Fraction images derived from Terra Modis data for mapping burnt areas in Brazilian Amazonia

The objective of this paper is to present a method for mapping burnt areas in Brazilian Amazonia using Terra MODIS data. The proposed approach is based on image segmentation of the shade fraction images derived from MODIS, using a non‐supervised classification algorithm followed by an image editing procedure for minimizing misclassifications. Acre State, the focus of this study, is located in the western region of Brazilian Amazonia and undergoing tropical deforestation. The extended dry season in 2005 affected this region creating conditions for extensive forest fires in addition to fires associated with deforestation and land management. The high temporal resolution of MODIS provides information for studying the resulting burnt areas. Landsat 5 TM images and field observations were also used as ground data for supporting and validating the MODIS results. Multitemporal analysis with MODIS showed that about 6500 km² of land surface were burnt in Acre State. Of this, 3700 km² corresponded to the previously deforested areas and 2800 km² corresponded to areas of standing forests. This type of information and its timely availability are critical for regional and global environmental studies. The results showed that daily MODIS sensor data are useful sources of information for mapping burnt areas, and the proposed method can be used in an operational project in Brazilian Amazonia.     

Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS

Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution (<30 m) data are used to assess their accuracy with little regard to the accuracy of the higher spatial resolution reference data. In this study we aimed to investigate whether Landsat Enhanced Thematic Mapper (ETM+)‐derived reference imagery can be more accurately produced using such spectrally informed methods. The efficacy of several spectral index methods to discriminate between burned and unburned surfaces over a series of spatial scales (ground, IKONOS, Landsat ETM+ and data from the MOderate Resolution Imaging Spectrometer, MODIS) were evaluated. The optimal Landsat ETM+ reference image of burned area was achieved using a charcoal fraction map derived by linear spectral unmixing (k = 1.00, a = 99.5%), where pixels were defined as burnt if the charcoal fraction per pixel exceeded 50%. Comparison of coincident Landsat ETM+ and IKONOS burned area maps of a neighbouring region in Mongu (Zambia) indicated that the charcoal fraction map method overestimated the area burned by 1.6%. This method was, however, unstable, with the optimal fixed threshold occurring at >65% at the MODIS scale, presumably because of the decrease in signal‐to‐noise ratio as compared to the Landsat scale. At the MODIS scale the Mid‐Infrared Bispectral Index (MIRBI) using a fixed threshold of >1.75 was determined to be the optimal regional burned area mapping index (slope = 0.99, r ² = 0.95, SE = 61.40, y = Landsat burned area, x = MODIS burned area). Application of MIRBI to the entire MODIS temporal series measured the burned area as 10 267 km² during the 2001 fire season. The char fraction map and the MIRBI methodologies, which both produced reasonable burned area maps within southern African savannah environments, should also be evaluated in woodland and forested environments.     

Mapping glacial lakes partially obscured by mountain shadows for time series and regional mapping applications

Mountain shadows in optical satellite images complicate the mapping of glacial lakes. Due to the rugged topography in periglacial alpine regions, many glacial lakes, especially smaller lakes, are partially shaded by mountain shadows in remotely sensed images. Shadows not only reduce the accuracy of lake mapping but also make changes in lake area hard to detect. In this paper, the characteristics of mountain shadows in remotely sensed imagery are explored, and their spatial relationships with regards to glacial lakes are modelled. Building on the previously developed Glacial Lakes Iterative Local Mapping (GLILM) method, a new water mapping approach is presented. The new method utilizes log-transformed spectral data and a normalized difference water index, NDWI_blue, for delineating the boundaries of lakes within shadowed regions. The application of this approach is explored within the context of mapping lakes across space and time using Landsat images in the glacially dominated Tianshan mountainous of Central Asia. The results demonstrate that glacial lakes, both in sunlit and in shaded areas, can be mapped reliably, and that the results are useful for lake change analysis studies.     

