Landsat Spectral Data Research Articles

Practical remote sensing data analysis for efficient geological field mapping: An example from the southwest portion of the Three Peaks 7.5ʹ quadrangle, southwest Utah

ABSTRACT We present the results of remote sensing analysis of U.S. Geological Survey digital elevation models, Landsat spectral data, and National Agriculture Imagery Program orthophotos to generate a preliminary geologic map that significantly aided our boots-on-the-ground geologic mapping of the southwest portion of the Three Peaks 7.5ʹ quadrangle in southwest Utah. Sedimentary rocks, intrusive rocks, and a variety of geologic contacts, including unconformities and faults, as well as unconsolidated alluvium are recognized in the study area. We constructed a series of geologic maps using remote sensing data and analysis techniques that are readily available to geoscientists. These techniques include band-ratioing, random forest analysis, and these analyses. Resolution of the resulting geologic maps generated by random forest analysis and principal component analysis were greatly improved by incorporating both the high resolution orthophoto and the 1/3 arc second digital elevation model into the principal component analysis. Our final remotely sensed geologic map integrated results from each technique. We used this remotely sensed geologic map to develop our preliminary plan for the field campaign. We preselected high priority targets (e.g., previously unrecognized units and faults) for in-person field analysis. We also identified highly accessible areas that allowed for efficient use of in-person field time needed for evaluation of large areas covered by relatively homogeneous units. The authors spent 25 days in the field over a seven-week field season, mapping the same area. Here, we compare the remote-sensed geologic maps with the final in-person field checked geologic map and discuss the utility of remote sensing data for detailed geologic field investigations. Preparing a remote sensing geologic map prior to field work has several advantages, including identification of mappable units, recognition of geologic contacts, and selection of priority target areas for direct evaluation of hypothesized field relationships, thereby promoting more efficient geologic mapping.

Quantitative Evaluation of Urban Expansion using NPP-VIIRS Nighttime Light and Landsat Spectral Data

Quantifying the evolution of urban expansion is of vital importance for optimizing land use patterns and promoting sustainable urban development. This research constructed the Enhanced Nighttime Light Urban Index (ENUI), which combines NPP-VIIRS nighttime light with Landsat spectral data, and used this index for the quantitative evaluation of urban expansion in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) urban agglomeration. The results showed that, between 2012 and 2018, (1) the extraction using the ENUI achieved an overall accuracy of 0.93–0.94, producer's accuracy in urban areas of 0.82–0.84, user's accuracy in urban areas of 0.73–0.75, and Kappa values of 0.75–0.78, showing a relatively high extraction accuracy; (2) the GBA urban expansion followed a circular and radial expansion pattern; (3) the growth trend of expansion intensity and development potential for sub-central cities was stronger than central cities; and (4) the agglomeration autocorrelation of the spatial expansion in the GBA presents a continuous "dispersion" trend. Compared with previous methods, our study improved the extraction accuracy of urban expansion and effectively reduced the deficiency of overflow effect for NPP-VIIRS nighttime light. The research can thus provide valuable datasets and reference for decision-making to adjust and optimize urban development pattern and design urban sustainable development. • NPP-VIIRS nighttime light, NDVI, NDWI and NDBI are used to quantify urban expansion. • The resulting overall accuracy and Kappa are 0.93–0.94 and 0.75–0.78, respectively. • The resulting producer's accuracy and user's accuracy are 0.82–0.84 and 0.73–0.75, respectively. • Multiple indicators are used to quantify urban expansion and analyze agglomeration. • Urban expansion rate showed different development patterns for different cities.

Integrating Phenological and Geographical Information with Artificial Intelligence Algorithm to Map Rubber Plantations in Xishuangbanna

Most natural rubber trees (Hevea brasiliensis) are grown on plantations, making rubber an important industrial crop. Rubber plantations are also an important source of household income for over 20 million people. The accurate mapping of rubber plantations is important for both local governments and the global market. Remote sensing has been a widely used approach for mapping rubber plantations, typically using optical remote sensing data obtained at the regional scale. Improving the efficiency and accuracy of rubber plantation maps has become a research hotspot in rubber-related literature. To improve the classification efficiency, researchers have combined the phenology, geography, and texture of rubber trees with spectral information. Among these, there are three main classifiers: maximum likelihood, QUEST decision tree, and random forest methods. However, until now, no comparative studies have been conducted for the above three classifiers. Therefore, in this study, we evaluated the mapping accuracy based on these three classifiers, using four kinds of data input: Landsat spectral information, phenology–Landsat spectral information, topography–Landsat spectral information, and phenology–topography–Landsat spectral information. We found that the random forest method had the highest mapping accuracy when compared with the maximum likelihood and QUEST decision tree methods. We also found that adding either phenology or topography could improve the mapping accuracy for rubber plantations. When either phenology or topography were added as parameters within the random forest method, the kappa coefficient increased by 5.5% and 6.2%, respectively, compared to the kappa coefficient for the baseline Landsat spectral band data input. The highest accuracy was obtained from the addition of both phenology–topography–Landsat spectral bands to the random forest method, achieving a kappa coefficient of 97%. We therefore mapped rubber plantations in Xishuangbanna using the random forest method, with the addition of phenology and topography information from 1990–2020. Our results demonstrated the usefulness of integrating phenology and topography for mapping rubber plantations. The machine learning approach showed great potential for accurate regional mapping, particularly by incorporating plant habitat and ecological information. We found that during 1990–2020, the total area of rubber plantations had expanded to over three times their former area, while natural forests had lost 17.2% of their former area.

