice sheets, as well as improved performance in adverse conditions such as low sun-angles and thin clouds. Additionally, the reduced noise level in OLI imagery results in fewer spurious motion vectors and improved RIFT performance in all conditions and surfaces. We conclude that OLI imagery should enable large-area ice sheet and glacier mapping so that its coverage is comparable to SAR, with a remaining limitation being image geolocation.

Historical Urban Land Use Transformation in Virtual Geo-Library

As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. A Landsat satellite sensor which is a remote sensing platform, with its ability to analyze global data, rapidly present itself as being an invaluable tool for studying the growth of urban areas. In this study, we present the virtual geo-library as the geovisualization tools to provide the analytical studies of the urbanization process in Malang City, East Java, Indonesia, using images derived from Landsat sensor family (1989 to 2014). We provide a dynamic geovisualization through virtual geo-library, where users could understand and get valuable scientific information (e.g., urban area changes and land use transformation in higher land). This system is also equipped with the tools to enable users to create automatic cartographic maps and print the results out as a digital pdf format file.

Landsat ETM+ Secchi Disc Transparency (SDT) retrievals for Rawal Lake, Pakistan

Satellite imagery holds significant potential for monitoring regional lake water clarity. This study addresses the use of satellite data and ground observations for the assessment of Rawal Lake water clarity in Pakistan. Satellite data from Landsat sensor for the years 2009–2013 are used to model Secchi Disc Transparency (SDT). Landsat images within ±3days of the measured SDT data is used for the development of a regression model. The results of this study show that ETM+ band3 and band1/band3 ratio is the reliable predictor of SDT with R2 values of 0.725 and 0.793 respectively. The modeled SDT is further used to estimate the Trophic State Index (TSI) and trophic condition of Rawal Lake. In addition, the in situ Chlorophyll-a (Chl-a) and Total Phosphorus (TP) concentration are used to calculate the TSI of the Lake. The Mann–Kendall (MK) statistical test shows that the increasing trend in TSI based on SDT is significant (τ=0.523). The trophic condition of Rawal Lake indicates that the Lake falls under the hypereutrophic category, that is, highly polluted and extremely unhealthy for the purpose of drinking.

Influencia de la complejidad estructural del dosel en la reflectancia de datos Landsat TM

La relación entre datos de instrumentos de percepción remota y datos de inventario de bosque, en particular de la complejidad estructural de los elementos leñosos del dosel es un asunto pobremente entendido no obstante su importancia en ecología, silvicultura y manejo de recursos. En el presente trabajo se evaluó por medio de técnicas multivariadas (biplot) y Modelos Lineales Generalizados (MLG), la influencia de los diferentes estratos leñosos del dosel de cuatro variables de estructura (altura, DAP, diámetro de copa y densidad de individuos) sobre la reflectancia de datos Landsat TM en tres tipos de bosque en el Parque Nacional Cofre de Perote. Los resultados biplot indican diferencias relacionadas con el tipo de bosque y la expresión métrica de los atributos estructurales. MLG indican que la reflectancia total es dependiente de la complejidad estructural del dosel, del tipo de bosque con ciertas limitaciones derivadas de la sensitividad de los sensores. Las bandas Landsat 1-3, 5-7 mostraron una más clara relación con atributos de la complejidad forestal, en particular para bosque de Pinus hartwegii y bosque mixto, al parecer las dimensiones estructurales del bosque y limitaciones en la sensitividad de los sensores Landsat impiden explicar la reflectancia a partir de la complejidad en el bosque de A. religiosa.