Mapping inland lake water quality across the Lower Peninsula of Michigan using Landsat TM imagery

The number, size, and distribution of inland freshwater lakes present a challenge for traditional water-quality assessment due to the time, cost, and logistical constraints of field sampling and laboratory analyses. To overcome this challenge, Landsat imagery has been used as an effective tool to assess basic water-quality indicators, such as Secchi depth (SD), over a large region or to map more advanced lake attributes, such as cyanobacteria, for a single waterbody. The overarching objective of this research application was to evaluate Landsat Thematic Mapper (TM) for mapping nine water-quality metrics over a large region and to identify hot spots of potential risk. The second objective was to evaluate the addition of landscape pattern metrics to test potential improvements in mapping lake attributes and to understand drivers of lake water quality in this region. Field-level in situ water-quality measurements were collected across diverse lakes (n = 42) within the Lower Peninsula of Michigan. A multicriteria statistical approach was executed to map lake water quality that considered variable importance, model complexity, and uncertainty. Overall, band ratio radiance models performed well (R² = 0.65–0.81) for mapping SD, chlorophyll-a, green biovolume, total phosphorus (TP), and total nitrogen (TN) with weaker (R² = 0.37) ability to map total suspended solids (TSS) and cyanobacteria levels. In this application, Landsat TM and pattern metrics showed poor ability to accurately map non-purgable organic carbon (NPOC) and diatom biovolume, likely due to a combination of gaps in temporal overpass and field sampling and lack of signal sensitivity within broad spectral channels of Landsat TM. The composition and configuration of croplands, urban, and wetland patches across the landscape were found to be moderate predictors of lake water quality that can complement lake remote-sensing data. Of the 4071 lakes, over 4 ha in the Lower Peninsula, approximately two-thirds, were identified as mesotrophic (n = 2715). This application highlights how an operational tool might support lake decision-making or assessment protocols to identify hot spots of potential risk.     

Tracking bamboo dynamics in Zhejiang,China, using time-series of Landsat data from 1990 to 2014

Bamboo is an important vegetation type and provides a number of critical ecosystem services. Reliable and consistent information on bamboo distribution is required to better estimate its effect on climate change mitigation and socio-economic development. However, such information is rare over a large spatial area. In this study, we evaluate the contribution of different features in the identification of bamboo stands and determine a more discriminative set of features. We propose a bamboo mapping system including feature extraction and feature selection and derive the long-term trends of bamboo distribution in Zhejiang Province, China, using time-series of Landsat data from 1990 to 2014, with an increment of 5 years (1990, 1995, 2000, 2005, 2010, and 2014). The resultant maps of bamboo in the six epochs were evaluated using independent validation samples. The overall accuracies (OAs) of all six epochs range from 85.9% to 90.7%. We found that bamboo distribution in Zhejiang substantially increased from 1990 to 2014, particularly during the 2000s. Based on the produced maps, the area of bamboo in this region increased from 5363 ± 490 km² in 1990 to 11671 ± 653 km² in 2014, which is consistent with the National Forest Resource Inventory (NFRI) data. Our study demonstrates the capability of time-series of Landsat data for continuous monitoring of bamboo at a large spatial scale.     

Change in area of Ebinur Lake during the 1998–2005 period

Ebinur Lake is located in a typical arid region in the north‐west of China. It is an area with the lowest elevation in the Junggar Basin in the Province of Xinjiang. Recent monitoring indicates that the lake surface area has increased. To obtain a continuous record of the change in lake area, a radiometric analysis of SPOT/VEGETATION (VGT) imagery was carried out based on methodology developed for regional lake area mapping. Two indices, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were selected to identify the water body of Ebinur Lake. The indices are calculated based on the spectral reflectances in the red and near infrared bands of VGT sensor. If the NDVI is less than a critical value (0) and if the NDWI is larger than a critical value (0), the pixel is flagged as a water body. Validation indicates that the methodology to identify water bodies based on multi‐spectral VGT data is applicable in our study area achieving an overall accuracy of 91.4%. Independent monitoring results elicit that the lake surface area was at its lowest in 1998. The yearly average surface area is about 503 km². The lake area increased to 603 km² during 1999. In the period 1999–2001 the area changes are marginal. A large area increase occurred from 2001 to 2002 till the lake area reached a surface area of 791 km². The lake area peaks to 903 km² in 2003 and subsequently decreased to areas of 847 km² in 2004 and 746 km² in 2005. Similar area change dynamics are observed when applying the remote sensing based technique. Seasonally, the typical dynamics elicit a larger surface area in spring and winter and a smaller one during summer.     

