Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images

Remote sensing offers a nondestructive tool for the quick and precise estimation of canopy chlorophyll content that serves as an important indicator of the plant ecosystem. In this study, the canopy chlorophyll content of 26 samples in 2007 and 40 samples in 2008 of maize were nondestructively estimated by a set of vegetation indices (VIs; Normalized Difference Vegetation Index, NDVI; Green Chlorophyll Index, CI_green; modified soil adjust vegetation index, MSAVI; and Enhanced Vegetation Index, EVI) derived from the hyperspectral Hyperion and Thematic Mapper (TM) images. The PROSPECT model was used for sensitivity analysis among the indices and results indicated that CI_green had a large linear correlation with chlorophyll content ranging from 100–1000 mg m^?2. EVI showed a moderate ability in avoiding saturation and reached a saturation of chlorophyll content above 600 mg m^?2. Both of the other two indices, MSAVI and NDVI, showed a clear saturation at chlorophyll content of 400 mg m^?2, which demonstrated they may be inappropriate for chlorophyll interpretation at high values. A validation study was also conducted with satellite observations (Hyperion and TM) and in-situ measurements of chlorophyll content in maize. Results indicated that canopy chlorophyll content can be remotely evaluated by VIs with r ² ranging from the lowest of 0.73 for NDVI to the highest of 0.86 for CI_green. EVI had a greater precision (r ²=0.81) than MASVI (r ²=0.75) in canopy chlorophyll content estimation. The results agreed well with the sensitivity study and will be helpful in developing future models for canopy chlorophyll evaluation.     

Remote sensing used to detect moisture status of pecan orchards grown in a desert environment

Remote-sensing techniques can detect and up-scale leaf-level physiological responses to large areas, and provide significant and reliable information on water use and irrigation management. The objectives of this study were to screen leaf-level physiological changes that occur during the cyclic irrigation of pecan orchards to determine which responses best represent changes in moisture status of plants and link plant physiological changes to remotely sensed surface reflectance data derived from the Landsat Thematic Mapper and Enhanced Thematic Mapper Plus (ETM+). The study was conducted simultaneously on two southern New Mexico mature pecan orchards. For both orchards, plant physiological responses and remotely sensed surface reflectance data were collected from trees that were either well watered or in water deficit. Remotely sensed variables included reflectance in band 1, the ratio between shortwave infrared (SWIR) bands (B5:B7), the normalized difference vegetation index, and SWIR moisture indices. Midday stem water potential (Ψ_smd) was the best performing leaf-level physiological response variable for detecting moisture status in pecans. The B5:B7 ratio positively and significantly correlated with Ψ_smd in five of six irrigation cycles while multiple linear regression weighted with six remotely sensed surface reflectance variables revealed a significant relationship with moisture status in all cycles in both orchards (R² > 0.73). Because changes in the B5:B7 band ratio and multiple regression of spectral variables correlate with the moisture status of pecan orchards, we conclude that remotely sensed data hold promise for detecting the moisture status of pecans.     

Application of multitemporal Landsat data to monitor land cover changes in the Eastern Nile Delta region,Egypt

Due to the progressive increase in development of desert land in Egypt, the demand for efficient and accurate land cover change information is increasing. In this study, we apply the methodology of post‐classification change detection to map and monitor land cover change patterns related to agricultural development and urban expansion in the desert fringes of the Eastern Nile Delta region. Using a hybrid classification approach, we employ multitemporal Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images from 1984, 1990 and 2003 to produce three land cover/land‐use maps. Post‐classification comparison of these maps was used to obtain ‘from–to’ statistics and change detection maps. The change detection results show that agricultural development increased by 14% through the study period. The average annual rate of land reclamation during 1990–2003 (4511 ha a^?1) was comparable to that during 1984–1990 (4644 ha a^?1), reflecting a systematic national plan for desert reclamation that went into effect. We find that the increase in urbanization (by ca 21 300 ha) during 1990–2003 was predominantly due to encroachment into traditionally cultivated land at the fringes of urban centres. Our results accurately quantify the land cover changes and delineate their spatial patterns, demonstrating the utility of Landsat data in analysing landscape dynamics over time. Such information is critical for making efficient and sustainable policies for resource management.     

