Thematic Mapper Data Research Articles

Comparison of in-Situ Hyperspectral and Landsat ETM+ Data for Chlorophyll-a Mapping in Case-II Water (Krishnarajapuram Lake, Bangalore)

Water quality of inland water bodies is most important for humankind as it has direct impact on health, environment, agriculture etc. Estimation of chlorophyll-a (Chl-a) content provides phytoplankton concentration, a measure of water quality. The estimation of Chl-a using laboratory method consumes chemicals and takes long time. On the other hand, spectral reflectance methods viz. in-situ hyperspectral spectroradiometer measurements and satellite remote sensing are less expensive and provide results faster. The laboratory and spectroradiometer methods require somebody to sail into the lake to collect samples or measure the spectral reflectance, whereas the satellite remote sensing collects data from space, provides more samples and synoptic coverage. The aim of this study is to estimate the chlorophyll-a concentration using in-situ hyperspectral spectroradiometer data and Landsat-7 Enhanced Thematic Mapper (ETM+) data, compare the results and map chlorophyll concentration of the lake with suitable data. The relation between band reflectance ratios and measured values of Chl-a was established using regression analysis. That relation was used to map the Chl-a concentration of the lake. It was found that spectroradiometer data provided better correlation with measured values of Chl-a than Multispectral data. As satellite data provide full coverage, mapping of Chl-a concentration of lake was done with it.

Analysis of different polarimetric target decomposition methods in forest density classification using C band SAR data

ABSTRACTThe ability of synthetic aperture radar (SAR) C-band microwave energy to penetrate within forest vegetation makes it possible to extract information on crown components, which in turn gives a better approximation of relative canopy density than optical data-derived canopy density. Many studies have been reported to estimate forest biomass from SAR data, but the scope of C-band SAR in characterizing forest canopy density has not been adequately understood with polarimetric techniques. Polarimetric classification is one of the most significant applications of polarimetric SAR in remote sensing. The objective of the present study was to evaluate the feasibility of different polarimetric SAR data decomposition methods in forest canopy density classification using C-band SAR data. Landsat (Land Satellite) 5 TM (Thematic Mapper) data of the same area has been used as optical data to compare the classification result. RADARSAT (Radar Satellite)-2 image with fine quad-pol obtained on 27 October 2011 over tropical dry forests of Madhav National Park, India, was used for the analysis of full polarimetric data. Six decomposition methods were selected based on incoherent decomposition for generating input images for classification, i.e. Huynen, Freeman and Durden, Yamaguchi, Cloude, Van zyl, and H/A/α. The performance of each decomposition output in relation to each land cover unit present in the study area was assessed using a support vector machine (SVM) classifier. Results show that Yamaguchi 4-component decomposition (overall accuracy 87.66% and kappa coefficient (κ) 0.86) gives better classification results, followed by Van Zyl decomposition (overall accuracy 87.20% and κ 0.85) and Freeman and Durden (overall accuracy 86.79% and κ 0.85) in forest canopy density classification. Both model-based decompositions (Freeman and Durden and Yamaguchi4) registered good classification accuracy. In eigenvector or eigenvalue decompositions, Van zyl registered the second highest accuracy among different decompositions. The experimental results obtained with polarimetric C-band SAR data over a tropical dry deciduous forest area imply that SAR data have significant potential for estimating canopy density in operational forestry. A better forest density classification result can be achieved within the forest mask (without other land cover classes). The limitations associated with optical data such as non-availability of cloud-free data and misclassification because of gregarious occurrence of bushy vegetation such as Lantana can be overcome by using C-band SAR data.