Spatial variability of SEBAL estimated root-zone soil moisture across scales

This study investigated the spatial scaling behaviour of root-zone soil moisture obtained from optical/thermal remote-sensing observations. The data for this study were obtained from Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites on five different dates between early spring (April) and fall (September) in the years from 2000 to 2004 in the semi-arid middle Rio Grande Valley of New Mexico. Soil moisture data were obtained using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm. The data were spatially aggregated and checked for power-law behaviour over a range of scales from 30 m to 15 km for Landsat and from 1 to 28 km for MODIS images. Results of this study demonstrate that power-law scaling of soil moisture in the middle Rio Grande area holds up to 1 km² pixel size, but is no longer valid beyond that scale. Whereas previous studies have studied soil moisture in the top 5 cm of the soil using radar and point measurements, our study uses SEBAL to estimate root-zone soil moisture. Our study is consistent with these previous studies in showing that variation in root-zone soil follows an empirical power law for pixel sizes of up to about 10⁶ m² and that there is an apparent break in the scaling at larger scales.     

利用长时间序列Landsat分析博斯腾湖面积变化

目的 近年来博斯腾湖面积波动较大,影响当地经济发展,掌握博斯腾湖面积变化及其与气候变化的相关规律,以指导湖泊保护和可持续利用管理策略。方法 利用Landsat影像计算1988—2014年博斯腾湖面积,监测并分析湖水面积年际变化及空间变化趋势,探讨博斯腾湖流域年降水量、年均气温变化和人类活动对湖水面积的影响,并将监测结果与MODIS数据计算的2000—2014年湖水面积以及1987—2011年实测水位数据进行对比验证。结果 结果表明,以2002年为分界线,博斯腾湖面积变化分2个阶段:1)1988—2002年,湖水面积呈增加趋势,增加288.88 km²,增长了31.62%;2)2002—2014年呈减小趋势,减小281.56 km²,减少了23.42%。根据气候条件分布差异,将博斯腾湖流域分为山区和平原区,分析发现:1988—2002年,山区年降水量和气温上升,与湖水面积呈显著正相关;2002年后,山区年降水量相对下降,平原区气温升高,人类活动用水量增加。结论 湖水面积变化受流域气候与人类活动共同作用,1988—2002年主要受山区气候影响,2002年后湖水面积缩小可能是气温升高和人类活动用水量增加导致。     

Cropland distributions from temporal unmixing of MODIS data

Knowledge of the distribution of crop types is important for land management and trade decisions, and is needed to constrain remotely sensed estimates of variables, such as crop stress and productivity. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop type mapping. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation. We developed and tested a linear unmixing approach with MODIS that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. In this method, termed probabilistic temporal unmixing (PTU), endmember sets were constructed using Landsat data to identify pure pixels, and uncertainty resulting from endmember variability was quantified using Monte Carlo simulation. This approach was evaluated using Landsat classification maps in two intensive agricultural regions, the Yaqui Valley (YV) of Mexico and the Southern Great Plains (SGP). Performance of the mixture model varied depending on the scale of comparison, with R² ranging from roughly 50% for estimating crop area within individual pixels to greater than 80% for crop cover within areas over 10 km². The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of land cover distributions using coarse resolution sensors, such as MODIS.     