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

Soil moisture is an important parameter that influences the exchange of water and energy fluxes between the land surface and the atmosphere. Through the simulation by a Soil–Vegetation–Atmosphere Transfer model, Carlson proposed the universal spatial information-based method to determine soil moisture that is insensitive to the initial atmospheric and surface conditions, net radiation, and atmospheric correction. In this study, a practical normalized soil moisture model is established to describe the relationship among the normalized soil moisture (M), the normalized land surface temperature (T*), and the fractional vegetation cover. The dry and wet points are determined using the surface energy balance principle, which has a robust physical basis. This method is applied to retrieve soil moisture for the Soil Moisture-Atmosphere Coupling Experiment campaign in the Walnut Creek watershed, which has a humid climate, and at the Linzestation, which has a semi-arid climate. The validation data are obtained on days of year (DOYs) 182 and 189 in 2002 in the humid region and on DOYs 148 and 180 in 2008 for the semi-arid region; these data collection days are coincident with the overpass of the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus. When the estimates are compared with the in situ measurements of soil water content, the root mean square error is approximately 0.10 m³ m^?3 with a bias of 0.05 m³ m^?3 for the humid region and 0.08 m³ m^?3 with a bias of 0.03 m³ m^?3 for the semi-arid region. These results demonstrate that the practical normalized soil moisture model is applicable in both humid and semi-arid regions.     

Historical and future land-cover changes in the Upper Ganges basin of India

The green revolution represents one of the greatest environmental changes in India over the last century. The Upper Ganges (UG) basin is experiencing rapid rates of change of land cover and irrigation practices. In this study, we investigated the historical rate of change and created future scenario projections by means of 30 m-resolution multi-temporal Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus data of the UG basin. Post-classification change analysis methods were applied to Landsat images in order to detect and quantify land-cover changes in the UG basin. Subsequently, Markov chain analysis was applied to project future scenarios of land-cover change. Fifteen different scenarios were generated based on historic land-cover change. These scenarios diverged in terms of future projections, highlighting the dynamic nature of the changes. This study has shown that between the years 1984 and 2010 the main land-cover change trends are conversion from shrubs to forest (+4.7%), urbanization (+5.8%), agricultural expansion (+1.3%), and loss of barren land (–9.5%). The land-cover change patterns in the UG basin were mapped and quantified, showing the capability of Landsat data in providing accurate land-cover maps. These results, in combination with those derived from the Markov model, provide the necessary evidence base to support regional land-use planning and develop future-proof water resource management strategies.     

Spatio-temporal pattern of urban land cover evolvement with urban renewal and expansion in Shanghai based on mixed-pixel classification for remote sensing imagery

Research into pixel unmixing in remote sensing imagery led to the development of soft classification methods. In this article, we propose a possibilistic c repulsive medoids (PCRMdd) clustering algorithm which attempts to find c repulsive medoids as a minimal solution of a particular objective function. The PCRMdd algorithm is applied to predict the proportion of each land use class within a single pixel, and generate a set of endmember fraction images. The clustering results obtained on multi-temporal Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper plus (ETM+) images of Shanghai city in China reveal the spatio-temporal pattern of Shanghai land use evolvement and urban land spatial sprawl in course of urbanization from 1989 to 2002. The spatial pattern of land use transformation with urban renewal and expansion indicates the urban land use structure is gradually optimized during vigorous urban renewal and large-scale development of Pudong area, which will have an active influence on improving urban space landscape and enhancing the quality of the ecological environment. In addition, accuracy analysis demonstrates that PCRMdd represents a robust and effective tool for mixed-pixel classification on remote sensing imagery to obtain reliable soft classification results and endmember spectral information in a noisy environment.     