Plot-level aboveground woody biomass modeling using canopy height and auxiliary remote sensing data in a heterogeneous savanna

Remote sensing studies aiming at assessing woody biomass have demonstrated a strong relationship between canopy height and plot-level aboveground biomass, but most of these studies focused on closed canopy forests. To date, a few studies have examined the strength and reliability of this relationship using large footprint lidar in savannas. Furthermore, there have been few studies of appropriate methods for the comparison of models that relate aboveground biomass to canopy height metrics without consideration of variation in species composition (generic models) to models developed for individual species composition or vegetation types. We developed generic models using the classical least-squares regression modeling approach to relate selected canopy height metrics to aboveground woody biomass in a savanna landscape. Hierarchical Bayesian analysis (HBA) was then used to explore the implications of using generic or composition-specific models. Our study used the estimates of aboveground biomass from field data, canopy height estimates from airborne discrete return lidar, and a proxy for canopy cover (the Normalized Difference Vegetation Index) from Landsat 5 Thematic Mapper data, collected from the oak savannas of Tejon Ranch Conservancy in Kern County, California. Models were developed and analyzed using estimates of canopy height and aboveground biomass calculated at the level of 50-m diameter plots, comparable with footprint diameter of existing large footprint spaceborne lidar data. The two generic models that incorporated canopy cover proxies performed better than one model that did not use canopy cover information. From the HBA, we found out that for all models both the intercept and slope had interspecific variability. The valley oak dominated plots consistently had higher slopes and intercepts, whereas the plots dominated by blue oaks had the lowest. However, the intercept and slope values of the composition-specific models did not differ much from the generic (overall) values and their 95% credible intervals (CIs) overlapped the generic mean values. We conclude that the narrow range of the distribution and the overlap of the CIs of the composition-specific and generic parameters suggest that scaling rules do exist for savannas. The distribution of the posterior densities of the differences between composition-level and generic-level parameter values showed high support for the use of generic parameters, suggesting that all three models are applicable across the range of compositions in this study. Therefore, in this case, the choice of method depends more on secondary considerations such as data availability and scale of analyses.

Simulating the effects of land use changes on soil erosion using RUSLE model

The aim of this paper is to evaluate the impacts of land use change on soil loss. Soil loss was quantified using the revised universal soil loss equation model in Darabkola catchment. Land use maps of 1992, 1998 and 2012 were derived from Landsat Thematic Mapper data. The mean annual soil loss was therefore determined for these years. The results showed open-canopy forest area decreased by 36% between 1992 and 1998. Likewise, the decreasing trend of forest lands which are near to residential areas has continued from 1795 ha in 1998 to 1765 ha in 2012. Also the results indicate that the maximum annual soil loss ranged from 5.06, 6.19 and 15.23 ton h−1 y−1 in 1992, 1998 and 2012, respectively. Also, by assuming that all watershed conditions and land uses be constant in the future, then the area of close- and open-canopy forest and dry agricultural lands will be 23.23, 2.88 and 29.89 ha in 2040, respectively.