A long-term perspective on deforestation rates in the Brazilian Amazon

Abstract. Monitoring tropical forest cover is central to biodiversity preservation, terrestrial carbon stocks, essential ecosystem and climate functions, and ultimately, sustainable economic development. The Amazon forest is the Earth’s largest rainforest, and despite intensive studies on current deforestation rates, relatively little is known as to how these compare to historic (pre 1985) deforestation rates. We quantified land cover change between 1975 and 2014 in the so-called Arc of Deforestation of the Brazilian Amazon, covering the southern stretch of the Amazon forest and part of the Cerrado biome. We applied a consistent method that made use of data from Landsat sensors: Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI). We acquired suitable images from the US Geological Survey (USGS) for five epochs: 1975, 1990, 2000, 2010, and 2014. We then performed land cover analysis for each epoch using a systematic sample of 156 sites, each one covering 10 km × 10 km, located at the confluence point of integer degree latitudes and longitudes. An object-based classification of the images was performed with five land cover classes: tree cover, tree cover mosaic, other wooded land, other land cover, and water. The automatic classification results were corrected by visual interpretation, and, when available, by comparison with higher resolution imagery. Our results show a decrease of forest cover of 24.2% in the last 40 years in the Brazilian Arc of Deforestation, with an average yearly net forest cover change rate of -0.71% for the 39 years considered.

An assessment of chlorophyll-a concentration spatio-temporal variation using Landsat satellite data, in a small tropical reservoir

Traditional approaches to monitoring aquatic systems are often limited by the need for data collection which often is time-consuming, expensive and non-continuous. The aim of the study was to map the spatio-temporal chlorophyll-a concentration changes in Malilangwe Reservoir, Zimbabwe as an indicator of phytoplankton biomass and trophic state when the reservoir was full (year 2000) and at its lowest capacity (year 2011), using readily available Landsat multispectral images. Medium-spatial resolution (30 m) Landsat multispectral Thematic Mapper TM 5 and ETM+ images for May to December 1999–2000 and 2010–2011 were used to derive chlorophyll-a concentrations. In situ measured chlorophyll-a and total suspended solids (TSS) concentrations for 2011 were employed to validate the Landsat chlorophyll-a and TSS estimates. The study results indicate that Landsat-derived chlorophyll-a and TSS estimates were comparable with field measurements. There was a considerable wet vs. dry season differences in total chlorophyll-a concentration, Secchi disc depth, TSS and turbidity within the reservoir. Using Permutational multivariate analyses of variance (PERMANOVA) analysis, there were significant differences (p < 0.0001) for chlorophyll-a concentration among sites, months and years whereas TSS was significant during the study months (p < 0.05). A strong positive significant correlation among both predicted TSS vs. chlorophyll-a and measured vs. predicted chlorophyll-a and TSS concentrations as well as an inverse relationship between reservoir chlorophyll-a concentrations and water level were found (p < 0.001 in all cases). In conclusion, total chlorophyll-a concentration in Malilangwe Reservoir was successfully derived from Landsat remote sensing data suggesting that the Landsat sensor is suitable for real-time monitoring over relatively short timescales and for small reservoirs. Satellite data can allow for surveying of chlorophyll-a concentration in aquatic ecosystems, thus, providing invaluable data in data scarce (limited on site ground measurements) environments.

Pre- and Post-Launch Spatial Quality of the Landsat 8 Thermal Infrared Sensor

The Thermal Infrared Sensor (TIRS) for the Landsat 8 platform was designed and built at NASA Goddard Space Flight Center (GSFC). TIRS data will extend the data record for thermal observations from the heritage Landsat sensors, dating back to the launch of Landsat 4 in 1982. The two-band (10.9 and 12.0 μm) pushbroom sensor with a 185 km-wide swath uses a staggered arrangement of quantum well infrared photodetector (QWIPs) arrays. The required spatial resolution is 100 m for TIRS, with the assessment of crop moisture and water resources being science drivers for that resolution. The evaluation of spatial resolution typically relies on a straight knife-edge technique to determine the spatial edge response of a detector system, and such an approach was implemented for TIRS. Flexibility in the ground calibration equipment used for TIRS thermal-vacuum chamber testing also made possible an alternate strategy that implemented a circular target moved in precise sub-pixel increments across the detectors to derive the edge response. On-orbit, coastline targets were developed to evaluate the spatial response performance. Multiple targets were identified that produced similar results to one another. Even though there may be a slight bias in the point spread function (PSF)/modulation transfer function (MTF) estimates towards poorer performance using this approach, it does have the ability to track relative changes for monitoring long-term instrument status. The results for both pre- and post-launch response analysis show general good agreement and consistency with edge slope along-track values of 0.53 and 0.58 pre- and post-launch and across-track values 0f 0.59 and 0.55 pre- and post-launch.