Quantifying scaling effects on satellite‐derived forest area estimates for the conterminous USA

We quantified the scaling effects on forest area estimates for the conterminous USA using regression analysis and the National Land Cover Dataset 30 m satellite‐derived maps in 2001 and 1992. The original data were aggregated to: (1) broad cover types (forest vs. non‐forest); and (2) coarser resolutions (1 km and 10 km). Standard errors of the model estimates were 2.3% and 4.9% at 1 km and 10 km resolutions, respectively. Our model improved the accuracies for 1 km by 0.6% (12 556 km²) in 2001 and 1.9% (43 198 km²) in 1992, compared to the forest estimates before the adjustments. Forest area observed from Moderate Resolution Imaging Spectroradiometer (MODIS) 2001 1 km land‐cover map for the conterminous USA might differ by 80 811 km² from what would be observed if MODIS was available at 30 m. Of this difference, 58% (46 870 km²) could be a relatively small net improvement, equivalent to 1444 Tg (or 1.5%) of total non‐soil forest CO₂ stocks. With increasing attention to accurate monitoring and evaluation of forest area changes for different regions of the globe, our results could facilitate the removal of bias from large‐scale estimates based on remote sensors with coarse resolutions.     

Satellite‐based assessment of the dynamics of new lakes in southern Egypt

Lakes in arid landscapes are indicators of environmental change and important sources of water for human use. In regions without in situ hydrologic measurements, remote sensing may provide the only means to monitor long‐term changes in water storage. We used a synergistic combination of multiple satellite remote‐sensing methods to provide the first comprehensive assessment of the dynamics of a newly formed chain of large lakes in the hyper‐arid Toshka Depression of southern Egypt. A total of 145 MODIS and AVHRR satellite images were used to monitor changes in lake surface area, which increased to a maximum of 1740 km² before declining to 900 km². Two methods were tested for satellite‐based measurement of lake levels and volumes, one based on analysis of a digital elevation model and one using data from the ICESat GLAS laser altimeter. This study shows the power of satellite remote sensing for long‐term monitoring of regional‐scale hydrologic transformations.     

Frontal changes in the Manimahesh and Tal Glaciers in the Ravi basin,Himachal Pradesh,northwestern Himalaya (India), between 1971 and 2013

The Manimahesh and Tal Glaciers are located in the Budhil fifth-order sub-basin of the Ravi, Himachal Himalaya, Northwestern Himalaya (India). These glaciers were analysed using high- (Corona KH-4A) to medium- (Landsat TM/ETM+/OLI, ASTER) spatial resolution satellite data between 1971 and 2013, along with extensive field measurements (2011–2014) of frontal changes. The results show that the Manimahesh and Tal Glaciers retreated by 157 ± 34 m (4 ± 1 m year^–1) and 45 ± 34 m (1 ± 1 m year^–1), respectively, whereas, the total area lost is estimated at 0.21 ± 0.01 km² (0.005 km² year^–1) and 0.010 ± 0.003 km² (0.0002 km² year^–1), respectively, between 1971 and 2013. The rate of retreat is significantly lower than that previously reported. Our field measurements (2011–2014) also suggest a retreating trend and validate the measured glacier changes using remotely sensed temporal data.     

Multitemporal assessment of the geomorphologic evolution of the Restinga of Marambaia,Rio de Janeiro,Brazil

The Restinga of Marambaia is an emerged sand bar located between the Sepetiba Bay and the South Atlantic Ocean, on the south‐east coast of Brazil. The objective of this study was to observe the geomorphologic evolution of the coastal zone of the Restinga of Marambaia using multitemporal satellite images acquired by multisensors from 1975 to 2004. The images were digitally segmented by a region growth algorithm and submitted to an unsupervised classification procedure (ISOSEG) followed by a raster edit based on visual interpretation. The image time‐series showed a general trend of decrease in the total sand bar area with values varying from 80.61 km² in 1975 to 78.15 km² in 2004. The total area calculation based on the 1975 and 1978 Landsat MSS data was shown to be super‐estimated in relation to the Landsat TM, Landsat ETM+, and CBERS‐2 CCD data. These differences can also be associated to the relatively poorer spatial resolution of the MSS data, nominally 79 m, against the 20 m of the CCD data and 30 m of the TM and ETM+ data. For the estimates of the width in the central portion of the sand bar the variation was from 158 m (1975) to 100 m (2004). The formation of a spit in the northern region of the study area was visually observed. The area of the spit was estimated, with values varying from 0.82 km² (1975) to 0.55 km² (2004).     