Following a site-specific secondary succession in the Amazon using the Landsat CDR product and field inventory data

Secondary forests cover large areas and are strong carbon sinks in tropical regions. They are important for ecosystem functioning, biodiversity conservation, watershed protection, and recovery of soil fertility. In this study, we used the Surface Reflectance Climate Data Record (CDR) product from 16 Thematic Mapper (TM)/Landsat-5 images (1984–2010) to continuously track the secondary succession (SS) of a forest following land abandonment in 1980. Changes in canopy structure and floristic composition were analysed using data from four field inventories (1995, 2002, 2007, and 2012). To characterize variations in brightness, greenness, spectral reflectance, and shadows with the natural regeneration of vegetation, we applied tasselled cap transformations, principal component analysis (PCA), and linear spectral mixture models to the TM datasets. Shade fractions were plotted over time and correlated with the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). Because image texture may reflect the variability of the successional process, eight co-occurrence-based filter metrics were calculated for selected TM bands and plotted as a function of time since abandonment. The successional forest was compared to a nearby primary reference forest (PF) and had differences in the spectral and textural means evaluated using analysis of variance (ANOVA). The results showed increases of 35% and 10.4% over time in basal area and tree height, respectively. Species richness within the assemblage of sampling units increased from 14 to 71 between 1995 and 2012, and this trend was also confirmed using an individual-based rarefaction analysis. Species richness in 2012 was still lower than that observed in the PF site, which presented greater amounts of aboveground biomass (336.4 ± 17.0 ton ha^?1 for PF versus 98.5 ± 21.4 ton ha^?1 for SS in 2012). Brightness and greenness tasselled cap differences between the SS and PF rapidly decreased from 1984 (SS at the age of 4 years) to 1991 (age of 11 years). Brightness also decreased from 1997 to 2003, as indicated by PC1 scores and surface reflectance of the TM bands 4 (near infrared) and 5 (shortwave infrared). Spectral mixture shade fraction increased from young to old successional stages with strata composition and canopy structure development, whereas NDVI and EVI decreased over time. Because EVI was strongly dependent on near infrared reflectance (r = + 0.96), it was also much more strongly correlated with the shade fraction (r = ?0.93) than NDVI. Except for the image texture mean that decreased from young to old successional stages in TM bands 4 and 5, no clear trend was observed in the remaining texture metrics over the time period of vegetation regeneration. Overall, due to structural-floristic and spectral/textural differences with the PF, the SS site was still distinguishable using Landsat data 30 years after land abandonment. Most of the spectral metric means between PF and SS were significantly different over time at 0.01 significance level, as indicated by ANOVA.     

Assessing the capability of broadband indices derived from Landsat 8 Operational Land Imager to monitor above ground biomass and salinity in semiarid saline environments of the Bahía Blanca Estuary,Argentina

In arid and semi-arid ecosystems, salinisation and desertification are the most common processes of land degradation, and satellite data may provide a valuable tool to assess land surface condition and vegetation status. The aim of this study was to evaluate the capability of Landsat 8 OLI (Operational Land Imager) remote sensing information and broadband indices derived from it, to monitor above ground biomass (AGB) and salinity in two different semiarid saline environments (unit a and unit b) in the Bahía Blanca Estuary. Unit a (Ua) is composed of bushes of Cyclolepis genistoides in association with Atriplex undulata and 41% of bare soil. Unit b (Ub) is composed of dense thickets of Allenrolfea patagonica in association with C. genistoides and 34% of bare soil. Pearson’s correlation analyses were performed between field estimates of AGB and salinity (soil salinity and interstitial water salinity) and remote sensing estimates. Satellite data include surface reflectance of individual bands, vegetation indices (NDVI [normalised difference vegetation index], SAVI [soil-adjusted vegetation index], MSAVI2 [modified soil-adjusted vegetation index], NDII [normalised difference infrared index], GNDVI [green normalised difference vegetation index], GRNDI [green-red normalised difference index], OSAVI [optimised soil-adjusted vegetation index], SR [simple ratio]), and salinity indices (SI1, SI2, SI3 [salinity index 1, 2 and 3, respectively] and BI [brightness index]). Correlation analyses involving AGB were performed twice; first considering all months and then again excluding the months with higher soil salinities. In Ua, soil adjusted vegetation indices SAVI and MSAVI2 showed to be suitable to detect changes in the total green AGB and C. genistoides green AGB (the major contributor to total green AGB). After excluding data from December and January (the months with the highest soil salinity), green AGB of A. undulata also showed a significant positive correlation with soil adjusted indices SAVI, MSAVI2 and OSAVI. Although proportionally this species was not a large contributor to the total biomass, it is characterised by a high leaf reflectance, which makes it suitable for biomass retrieval. In Ub, significant positive correlations were obtained between NDVI, SAVI, NDII, OSAVI and SR indices and the AGB green ratio, but significant negative correlations were obtained between A. patagonica red AGB and these vegetation indices. When December and January were excluded from the analysis the negative correlations between vegetation indices NDVI, OSAVI and SR and red AGB remained significant (r = ?0.68, ?0.76 and ?0.7, respectively). The positive correlations between these indices and AGB green ratio (r = 0.73, 0.78 and 0.75, respectively) remained significant as well. Significant negative correlations were also found between NDVI, NDII, GNDVI, OSAVI and SR indices and field salinity estimates. As soil salinisation induces A. patagonica reddening, red AGB and soil salinity covariate in the field, and the negative correlation with vegetation indices may be useful to retrieve information on both variables combined, which are indicative of water stress. Correlation analysis between field estimates of salinity and spectral salinity indices showed significant positive correlation for all the tested indices. The obtained results highlight the importance of a thoughtful selection of remote sensing indices to account for changes in vegetation biomass, especially in arid and semiarid environments particularly sensitive to desertification and salinisation. Also, ground truth cannot be overlooked, and field work is necessary to test index performance in every case.     