湿地土壤全氮和全磷含量高光谱模型研究

PDF HTML阅读 XML下载 导出引用 引用提醒 湿地土壤全氮和全磷含量高光谱模型研究 DOI: 10.5846/stxb201501230186 作者: 作者单位: 辽宁师范大学海洋经济与可持续发展研究中心,辽宁师范大学海洋经济与可持续发展研究中心 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41271421）；教育部人文社会科学研究规划基金项目（14YJA630064） Estimating the total nitrogen and total phosphorus content of wetland soils using hyperspectral models Author: Affiliation: Center for Studies of Marine Economy and Sustainable Development,Liaoning Normal University,Center for Studies of Marine Economy and Sustainable Development,Liaoning Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:氮磷是湿地生态系统土壤中的重要营养元素，其对湿地植被生长、湿地生态系统生产力、区域富营养化变化、湿地环境生态净化功能等具有重要的影响作用。研究氮磷营养物质在湿地土壤中的分布变化特征，对湿地生态系统评估、恢复和管理具有重要的意义。以中国高纬度地区面积最大的滨海芦苇湿地——盘锦湿地为研究区，采用不同建模方法（再抽样多元逐步回归模型bootstrap SMLR和再抽样偏最小二乘回归模型bootstrap PLSR）和光谱变换技术（包络线去除CR、光谱一阶微分FD和光谱倒数的对数LR），分别建立了湿地土壤全氮和全磷含量的估算模型。基于湿地土壤实测光谱，模拟高光谱Hyperion数据和多光谱TM数据，在此基础上进行湿地土壤营养元素含量估算。对比所建反演模型的估算精度，探讨高光谱遥感技术对湿地土壤营养元素组分的估算能力和适用性。研究结果表明：bootstrap PLSR相比于bootstrap SMLR建模方法，其对研究区湿地土壤全氮和全磷含量的估算获得了较高精度；对盘锦湿地土壤全氮含量的估算，最高估算精度产生于CR光谱变换技术结合bootstrap PLSR建模；对湿地土壤全磷含量的估算，最高估算精度产生于原光谱数据结合bootstrap PLSR建模；模拟高光谱数据Hyperion对湿地土壤全氮和全磷含量的估算精度均高于模拟多光谱数据TM，模拟Hyperion的估算精度更接近于实测光谱的估算精度。 Abstract:Nitrogen and phosphorus in wetland soils are important limiting nutrients for plant growth, maximum photosynthetic rate and capacity, and net primary productivity. They have been found to be significantly involved in the estuarine eutrophication and environmental purification of wetland ecosystem. The research may be focused on distributing and changing characteristics of nitrogen and phosphorus in wetland soils. It is important for the evaluation, restoration, and management of wetland ecosystems. Our study area is located in the Panjin wetland (40°45'-41°10'N, 121°45'-122°00'E), which is a part of the Shuangtaihekou National Nature Reserve Administration. Panjin wetland is the largest coastal reed wetland situated in the high-latitude areas of China. The laboratory measurements of total nitrogen, total phosphorus, and spectral reflectance for surface soil samples had been conducted. Different modeling methods, such as bootstrap stepwise multiple linear regression (SMLR), bootstrap partial least square regression (PLSR), and spectral transformation techniques, such as continuum removal (CR), first difference derivative (FD), and log transformed spectra (LR), were used to develop the estimation models of total nitrogen and total phosphorous in wetland soils. Based on the simulated hyperspectral Hyperion data and multispectral Thematic Mapper (TM) data of the wetland soils, soil nitrogen and phosphorous contents were estimated, respectively. Subsequently, the estimated accuracies of the developed models were compared, and thus, the ability and suitability of estimating nitrogen and phosphorous components in wetland soils using hyperspectral technologies were explored. The results indicated that bootstrap PLSR achieved higher accuracies of estimating the total nitrogen and total phosphorous content of wetland soils in the study area than did bootstrap SMLR. The spectral transformed technique of CR used in combination with the modeling method of bootstrap PLSR yielded the highest estimation accuracy for the prediction of the total nitrogen content of soils collected from Panjin wetland. The original spectral data combined with bootstrap PLSR produced the highest estimation accuracy to predict the total phosphorous content in wetland soils. Simulated hyperspectral Hyperion data attained higher accuracies of estimating total nitrogen and total phosphorous in wetland soils compared to simulated multispectral TM data. The estimation accuracies of the simulated Hyperion were closer to those of the measured spectra. The estimation accuracy of the total nitrogen content achieved from the measured spectra, simulated hyperspectral Hyperion, and multispectral TM were all higher than those of the total phosphorous content of the same soils. 参考文献 相似文献 引证文献

Change Detection of Land Use and Land Cover over a Period of 20 Years in Papua New Guinea

People have an inherent tenacity to throng coastal regions in pursuit of better living conditions. As such the brisk dynamism of land use/land cover activities in a coastal region becomes obvious. The former keeps changing rapidly due to burgeoning population. A digital change detection analysis is performed with the help of Geographic Information System (GIS) on the Remote Sensing data spanning over last 20 years, complemented by in-situ data and ground truth information. This current research briefly endeavours to find out the nature of change happening in the major three coastal cities of Papua New Guinea (PNG), namely Alotau, capital of Milnebay province; Lae, capital of Morobe province and Port Moresby, capital of Papua New Guinea. Changes in land use and land cover that took place over 20 years have been recorded using Landsat 5 thematic mapper (TM) data of 1992 and Landsat 8 operational land imager (OLI) data. Land use and land cover maps of 1992, and 2013/14, and change detection matrix of 1992-2013/14 are derived. Results show an immensely sprawling urban landscape, evincing about five times growth during 1992 to 2014. At the same time “natural forests” dwindled by 444.96 hectares in Alotau, 6977.25 hectares in Lae and “mangrove” and “grass/shrub land” decreased by 127.78 and 4859.39 hectares respectively around Port Moresby. The above changes owe to ever increasing population pressure, land tenure shift, agriculture and industrial development.