Estimating phycocyanin pigment concentration in productive inland waters using Landsat measurements: A case study in Lake Dianchi

Using remote sensing reflectance (R(rs)(λ), sr(-1)) and phycocyanin (PC, mg m(-3)) pigment data as well as other bio-optical data collected from two cruises in September and December 2009 in Lake Dianchi (a typical plateau lake of China), we developed a practical approach to estimate PC concentrations that could be applied directly to Landsat measurements. The visible and near-IR bands as well as their band ratios of simulated Landsat data were used as inputs to the algorithms, where the algorithm coefficients for each Landsat sensor were determined through multivariate regressions. The coefficients of determination (R(2)) between the R(rs)-modeled and measured PC were all > 0.97 for the spectral bands corresponding to Landsat 8 OLI, Landsat 7 ETM + , Landsat 5 TM, and Landsat 4 TM, with mean absolute percentage errors (MAPE) < 10% for PC ranging between ~80 and 700 mg m(-3) (n = 14). The algorithms were further evaluated using an independent data set (n = 14), yielding larger but still acceptable MAPE (~30%) for PC ranging between ~80 and 500 mg m(-3). Application of the approach to Landsat 8 measurements over Lake Dianchi suggests potential use of the approach for periodical assessment of the lake's bloom conditions, yet its empirical nature together with the lack of specific narrow bands on Landsat sensors to explicitly account for the PC absorption around 625 nm calls for extra caution when applied to other eutrophic lakes.

Meeting Earth Observation Requirements for Global Agricultural Monitoring: An Evaluation of the Revisit Capabilities of Current and Planned Moderate Resolution Optical Earth Observing Missions

Agriculture is a highly dynamic process in space and time, with many applications requiring data with both a relatively high temporal resolution (at least every 8 days) and fine-to-moderate (FTM &lt; 100 m) spatial resolution. The relatively infrequent revisit of FTM optical satellite observatories coupled with the impacts of cloud occultation have translated into a barrier for the derivation of agricultural information at the regional-to-global scale. Drawing upon the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative’s general satellite Earth observation (EO) requirements for monitoring of major production areas, Whitcraft et al. (this issue) have described where, when, and how frequently satellite data acquisitions are required throughout the agricultural growing season at 0.05°, globally. The majority of areas and times of year require multiple revisits to probabilistically yield a view at least 70%, 80%, 90%, or 95% clear within eight days, something that no present single FTM optical observatory is capable of delivering. As such, there is a great potential to meet these moderate spatial resolution optical data requirements through a multi-space agency/multi-mission constellation approach. This research models the combined revisit capabilities of seven hypothetical constellations made from five satellite sensors—Landsat 7 Enhanced Thematic Mapper (Landsat 7 ETM+), Landsat 8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI/TIRS), Resourcesat-2 Advanced Wide Field Sensor (Resourcesat-2 AWiFS), Sentinel-2A Multi-Spectral Instrument (MSI), and Sentinel-2B MSI—and compares these capabilities with the revisit frequency requirements for a reasonably cloud-free clear view within eight days throughout the agricultural growing season. Supplementing Landsat 7 and 8 with missions from different space agencies leads to an improved capacity to meet requirements, with Resourcesat-2 providing the largest incremental improvement in requirements met. The best performing constellation can meet 71%–91% of the requirements for a view at least 70% clear, and 45%–68% of requirements for a view at least 95% clear, varying by month. Still, gaps exist in persistently cloudy regions/periods, highlighting the need for data coordination and for consideration of active EO for agricultural monitoring. This research highlights opportunities, but not actual acquisition rates or data availability/access; systematic acquisitions over actively cropped agricultural areas as well as a policy which guarantees continuous access to high quality, interoperable data are essential in the effort to meet EO requirements for agricultural monitoring.

Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations

Recently, water extraction based on the indices method has been documented in many studies using various remote sensing data sources. Among them, Landsat satellites data have certain advantages in spatial resolution and cost. After the successful launch of Landsat 8, the Operational Land Imager (OLI) data from the satellite are getting more and more attention because of its new improvements. In this study, we used the OLI imagery data source to study the water extraction performance based on the Normalized Difference Vegetation Index, Normalized Difference Water Index, Modified Normalized Water Index (MNDWI), and Automated Water Extraction Index (AWEI) and compared the results with the Thematic Mapper (TM) imagery data. Two test sites in Tianjin City of north China were selected as the study area to verify the applicability of OLI data and demonstrate its advantages over TM data. We found that the results of surface water extraction based on OLI data are slightly better than that based on TM in the two test sites, especially in the city site. The AWEI and MNDWI indices performs better than the other two indices, and the thresholds of water indices show more stability when using the OLI data. So, it is suitable to combine OLI imagery with other Landsat sensor data to study water changes for long periods of time.

Long-term Monitoring of the Environmental Impact of a Refugee Camp Based on Landsat Time Series: The Example of Deforestation and Reforestation During the whole Lifespan of the Camp Lukole, Tanzania

This study deals with the long-term monitoring of the environmental impact of the refugee camp Lukole, Tanzania. Based on high resolution (HR) satellite time series of different Landsat sensors, the whole lifespan of the camp is covered, starting before the camp was established (1994) until seven years after the dismantling (2015). A fully automated preclassification approach for different Landsat sensors was applied, and the results were integrated into a post-classification object-specific change comparison to quantify de- and reforestation processes during this time. The second part of the study will focus on different visualization methods to display the changes throughout the whole study area over time.

Optimal Satellite Sensor Selection Utilized to Monitor the Impact of Urban Sprawl on the Thermal Environment in Doha City, Qatar

In the present study, the most appropriate Landsat and ASTER Image spatial and spectral resolutions were selected to extract thermal infrared data in Doha City, Qatar from 1990-2015. Results indicated Urban Heat Island (UHI) calculated from Landsat sensor data was more consistent with ground truthed temperatures. Landsat TM thermal infrared data with low spatial resolution (60-120 m) was more appropriate for large-scale thermal studies, but was not suitable to assess complex urban thermal environments and establish Land Surface Temperatures (LSTs) for individual buildings. However, ASTER sensor, with a Thermal Infrared (TIR) subsystem of 90 m spatial resolution, showed more accurate determination of thermal patterns and LSTs. Landsat data revealed relatively high temperatures, which exhibited increased congruence with ground truthed measures. In Doha City, LST showed an increased trend from 1990 to 2015; low in 1990, moderate in 2000, to very high in 2015. Furthermore, our results confirmed accelerated urban sprawl during 1990-2015. The evaluated areas increased from 80 km2 in 1990 to 179 km2 in 2015. A strong positive relationship was observed between UHI and urban area intensity. Urban planning absence significantly affected the rural environment. Urbanized areas exhibited high thermal conductivity and radiation heat budgets, characterized by increased atmospheric and surface temperatures compared to surrounding rural areas. Therefore, rapid urbanization rates must undergo proper planning and management regimes to insure positive impacts on the local climate and environment.