Irrigated area mapping in heterogeneous landscapes with MODIS time series,ground truth and census data,Krishna Basin,India

Diverse irrigated areas were mapped in the Krishna River Basin (258,912 km²), southern India, using an irrigated fraction approach and multiple ancillary data sources. Unsupervised classification of a monthly time series of net difference vegetation index (NDVI) images from the Moderate Resolution Imaging Spectrometer (MODIS) over January–December 2002 generated 40 classes. Nine generalized classes included five irrigated classes with distinct NDVI time signatures: continuous irrigation, double‐cropped, irrigated with low biomass, minor irrigation, and groundwater irrigation. Areas irrigated by surface water began greening 45 days after groundwater‐irrigated areas, which allowed separation of surface and groundwater irrigation in the classification. The fraction of each class area irrigated was determined using three different methods: ground truth data, a linear regression model calibrated to agricultural census data, and visual interpretation of Landsat TM imagery. Irrigated fractions determined by the three methods varied least for the double‐cropped irrigated class (0.62–0.79) and rangeland (0.00–0.02), and most for the minor irrigated class (0.06–0.43). Small irrigated patches (<0.1 km²) accounted for more irrigated area than all major surface water irrigated areas combined. The irrigated fractions of the minor and groundwater‐irrigated classes differed widely by method, suggesting that mapping patchy and small irrigated areas remains challenging, but comparison of multiple data sources improves confidence in the classification and highlights areas requiring more intensive fieldwork.     

Land-use land-cover classification analysis of Giba catchment using hyper temporal MODIS NDVI satellite images

Landsat-based land-use land-cover (LULC) mapping studies were previously conducted in Giba catchment, comprising an area of 4019 km². No attempt has been done to map LULC of this catchment through the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series data. This article is aimed to see whether time-series MODIS NDVI data set is applicable for LULC mapping of Giba catchment or not. MODIS NDVI data sets of the year 2010 were used for classification analysis. The original data were subjected to MODIS Reproduction Tool and stacking. The re-projected and stacked images were filtered using Harmonic Analysis of Time-Series filtering algorism to remove the effects of cloud and other noises. The MODIS NDVI data sets (16-day maximum value composite) were classified using the ISODATA clustering algorithm available under ERDAS IMAGINE software. A series of unsupervised classification runs were carried out with a pre-defined number of classes (5–24). From this classification, the optimal numbers of classes were determined to be eight after checking for average divergence analysis. The classification result became eight LULC classes namely: bare land, grass land, irrigated land, cultivated land, area closure, shrub land, bush land, and forest land with an overall accuracy of 87.7%. It was therefore concluded that MODIS NDVI time-series image is applicable for mapping large watersheds.     

Using MODIS images to examine the surface extents and variations derived from the DEM and laser altimeter data in the Danjiangkou Reservoir,China

We used publicly available digital spatial datasets to study the area extents and their horizontal variations of two water bodies within the Danjiangkou Reservoir, China. Between 2003 and 2005, the water levels varied from 140 to 149 m above mean sea level as measured by the Geoscience Laser Altimeter System (GLAS). Detailed procedures to derive the horizontal extents and variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) coupled with GLAS data and to verify the extents and variations spatially were provided. For the water bodies on the north and west, the surface water extents derived from four MODIS images varied between 174 and 218 km² and from 96 to 135 km², respectively. The extents by inundating the DEM using the GLAS data were 178–212 km² for the water body on the north and 104–118 km² for the water body on the west. The spatial verifications of surface water extents derived from the MODIS images versus DEM coupled with GLAS data agreed 83–93%. Within the ring areas between water/land boundaries at elevations of 140 and 147 m, and 140 and 149 m, the spatial agreement was 52–75%.     