PALSAR-FBS L-HH Mode and Landsat-TM Data Fusion for Geological Mapping

Characterized by lithological diversity and rich mineral resources, Benshangul-Gumuz National Regional State located in Asosa Zones, Western Ethiopia has been investigated for geological mapping and morpho-structural lineaments extraction using PALSAR (Phased Array type L-band Synthetic Aperture Radar ) Fine Beam Single (FBS) L-HH polarization and Landsat-5 TM (Thematic Mapper ) datasets. These data were preprocessed to retrieve ground surface reflectance and backscatter coefficients. To overcome the geometry acquisition between the two sensors, they were geometrically and topographically rectified using ASTER-V2 DEM. Intensity-Hue-Saturation, directional filters and automatic lineaments extraction were applied on the datasets for lithological units’ discrimination and structural delimitation for potential mineral exploration. The obtained results showed good relationship among the topographic morphology, rock-substrate, structural variations properties, and drainage network. The spectral variations were easily associated with lithological units. Likewise, the morpho-structural information highlighted in the PALSAR image was visible without altering the radiometric integrity of the details in TM bands through the fusion process. Moreover, predominant lineaments directions trending NE-SW, NS, and NW-SE were identified. Results of this study highlighted the importance of the PALSAR FBS L-HH mode and TM data fusion to enhance geological features and lithological units for mineral exploration particularly in tropical zones.

Mapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data

Mapping cropland distribution over large areas has attracted great attention in recent years, however, traditional pixel-based classification approaches produce high uncertainty in cropland area statistics. This study proposes a new approach to map fractional cropland distribution in Mato Grosso, Brazil using time series MODIS enhanced vegetation index (EVI) and Landsat Thematic Mapper (TM) data. The major steps include: (1) remove noise and clouds/shadows contamination using the Savizky–Gloay filter and temporal resampling algorithm based on the time series MODIS EVI data; (2) identify the best periods to extract croplands through crop phenology analysis; (3) develop a seasonal dynamic index (SDI) from the time series MODIS EVI data based on three key stages: sowing, growing, and harvest; and (4) develop a regression model to estimate cropland fraction based on the relationship between SDI and Landsat-derived fractional cropland data. The root mean squared error of 0.14 was obtained based on the analysis of randomly selected 500 sample plots. This research shows that the proposed approach is promising for rapidly mapping fractional cropland distribution in Mato Grosso, Brazil.

Remote sensing of the impacts of construction in coastal waters on suspended particulate matter concentration – the case of the Yangtze River delta, China

This study investigates the capability of high-spatial resolution Landsat Thematic Mapper (TM) data to sense and document suspended particulate matter concentration (SPMC) variability resulting from the influence of large structures in coastal waters. Two bridges, located in the coastal waters of the Yangtze River delta, are used as examples. A new SPMC inverse model, relating SPMC to TM optical properties through linear regression in the red and near-infrared bands, is developed. In total, 780 samples and 30 transects taken between 2006 and 2011 were used to compare and contrast SPMC at locations upstream and downstream of the bridges. The comparisons show: (i) within a distance of 0.3 km downstream from the bridges, SPMC mostly increased by 3–60% (8.40–176.29 mg l–1); (ii) when SPMC values upstream were low (<300 mg l–1), the increase in SPMC extended to 3.0–6.5 km downstream; (iii) under conditions of high turbidity (>400 mg l–1) upstream, decreases in SPMC were observed in 0.3–6.5 km downstream. The bridges influenced SPMC by blocking the transport of upstream suspended particulate matter (deposition) and through stirring of the sediments near the base of their piers (resuspension). The results can be generalized to other offshore engineering structures.