A simple Landsat–MODIS fusion approach for monitoring seasonal evapotranspiration at 30 m spatial resolution

Persistent cloud-cover in the humid southeastern USA and the low temporal resolution of Landsat sensors limit the derivation of seasonal evapotranspiration (ET) maps at moderate spatial resolution. This article introduces a Landsat Moderate Resolution Imaging Spectroradiometer (Landsat–MODIS) ET fusion model that uses simple linear regression to integrate Landsat-derived reference ET fraction (ETrF) from mapping ET at high resolution with internalized calibration (METRIC) model and the vegetation temperature condition index (VTCI) derived from MODIS images. For a study site in Florida, model-estimated ET and ET estimated using energy budget eddy covariance at a US Geological Survey (USGS) station in Ferris Farm, Florida, were found to be in a good agreement with a root mean squared error of 0.44 mm day–1, coefficient of determination (R2) of 0.80, Nash–Sutcliffe efficiency of 0.79 for daily ET (ETd), and 2% relative error for cumulative seasonal ET during the growing season of 2001. At another study site in Alabama, the model underestimated 2008 annual water balance ET for the Fish River Watershed by 39 mm or 4%. Comparisons of model-estimated ET with that obtained using a non-fusion Landsat-only approach at both sites indicated that the fusion of Landsat and MODIS ET values reduces potential errors in ET estimation that would otherwise arise due to insufficient availability of cloud-free Landsat images for METRIC processing. Validation results and application of the model in deriving seasonal/annual ET for different land-cover classes in the Fish River Watershed suggested that the fusion model has the potential to be used in continuously monitoring ET for field- to watershed-level agricultural and hydrological applications in the southeastern USA.

Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia

High spatio-temporal resolution optical remote sensing data provide unprecedented opportunities to monitor and detect forest disturbance and loss. To demonstrate this potential, a 12-year time series (2000 to 2011) with an 8-day interval of a 30m spatial resolution data was generated by the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) with Landsat sensor observations and Moderate Resolution Imaging Spectroradiometer (MODIS) data as input. The time series showed a close relationship over homogeneous forested and grassland sites, with r2 values of 0.99 between Landsat and the closest STARFM simulated data; and values of 0.84 and 0.94 between MODIS and STARFM. The time and magnitude of clearing and re-clearing events were estimated through a phenological breakpoint analysis, with 96.2% of the estimated breakpoints of the clearing event and 83.6% of the re-clearing event being within 40days of the true clearing. The study highlights the benefits of using these moderate resolution data for quantifying and understanding land cover change in open forest environments.

Geo-information Based Spatio-temporal Modeling of Urban Land Use and Land Cover Change in Butwal Municipality, Nepal

Abstract. Unscientific utilization of land use and land cover due to rapid growth of urban population deteriorates urban condition. Urban growth, land use change and future urban land demand are key concerns of urban planners. This paper is aimed to model urban land use change essential for sustainable urban development. GI science technology was employed to study the urban change dynamics using Markov Chain and CA-Markov and predicted the magnitude and spatial pattern. It was performed using the probability transition matrix from the Markov chain process, the suitability map of each land use/cover types and the contiguity filter. Suitability maps were generated from the MCE process where weight was derived from the pair wise comparison in the AHP process considering slope, land capability, distance to road, and settlement and water bodies as criterion of factor maps. Thematic land use land cover types of 1999, 2006, and 2013 of Landsat sensors were classified using MLC algorithm. The spatial extent increase from 1999 to 2013 in built up , bush and forest was observed to be 48.30 percent,79.48 percent and 7.79 percent, respectively, while decrease in agriculture and water bodies were 30.26 percent and 28.22 percent. The predicted urban LULC for 2020 and 2027 would provide useful inputs to the decision makers. Built up and bush expansion are explored as the main driving force for loss of agriculture and river areas and has the potential to continue in future also. The abandoned area of river bed has been converted to built- up areas.