Mapping of land degradation from space: a comparative study of Landsat ETM+ and ASTER data

The purpose of this study is to compare the role of spectral and spatial resolutions in mapping land degradation from space‐borne imagery using Landsat ETM+ and ASTER data as examples. Land degradation in the form of salinization and waterlogging in Tongyu County, western Jilin Province of northeast China was mapped from an ETM+ image of 22 June 2002 and an ASTER image recorded on 24 June 2001 using supervised classification, together with several other land covers. It was found that the mapping accuracy was achieved at 56.8% and higher for moderately degraded (e.g. salinized) farmland, and over 80% for severely degraded land (e.g. barren) from both ASTER and ETM+ data. The spatial resolution of the ASTER data exerts only a negligible effect on the mapping accuracy. The 30 m ETM+ outperforms the ASTER image of both 15 m and 30 m resolution in consistently generating a higher overall accuracy as well as a higher user's accuracy for barren land. The inferiority of ASTER data is attributed to the highly repetitive spectral content of its six shortwave infrared bands. It is concluded that the spectral resolution of an image is not as important as the information content of individual bands in accurately mapping land covers automatically.     

Geospatial variability in the egg raft distribution and abundance of Culex pipiens and Culex restuans in Urbana‐Champaign,Illinois

The aim of this study was to determine whether areas of high Culex pipiens and Culex restuans abundance in an urban environment, based on fixed oviposition surveillance sites, corresponded to remotely sensed data. A land use land cover (LULC) classification, based on Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) data acquired in July 2003 and Landsat‐5 TM data from July 1991, was compared to the abundance of Culex egg rafts in Urbana‐Champaign, East‐Central Illinois. We performed a maximum likelihood, unsupervised classification and generated three land cover classifications: urban, non‐urban and water. Ground coordinates of the Culex surveillance sites were overlaid onto LULC maps with 10 m² grid cells. The grid was stratified based on levels of drainage: 0 = poorly drained and 1 = well‐drained. Total LULC change from 1991 to 2003 in the Urbana‐Champaign study site was relatively low, at 12.1%. The most frequent LULC category was maintained urban. The egg raft rate was significantly higher in urban LULC habitats. Remote stratification of the urban land cover using QuickBird visible and near‐infrared (NIR) data revealed that high‐density canopy coverage was most frequently associated with high Culex abundance in oviposition traps. We demonstrate that optical remote sensing can identify land use parameters associated with high Culex oviposition.     

An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes,reservoirs, and estuaries

ABSTRACT

The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34°C in lake pixels >180 m from land, 4.89°C at the land-water boundary, and 1.11°C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.     