The application of ensemble techniques for land-cover classification in arid lands

Robust classification approaches are required for accurate classification of complex land-use/land-cover categories of desert landscapes using remotely sensed data. Machine-learning ensemble classifiers have proved to be powerful for the classification of remotely sensed data. However, they have not been evaluated for classifying land-cover categories in desert regions. In this study, the performance of two machine-learning ensemble classifiers – random forests (RF) and boosted artificial neural networks – is explored in the context of classification of land use/land cover of desert landscapes. The evaluation is based on the accuracy of classification of remotely sensed data, with and without integration of ancillary data. Landsat-5 Thematic Mapper data captured for a desert landscape in the north-western coastal desert of Egypt are used with ancillary variables derived from a digital terrain model to classify 13 different land-use/land-cover categories. Results show that the two ensemble methods produce accurate land-cover classifications, with and without integrating spectral data with ancillary data. In general, the overall accuracy exceeded 85% and the kappa coefficient (κ) attained values over 0.83. The integration of ancillary data improved the performance of the boosted artificial neural networks by approximately 5% and the random forests by 9%. The latter showed overall higher accuracy; however, boosted artificial neural networks showed better generalization ability and lower overfitting tendencies. The results reveal the merit of applying ensemble methods to integrated spectral and ancillary data of similar desert landscapes for achieving high classification accuracies.

Annual minimum temperature variations in early 21st century in Punjab, Pakistan

Climate change is a key emerging threat to the global environment. It imposes long lasting impacts both at regional and national level. In the recent era, global warming and extreme temperatures have drawn great interest to the scientific community. As in a past century considerable increase in global surface temperatures have been observed and predictions revealed that it will continue in the future. In this regard, current study mainly focused on analysis of regional climatic change (annual minimum temperature trends and its correlation with land surface temperatures in the early 21st century in Punjab) for a period of 1979–2013. The projected model data European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) has been used for eight Tehsils of Punjab i.e., annual minimum temperatures and annual seasonal temperatures. Trend analysis of annual minimum and annual seasonal temperature in (Khushab, Noorpur, Sargodha, Bhalwal, Sahiwal, Shahpur, Sillanwali and Chinoit) tehsils of Punjab was carried out by Regression analysis and Mann–Kendall test. Landsat 5 Thematic Mapper (TM) data was used in comparison with Model data for the month of May from the years 2000, 2009 and 2010. Results showed that no significant trends were observed in annual minimum temperature. A significant change was observed in Noorpur, Bhalwal, Shahpur, Sillanwali, Sahiwal, Chinoit and Sargodha tehsils during spring season, which indicated that this particular season was a transient period of time.

CHROMITITE PROSPECTING USING LANDSAT TM AND ASTER REMOTE SENSING DATA

Abstract. Studying the ophiolite complexes using multispectral remote sensing satellite data are interesting because of high diversity of minerals and the source of podiform chromitites. This research developed an approach to discriminate lithological units and detecting host rock of chromitite bodies within ophiolitic complexes using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Thematic Mapper (TM) satellite data. Three main ophiolite complexes located in south of Iran have been selected for the study. Spectral transform techniques, including minimum noise fraction (MNF) and specialized band ratio were employed to detect different rock units and the identification of high-potential areas of chromite ore deposits within ophiolitic complexes. A specialized band ratio (4/1, 4/5, 4/7) of ASTER, MNF components and Spectral Angle Mapper (SAM) on ASTER and Landsat TM data were used to distinguish ophiolitic rock units. Results show that the specialized band ratio was able to identify different rock units and serpentinized dunite as host rock of chromitites within ophiolitic complexes, appropriately. MNF components of ASTER and Landsat TM data were suitable to distinguish ophiolitic rock complexes at a regional scale. The integration of SAM and Feature Level Fusion (FLF) used in this investigation discriminated the ophiolitic rock units and prepared detailed geological map for the study area. Accordingly, high potential areas (serpentinite dunite) were identified in the study area for chromite exploration targets.The approach used in this research offers the image processing techniques as a robust, reliable, fast and cost-effective method for detecting serpentinized dunite as host rock of chromitite bodies within vast ophiolite complexes using ASTER and Landsat TM satellite data.