Changes in the glaciers of Chandra–Bhaga basin,Himachal Himalaya,India, between 1980 and 2010 measured using remote sensing

This study reports the glacier changes of Chandra–Bhaga basin, northwest Himalaya, India, from 1980 to 2010. Satellite remote-sensing data from the Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM), the Linear Imaging Self Scanning Sensor (LISS) and Advanced Wide Field Sensor (AWiFS) of the Indian Remote Sensing (IRS) series, and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) were used to study the changes in glacier parameters such as glacier area, length, snout elevation, and the impact of glacier topographical parameters (glacier slope, aspect, and altitude range) on the glacier changes. It was found that the total glaciated area had shrunk to 368.2 km² in 2010 from 377.6 km² in 1980, a loss of 2.5%. The average position of glacier terminuses retreated by 465.5 ± 169.1 m from 1980 to 2010 with an average rate of 15.5 ± 5.6 m year^?1. The decadal scale analysis showed that the average rate of retreat had increased the most in the recent decade. A moraine-dammed lake located in the study region was found to have expanded in area from (0.65 ± 0.01) km² in 1980 to (1.26 ± 0.03) km² in 2010. Glaciers with steep slope and less altitude range have lost more area than the glaciers having gentle slope and greater altitude range.     

Mapping pasture biomass in Mongolia using Partial Least Squares,Random Forest regression and Landsat 8 imagery

The aim of this study was to develop a robust methodology to estimate pasture biomass across the huge land surface of Mongolia (1.56 × 10⁶ km²) using high-resolution Landsat 8 satellite data calibrated against field-measured biomass samples. Two widely used regression models were compared and adopted for this study: Partial Least Squares (PLS) and Random Forest (RF). Both methods were trained to predict pasture biomass using a total of 17 spectral indices derived from Landsat 8 multi-temporal satellite imagery as predictor variables. For training, reference biomass data from a field survey of 553 sites were available. PLS results showed a satisfactory correlation between field measured and estimated biomass with coefficient of determination (R²) = 0.750 and Root Mean Square Error (RMSE) = 101.10 kg ha^?1. The RF regression gave similar results with R² = 0.764, RMSE = 98.00 kg ha^?1. An examination of feature importance found the following vegetation indices to be the most relevant: Green Chlorophyll Index (CL_green), Simple Ratio (SR), Wide Dynamic Range Vegetation Index (WDRVI), Enhanced Vegetation Index EVI₁ and Normalized Difference Vegetation Index (NDVI) indices. With respect to the spectral reflectances, Red and Short Wavelength Infra-Red2 (SWIR₂) bands showed the strongest correlation with biomass. Using the developed PLS models, a spatial map of pasture biomass covering Mongolia at a spatial resolution of 30 m was generated. Our study confirms the high potential of RF and PLS regression (PLSR) models to predict pasture biomass. The computationally simpler PLSR model is preferred for applications involving large regions. This method can be implemented easily, provided that sufficient reference data and cloud-free observations are available.     

Water level estimation by remote sensing for the 2008 flooding of the Kosi River

Flood is a natural disaster which worsens when it is triggered by man-made constructions. This paper discusses one such flood event which occurred because of breach of a levee in the upper reach of the Kosi River in 2008, when floodwater spread over a large portion of the low-lying Ganga Plain of North Bihar, India. Here we have analysed a suite of space-based observations from radar altimetry, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and Tropical Rainfall Measuring Mission (TRMM) precipitation data, together with in situ monthly precipitation data, with a main emphasis on the results from altimetry and MODIS data. A methodology to calculate water levels, using MODIS data and Envisat data together, is also discussed. Our analyses suggest a rise in water level of 1.0–1.4 m in the flooded region during the flood event and a maximum extent for the flooded area of around 2900 km². Analyses of TRMM precipitation data do not indicate any influence of high precipitation in the upper catchment of the Kosi Basin on river water feeding into the plain area after breaching of dam. However, heavy and prolonged precipitation was found downstream of the dam over the flooded area during the flood period.