植被指数在城市绿地信息提取中的比较研究

利用植被指数从TM 影像中提取植被, 从技术与经济成本方面综合考虑, 是一个比较好的手段。但在城市绿地信息提取中, 由于城市下垫面的特殊性和植被指数的繁多, 究竟哪些植被指数最适合于城市绿地, 还仍然是一个急待解决的难点问题。通过以上海中心城区为研究靶区, 利用单因子方差分析与多重比较对植被指数在城市绿地信息提取中的优劣进行比较研究, 得到如下结论: ①TM 影像经过植被指数计算处理后, 植被信息确实得到了增强, 但不同的植被指数也有所差别。如果以区分植被与非植被之间差异程度做标准, 那么植被指数提取植被由优到劣则依次是GEMI、RDVI、NDVI、GNDVI、RVI、TNDVI、DVI、EVI 和TGDVI。②植被指数基本能从TM 影像提取植被, 但把植被再细分的效果不是太好。总体来看, 除EVI 和TGDVI 以外, 植被指数能较好的区分草地与农田; 而树林与农田及草地与树林的区分则因不同的植被指数有所差异。区分草地与树林较好的是EVI, 区分草地与农田较好的是GEMI, 区分树林与农田较好的是TNDVI。③植被指数不但细分植被的效果不是太理想, 而且也不能很好的细分非植被地物。总体来说, 所有的植被指数都很难把建筑物与道路区别开, 尤其TGDVI、DVI 和EVI 更是如此。不过NDVI、GNDVI、TNDVI 和GEMI 能很好地把水体从TM 影像中提取出来, 其余的植被指数则只能区分植被与非植被, 不能再进一步的区分非植被地物。     

Drought monitoring of the wetland in the Tumen River Basin between 1991 and 2016 using Landsat TM/ETM+

Wetland areas are known as ‘the kidneys of the Earth’ because they provide important functions towards stabilizing the environment, long-term protection of water sources, effectively minimizing sediment loss, purifying surface water from industrial and agricultural pollutants, and enhancing aquifer recharge. The condition of water supply in wetlands directly affects the growth of wetland plants and local biodiversity. Therefore, drought monitoring is vital in wetlands. In this study, Vegetation Temperature Condition Index (VTCI) derived from normalized difference vegetation index (NDVI) and land surface temperature (LST) is used to observe the drought status of the wetland in the cross-border (China and North Korea) Tumen River Basin from 1991 to 2016. For this purpose, the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data for six periods were used for the analysis. Soil moisture maps acquired from the China Meteorological Administration Land Data Assimilation System Version 1.0 (CLDAS-V1.0) were then introduced for validating the reliability of the drought monitoring method. The results showed that most areas with a normal moisture level (decreased 25.8%) began experiencing slight drought (increased 29.7%). The coefficient of determination (R²) between VTCI and soil moisture showed values of 0.69, 0.32, and 0.2 for 0–5 cm, 0–10 cm, and 10–20 cm thicknesses, respectively. Although climate change probably contributes to the formation of drought by decreasing precipitation (50 mm decrease in Chinese section) and increasing temperature (0.5°C increase in North Korean section), human activities such as surges in daily water consumption appear as the main threats that leading to droughts in this wetland.     

Effects of sediments and coloured dissolved organic matter on remote sensing of chlorophyll-a using Landsat TM/ETM+ over turbid waters

Remote sensing of chlorophyll-a is challenging in water containing inorganic suspended sediments (i.e. non-volatile suspended solids, NVSS) and coloured dissolved organic matter (CDOM). The effects of NVSS and CDOM on empirical remote-sensing estimates of chlorophyll-a in inland waters have not been determined on a broad spatial and temporal scale. This study evaluated these effects using a long-term (1989–2012) data set that included chlorophyll-a, NVSS, and CDOM from 39 reservoirs across Missouri (USA). Model comparisons indicated that the machine-learning algorithm BRT (boosted regression trees, validation Nash–Sutcliffe coefficient = 0.350) was better than linear regression (validation Nash–Sutcliffe coefficient = 0.214) for chlorophyll-a estimate using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery. Only a small proportion of BRT model residuals could be explained by sediments or CDOM, and the observed trends in BRT residuals were different from the theoretical effects expected from NVSS and CDOM. Our results also indicated a small systematic bias by the BRT model, but it was not likely caused by NVSS or CDOM.     