Multi-scale crown closure retrieval for moso bamboo forest using multi-source remotely sensed imagery based on geometric-optical and Erf-BP neural network models

This article focuses on retrieving the multi-scale crown closure (CC) of Moso bamboo forest using Système Pour l’Observation de la Terre (SPOT5) and Landsat Thematic Mapper (TM) satellite remotely sensed imagery based on the geometric-optical model and the artificial neural network (ANN) model. CC at local scale was first retrieved using the Li-Strahler geometric-optical model (LSGM) and images from an unmanned aerial vehicle (UAV). Then, multi-scale CC was retrieved using the Erf-BP model (a kind of back-propagation (BP) feed-forward neural network, which takes a Gaussian error function (Erf) as an activation function of the hidden layer) based on a combination of SPOT5 and Landsat TM images. The results show that by combining multi-source remotely sensed data, the CC of Moso bamboo forest can be retrieved at the local region, township area, and county scale with high accuracy using the Erf-BP model. Estimated values have a linear relationship with the observed values at a significance level of 0.05. The highest accuracy of the retrieval of CC (referred to as LSGM-UAV-CC) was observed at the local region based on LSGM and UAV, with the coefficient of determination (R2) of 0.63, followed by that at the township area with an R2 of 0.0.55 based on LSGM-UAV-CC and SPOT5 data using the Erf-BP model (Erf-BP-SPOT5-CC), and that at the county scale with an R2 of 0.54 based on Erf-BP-SPOT5-CC and Landsat TM data using the Erf-BP model (Erf-BP-TM-CC).

Integration of ASTER and landsat TM remote sensing data for chromite prospecting and lithological mapping in Neyriz ophiolite zone, south Iran

AbstractChromite deposits in Iran are located in the ophiolite complexes, which have mostly podiform types and irregular in their settings. Exploration for podiform chromite deposits associated with ophiolite complexes has been a challenge for the prospectors due to tectonic disturbance and their distribution patterns. Most of Iranian ophiolitic zones are located in mountainous and inaccessible regions. Remote sensing approach could be applicable tool for choromite prospecting in Iranian ophiolitic zones with intensely rugged topography, where systematic sampling and conventional geological mapping are limited. In this study, Landsat Thematic Mapper (TM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data were used for chromite prospecting and lithological mapping in the Neyriz ophiolitic zone in the south of Iran. Image transformation techniques, namely decorrelation stretch, band ratio and principal component analysis (PCA) were applied to Landsat TM and ASTER data sets for lithological mapping at regional scale. The RGB decorrelated image of Landsat TM spectral bands 7, 5, and 4, and the principal components PC1, PC2 and PC3 image of ASTER SWIR spectral bands efficiently showed the occurrence of major lithological units in the study area at regional scale. The band ratios of 5/3, 5/1, 7/5 applied on ASTER VNIR‐SWIR bands were very useful for discriminating most of rock units in the study area and delineation of the transition zone and mantle harzburgite in the Neyriz ophiolitic complex. Spectral Angle Mapper (SAM) technique was implemented to ASTER VNIR‐SWIR spectral bands for detecting minerals of rock units and especially delineation of the transition zone and mantle harzburgite as potential zones with high chromite mineralization in the Neyriz ophiolitic complex. The integration of information extracted from the image processing algorithms used in this study mapped most of lithological units of the Neyriz ophiolitic complex and identified potential areas of high chromite mineralization (transition zone and mantle harzburgite) for chromite prospecting targets in the future. Furthermore, image processing results were verified by comprehensive fieldwork and laboratory analysis in the study area. Accordingly, result of this investigation indicate that the integration of information extracted from the image processing algorithms using Landsat TM and ASTER data sets could be broadly applicable tool for chromite prospecting and lithological mapping in mountainous and inaccessible regions such Iranian ophiolitic zones.