Multispectral image unsupervised segmentation using watershed transformation and cross-entropy minimization in different land use

A general-purpose unsupervised segmentation algorithm based on cross-entropy minimization by pixel was developed; this algorithm, known as the SCEMA (Segmentation Cross-Entropy Minimization Algorithm), starts from an initial segmentation and iteratively searches the best statistical model, estimating the probability density of the image to reduce the cross-entropy with respect to the previous iteration. The SCEMA was tested using satellite images from the Landsat Thematic Mapper sensor of Landsat 5 for the Amazon region (12 images for testing and 15 for validation). Theme classes identified in the image were (1) water, (2) vegetation, and (3) agriculture. Using the Kappa index and other statistics parameters, the comparison of classifications is made with the following segmentation methods: (1) cross-entropy minimization by pixel, (2) cross-entropy minimization by region, (3) K-means, and (4) maximum likelihood. The results indicate that cross-entropy minimization by pixel results in a consistent segmentation of images. The algorithm also compares favorably to other well-known image segmentation methods, and the numerical test results illustrate the efficiency of our approach for image segmentation.

An Analysis of the Side Slither On-Orbit Calibration Technique Using the DIRSIG Model

Pushbroom-style imaging systems exhibit several advantages over line scanners when used on space-borne platforms as they typically achieve higher signal-to-noise and reduce the need for moving parts. Pushbroom sensors contain thousands of detectors, each having a unique radiometric response, which will inevitably lead to streaking and banding in the raw data. To take full advantage of the potential exhibited by pushbroom sensors, a relative radiometric correction must be performed to eliminate pixel-to-pixel non-uniformities in the raw data. Side slither is an on-orbit calibration technique where a 90-degree yaw maneuver is performed over an invariant site to flatten the data. While this technique has been utilized with moderate success for the QuickBird satellite [1] and the RapidEye constellation [2], further analysis is required to enable its implementation for the Landsat 8 sensors, which have a 15-degree field-of-view and a 0.5% pixel-to-pixel uniformity requirement. This work uses the DIRSIG model to analyze the side slither maneuver as applicable to the Landsat sensor. A description of favorable sites, how to adjust the maneuver to compensate for the curvature of “linear” arrays, how to efficiently process the data, and an analysis to assess the quality of the side slither data, are presented.

An approach to monitoring mangrove extents through time-series comparison of JERS-1 SAR and ALOS PALSAR data

Between 2007 and 2010, Japan’s Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) captured dual polarization HH and HV data across the tropics and sub-tropics. A pan tropical dataset of Japanese Earth Resources Satellite (JERS-1) SAR (HH) data was also acquired between 1995 and 1998. The provision of these comparable cloud-free datasets provided an opportunity for observing changes in the extent of coastal mangroves over more than a decade. Focusing on nine sites distributed through the tropics, this paper demonstrates how these data can be used to backdate and update existing baseline maps of mangrove extent. The benefits of integrating dense time-series of Landsat sensor data for both validating assessments of change and determining the causes of change are outlined. The approach is evaluated for wider application across the geographical range of mangroves in order to advance the development of JAXA’s Global Mangrove Watch (GMW) program.

Land use and land cover change dynamics across the Brazilian Amazon: insights from extensive time-series analysis of remote sensing data.

Throughout the Amazon region, the age of forests regenerating on previously deforested land is determined, in part, by the periods of active land use prior to abandonment and the frequency of reclearance of regrowth, both of which can be quantified by comparing time-series of Landsat sensor data. Using these time-series of near annual data from 1973–2011 for an area north of Manaus (in Amazonas state), from 1984–2010 for south of Santarém (Pará state) and 1984–2011 near Machadinho d’Oeste (Rondônia state), the changes in the area of primary forest, non-forest and secondary forest were documented from which the age of regenerating forests, periods of active land use and the frequency of forest reclearance were derived. At Manaus, and at the end of the time-series, over 50% of regenerating forests were older than 16 years, whilst at Santarém and Machadinho d’Oeste, 57% and 41% of forests respectively were aged 6–15 years, with the remainder being mostly younger forests. These differences were attributed to the time since deforestation commenced but also the greater frequencies of reclearance of forests at the latter two sites with short periods of use in the intervening periods. The majority of clearance for agriculture was also found outside of protected areas. The study suggested that a) the history of clearance and land use should be taken into account when protecting deforested land for the purpose of restoring both tree species diversity and biomass through natural regeneration and b) a greater proportion of the forested landscape should be placed under protection, including areas of regrowth.