Measuring fire severity using UAV imagery in semi-arid central Queensland,Australia

Remote-sensing methods for fire severity mapping have traditionally relied on multispectral imagery captured by satellite platforms carrying passive sensors such as Landsat Thematic Mapper /Enhanced Thematic Mapper Plus or Moderate Resolution Imaging Spectroradiometer. This article describes the analysis of high spatial resolution Unmanned Aerial Vehicle (UAV) imagery to assess fire severity on a 117 ha experimental fire conducted on coal mine rehabilitation in an open woodland environment in semi-arid Central Queensland, Australia. Three band indices, Excess Green Index, Excess Green Index Ratio, and Modified Excess Green Index, were used to derive differenced (d) fire severity maps from UAV data. Fire severity data sets derived from aerial photograph interpretation were used to assess the utility of employing UAV technology to determine fire severity impacts. The dEGI was able to separate high severity, low severity, and unburnt areas with an overall classification accuracy of 58% and Kappa statistic of 0.37; outperforming the dEGIR (overall accuracy 55%, Kappa 0.31) and the dMEGI (overall accuracy 38%, Kappa 0.06). Classification accuracy increased for all indices when canopy shadows were masked, with dEGI improving to an overall accuracy of 68% and 0.48 Kappa. The McNemar’s test indicated that there was no significant difference between the classification accuracies for dEGI and dEGIR (p < 0.05). The test also demonstrated that dMEGI was significantly lower in accuracy compared to dEGI and dEGIR (p < 0.05). We quantified the proportion of burnt area within each severity class and calculated that 32% of the site was burnt at high severity, 34% was burnt at low severity, and 34% of the block was unburnt due to the patchy nature of the fire. We discuss the UAV-specific errors associated with fire severity mapping, and the potential for UAVs to assist land managers to assess the extent and severity of fire and subsequent recovery of burnt ecosystems at local scales (10⁴m²–1 km²).     

Landscape changes of the Ejin Delta in the Heihe River Basin in Northwest China from 1930 to 2010

The Heihe River Basin is located in the arid and semi-arid region of Northwest China; during the past 80 years, this basin has experienced water resource competition between irrigation agriculture and ecological demand in its middle and lower reaches, respectively. The land cover of the Ejin Delta in the lower reaches of the Heihe River Basin was interpreted and analysed for four different periods using a map created by Dr Sven Hedin in the 1930s, Corona satellite images taken in 1961, and Landsat Thematic Mapper (TM) images taken in 2000 and 2010. Overall, the results show that (1) the coarse resolution of the 1930s map increased the uncertainty of analysis in the study area and (2) the river area in the Ejin Delta decreased by 91.0% from the 1930s to 2000. In addition, two major terminal lakes, Gaxun Nuur Lake and Sogo Nuur Lake, dried up in 1961 and 1992, respectively, and the area of Populus euphratica decreased by 76.1% from the 1930s to 2000. Most reeds were overtaken by shrubs between the 1930s and 1961, which caused the area of reeds to decrease from 3481 to 1332 km² and the area of shrubs to increase from 805 to 2795 km². From the 1930s to 2000, the desert and alkaline land areas increased by 42.2% and 52.4%, respectively. (3) After the water transfer project was implemented in 2000, the area of Sogo Nuur Lake recovered to 40.58 km² by 2010. The areas of Populus euphratica, shrubland, and reedland showed a recovering trend, with increases of 4.5%, 6.5%, and 43.5%, respectively, by 2010. The desert and alkaline land areas decreased by 4.2% and 15.2%, respectively, by 2010. The area of cultivated land increased from 25 km² in 1961 to 85 km² in 2000 and rapidly approached 160 km² in 2010. These changes over time indicated that the ecological habitat in the Ejin Delta deteriorated between the 1930s and 2000. However, the water transfer project effectively changed the degradation trend.     

Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: impact of urbanization

ABSTRACT

The present study aims to quantify spatial relationship of land-use and land-cover (LULC) changes and land surface temperature (LST) using remote-sensing and geographical information system techniques over 10 major metropolitan cities of India. For this purpose, Landsat 7 Enhanced Thematic Mapper Plus images of these cities during winter period from 2001 to 2013 are used. Statistical analysis of the LULC classification has shown overall accuracy ranging between 85% and 88%. The LULC classification and estimated LST using the satellite imageries reveals the presence of multiple urban heat islands (UHIs) and their increase in number in all cities. Significant increase in built-up/urban areas are noticed at the expense of vegetated lands and barren lands over Lucknow, Nagpur, and Jaipur, whereas in Hyderabad and Bengaluru the built-up area and the dry/barren lands are observed to be increasing, at the expense of crop/grass lands. Higher UHI intensities in the range of 8.9–10.3°C are noticed over Mumbai, Nagpur, and Hyderabad compared to the other cities. Higher temperature zones (hotspots) are found to be increasing in the built-up area as well as in barren lands. Varying increase in UHI intensity among the cities is noticed which may have implications in the regional climate over the cities.     

Mapping bottom depth and albedo in coastal waters of the South China Sea islands and reefs using Landsat TM and ETM+ data

Optical models for the retrieval of shallow water bottom depth and albedo using multispectral data usually require in situ water depth data to tune the model parameters. In the South China Sea (SCS), however, such in situ data are often lacking or obsolete (perhaps from half a century ago) for most coastal waters around its islands and reefs. Here, we combine multispectral data collected by MODIS and Landsat to estimate bottom depth and albedo for four coral reef regions in the SCS, with results partially validated by some scarce in situ data. The waters in these remote regions are oligotrophic whose optical properties can be well derived from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements when the waters are optically deep. The MODIS-derived optical properties are used to estimate the water column attenuation to the Landsat measurements over shallow waters, thus eliminating the requirement of model tuning using field measured water depths. The model is applied to four Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images covering Pratas Atoll, Woody Island, Scarborough Shoal, and North Danger Reefs. The retrieved bathymetry around Pratas Atoll and North Danger Reefs are validated with some in situ data between 1 and 25 m. The relative difference and root mean square difference between the two measurements were 17% and 1.6 m, for Pratas Atoll and 11% and 1.1 m for North Danger Reefs, respectively. These results suggest that the approach developed here may be extended to other shallow, clear waters in the SCS.     

Land-use change analysis in Yulin prefecture,northwestern China using remote sensing and GIS

Rapid land-use change has taken place in many arid and semi-arid regions of China such as Yulin prefecture over the last decade due to rehabilitation measures. In this paper, land-use change dynamics were investigated by the combined use of satellite remote sensing and geographical information systems (GIS). Our objectives were to determine land-use transition rates among land-use types in Yulin prefecture over 14 years from 1986-2000 and to quantify the changes of various landscape metrics using FRAGSTATS, the spatial pattern analysis program for Categorical Maps. Using 30-m resolution Landsat Thematic Mapper (TM) data from the Institute of Remote Sensing Application (IRSA) in China, we classified images into six land-use types: cropland, forestland, grassland, water, urban and/or built-up land, and barren land. Significant changes in land-use occurred within the area over the study period. The results show the significant decrease in barren land was mainly due to conversion to grassland. Cropland increased by 3.39%, associated with conversions from grassland and barren land. The landscape has become more continuous, clumped and more homogeneous. The study demonstrates that the integration of satellite remote sensing and GIS was an effective approach for analysing the direction, rate and spatial pattern of land-use change.     

Improvements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification

The accuracy of conventional land use classification of irrigated agriculture from optical satellite images using maximum likelihood supervised classification was compared with a classification based on multistage maximum likelihood supervised classification. In the multistage maximum likelihood classification series of sub-classifications were carried out which included masking and/or omitting certain crops from the classifications. These series of classifications improved the identification of individual crops/land use types. The output from the optimum sub-classifications were stacked to give an overall crop types/land use map. When the multistage classification was tested against a single stage classification on a large irrigation scheme in Central Asia the final accuracy of crop/land use classification increased from 85% to 94%. Field verification confirmed the accuracy at 93.5%. These results were achieved with a single Landsat 7 Enhanced Thematic Mapper (ETM+) sensor dataset as of 2 August 1999 over an area of 38.5?km².