Urban land use/land cover changes in the Tema metropolitan area (1990–2010)

Land use and land cover changes are local and place specific, occurring incrementally in ways that often escape our attention. This study sought to detect changes in land cover in the Tema Metropolis of Ghana from 1990 to 2010. Multispectral Landsat Thematic Mapper data sets of 1990, 2000 and 2007 were acquired, pre-processed and enhanced. Unsupervised classification of the images was performed and six land cover classes (water, wetlands, closed vegetation, open vegetation, cropped lands, and built-up) were derived. The post-classification change detection technique was performed to derive the changes in land cover and their corresponding change matrices. Between 1990 and 2010, built-up areas expanded steadily to become the most prevalent land cover type in the metropolis, reducing vegetation cover dramatically. High population growth with its attendant rise in the demand for housing, and increasing commercial activities, were found to have influenced land cover changes over the period.

Phenological Response of an Arizona Dryland Forest to Short-Term Climatic Extremes

Baseline information about dryland forest phenology is necessary to accurately anticipate future ecosystem shifts. The overarching goal of our study was to investigate the variability of vegetation phenology across a dryland forest landscape in response to climate alterations. We analyzed the influence of site characteristics and climatic conditions on the phenological patterns of an Arizona, USA, ponderosa pine (Pinus ponderosa) forest during a five-year period (2005 to 2009) that encompassed extreme wet and dry precipitation regimes. We assembled 80 synthetic Landsat images by applying the spatial and temporal adaptive reflectance fusion method (STARFM) to 500 m MODIS and 30 m Landsat-5 Thematic Mapper (TM) data. We tested relationships between site characteristics and the timing of peak Normalized Difference Vegetation Index (NDVI) to assess the effect of climatic stress on the green-up of individual pixels during or after the summer monsoon. Our results show that drought-induced stress led to a fragmented phenological response that was highly dependent on microsite parameters, as both the spatial autocorrelation of peak timing and the number of significant site variables increased during the drought year. Pixels at lower elevations and with higher proportions of herbaceous vegetation were more likely to exhibit dynamic responses to changes in precipitation conditions. Our study demonstrates the complexity of responses within dryland forest ecosystems and highlights the need for standardized monitoring of phenology trends in these areas. The spatial and temporal variability of phenological signals may provide a quantitative solution to the problem of how to evaluate dryland land surface trends across time.

Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems

Automated cloud and cloud shadow identification algorithms designed for Landsat Thematic Mapper (TM) and Thematic Mapper Plus (ETM+) satellite images have greatly expanded the use of these Earth observation data by providing a means of including only clear-view pixels in image analysis and efficient cloud-free compositing. In an effort to extend these capabilities to Landsat Multispectal Scanner (MSS) imagery, we introduce MSS clear-view-mask (MSScvm), an automated cloud and shadow identification algorithm for MSS imagery. The algorithm is specific to the unique spectral characteristics of MSS data, relying on a simple, rule-based approach. Clouds are identified based on green band brightness and the normalized difference between the green and red bands, while cloud shadows are identified by near infrared band darkness and cloud projection. A digital elevation model is incorporated to correct for topography-induced illumination variation and aid in identifying water. Based on an accuracy assessment of 1981 points stratified by land cover and algorithm mask class for 12 images throughout the United States, MSScvm achieved an overall accuracy of 84.0%. Omission of thin clouds and bright cloud shadows constituted much of the error. Perennial ice and snow, misidentified as cloud, also contributed disproportionally to algorithm error. Comparison against a corresponding assessment of the Fmask algorithm, applied to coincident TM imagery, showed similar error patterns and a general reduction in accuracy commensurate with differences in the radiometric and spectral richness of the two sensors. MSScvm provides a suitable automated method for creating cloud and cloud shadow masks for MSS imagery required for time series analyses in temperate ecosystems. • Automated cloud/shadow identification algorithm for Landsat MSS data. • Enhances integration of MSS data into time series analysis with TM/ETM +/OLI. • Overall algorithm accuracy of 84.0%, with better performance for thick clouds. • Compared relatively well to coincident assessment of Fmask applied to TM data. • Performs well for most conditions except semi-transparent clouds and snow.

Monitoring land cover changes in Isfahan Province, Iran using Landsat satellite data.

Changes in land cover and land use reveal the effects of natural and human processes on the Earth's surface. These changes are predicted to exert the greatest environmental impacts in the upcoming decades. The purpose of the present study was to monitor land cover changes using Multispectral Scanner Sensor (MSS) and multitemporal Landsat Thematic Mapper (TM) data from the counties of Isfahan Province, Iran, during 1975, 1990, and 2010. The maximum likelihood supervised classification method was applied to map land cover. Postclassification change detection technique was also used to produce change images through cross-tabulation. Classification results were improved using ancillary data, visual interpretation, and local knowledge about the area. The overall accuracy of land cover change maps ranged from 88 to 90.6%. Kappa coefficients associated with the classification were 0.81 for 1975, 0.84 for 1990, and 0.85 for 2010 images. This study monitored changes related to conversion of agricultural land to impervious surfaces, undeveloped land to agricultural land, agricultural land to impervious surfaces, and undeveloped land to impervious surfaces. The analyses of land cover changes during the study period revealed the significant development of impervious surfaces in counties of Isfahan Province as a result of population growth, traffic conditions, and industrialization. The image classification indicated that agricultural lands increased from 2520.96 km(2) in 1975 to 4103.85 km(2) in 2010. These land cover changes were evaluated in different counties of Isfahan Province.

Assessing the potential applications of Landsat image archive in the ecological monitoring and management of a production mangrove forest in Malaysia

This paper explores the suitability of remotely sensed data obtained from Landsat imaging sensors, in combination with ancillary forest inventory data, for the ecological monitoring and management of a production mangrove forest in Malaysia. An assessment is presented on the capabilities and limitations of utilizing Landsat Thematic Mapper data for mapping mangrove species and stand age structure in the Matang Mangrove Forest Reserve. The cloud-free subset satellite images used in this study were acquired in May 1988, February 1995, December 1999, March 2001, December 2009 and November 2013. The December 1999 Landsat 7 ETM+ image was used for the error and accuracy assessment because it coincided with the year the reference map was produced. The image classification process included both pixel-based and object-based approaches conducted on a combination of software which included ENVI, ArcGIS 10.1 and eCognition. Results revealed that the pixel-based approach was not effective in delineating species and stand age structure due to the presence of the ‘salt and pepper effect’ in the image output. The object-based approach accurately delineated between commercial and non-commercial mangrove species. It also produced a reliable and accurate stand-age structure map based on three designated classes: clear-felled areas; young mangrove forest (less than 13 years old); and mature mangrove forest (13 years or older). The analysis highlights that the mapping and analysis of mangrove species and stand age structure with Landsat Thematic Mapper image datasets can play a useful role in achieving cost-efficient and ecologically sustainable outcomes in a production mangrove forest.

Evidence of drainage reversal in the NE Sahara revealed by space-borne remote sensing data

Interpretation of Radarsat-1, Shuttle Radar Topography Mission (SRTM) and Landsat 7 Enhanced Thematic Mapper (ETM+) data provided an overview of the evolving course of Wadi Qena region in the northeastern Sahara of North Africa. This resulted in a reconstruction model explaining that Wadi Qena developed through time in stages, including tectonically-induced drainage reversal. This was evidenced by presence of two opposite courses, physical continuity of the main channel beyond the present watershed, and prominent asymmetry as the downstream drainage makes up 80% of the total indicating an opposite style of drainage. Uplift and Red Sea tectonics caused a blockage of the wadi course, segmented the surrounding rock masses, and upended the slope of the land. The river course reversed direction and followed a contorted pathway to become a tributary of the Nile. Vestiges of the earlier Wadi Qena are preserved in the form of paleoslopes, and diverted valleys. The results demonstrate that views from space provide powerful tools for better understanding of the paleohydrology in arid regions.