Colour band fusion and region enhancement of spectral image using multivariate histogram

ABSTRACT Multi-spectral satellite remote sensing imagery have several applications including detection of objects or distinguishing land surface areas based on amount of greenery or water etc. The enhancement of spectral images helps extracting and visualizing spatial and spectral features. This paper identifies some specific regions of interest (RoI) of the earth's surface from the remotely sensed spectral or satellite image. The RoI are extracted and identified as major segments. Trivially, uni-variate histogram thresholding is used for gray images as a tool of segmentation. However, for color images multivariate histogram is effective to get control on color bands. It also helps emphasizing color information for clustering purpose. In this paper, the 2D and 3D histograms are used for clustering pixels in order to extract the RoI. The RGB color bands along with the infrared (IR) band information are used to form the multivariate histogram. Two datasets are used to carry out the experiment. The first one is an artificially designed dataset and the next is Indian Remotely Sensed (IRS-1A) satellite imagery. This paper proves the correctness of the proposed mathematical implication on the artificial dataset and consequently perform the application on LandSat Spectral data. The test result is found to be satisfactory.

Time series monitoring of impervious surfaces and runoff impacts in Metro Vancouver

Urban areas are increasing rapidly worldwide, leading to widespread changes in land surfaces over time. Urbanized land cover is heterogeneous, and is characterized by a large areal proportion of manufactured impervious surfaces which are linked to ecological degradation, habitat loss, and increase in precipitation runoff leading to pollution and safety risks. Data from the Landsat series of satellites present an opportunity to characterize urban land cover and impervious surfaces, over a large spatial and temporal scale. In this study, land cover changes from 1990 to 2015 are characterized in the large metropolitan area of Metro Vancouver, Western Canada. An ordinal regression is used to link Landsat spectral data with a detailed land classification containing classes of impervious surface used by municipal planners in the region (Spearman's Rho = 0.76). The regression is then used to classify a time series of imagery where static land classifications are not available, providing a 25-year time-series of change in impervious surface area. A trend in increasing impervious surface was detected across the municipalities in the region, with an overall areal increase of 31.96%. Precipitation events were then simulated at each time step, using precipitation rates adjusted for expected changes in climate by 2050. Both runoff depths and inundated area increased over time, with a 51% increase in area inundated by at least 5 cm. Runoff depths were evaluated for each municipality in the region, and compared to projected populations for 2050 to highlight communities that may face elevated levels of runoff risk. Results show a steady increase in impervious surfaces in the region. Impacts of future extreme precipitation events vary across the region, with flat and low-lying topographies appearing to be more severely affected, along with areas containing extensive impervious development.

High-Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data

Cloud computing and freely available, high-resolution satellite data have enabled recent progress in crop yield mapping at fine scales. However, extensive validation data at a matching resolution remain uncommon or infeasible due to data availability. This has limited the ability to evaluate different yield estimation models and improve understanding of key features useful for yield estimation in both data-rich and data-poor contexts. Here, we assess machine learning models’ capacity for soybean yield prediction using a unique ground-truth dataset of high-resolution (5 m) yield maps generated from combine harvester yield monitor data for over a million field-year observations across the Midwestern United States from 2008 to 2018. First, we compare random forest (RF) implementations, testing a range of feature engineering approaches using Sentinel-2 and Landsat spectral data for 20- and 30-m scale yield prediction. We find that Sentinel-2-based models can explain up to 45% of out-of-sample yield variability from 2017 to 2018 (r2 = 0.45), while Landsat models explain up to 43% across the longer 2008–2018 period. Using discrete Fourier transforms, or harmonic regressions, to capture soybean phenology improved the Landsat-based model considerably. Second, we compare RF models trained using this ground-truth data to models trained on available county-level statistics. We find that county-level models rely more heavily on just a few predictors, namely August weather covariates (vapor pressure deficit, rainfall, temperature) and July and August near-infrared observations. As a result, county-scale models perform relatively poorly on field-scale validation (r2 = 0.32), especially for high-yielding fields, but perform similarly to field-scale models when evaluated at the county scale (r2 = 0.82). Finally, we test whether our findings on variable importance can inform a simple, generalizable framework for regions or time periods beyond ground data availability. To do so, we test improvements to a Scalable Crop Yield Mapper (SCYM) approach that uses crop simulations to train statistical models for yield estimation. Based on findings from our RF models, we employ harmonic regressions to estimate peak vegetation index (VI) and a VI observation 30 days later, with August rainfall as the sole weather covariate in our new SCYM model. Modifications improved SCYM’s explained variance (r2 = 0.27 at the 30 m scale) and provide a new, parsimonious model.

Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia.

This study develops a modelling framework by utilizing multi-sensor imagery for classifying different forest and land use types in the Phnom Kulen National Park (PKNP) in Cambodia. Three remote sensing datasets (Landsat optical data, ALOS L-band data and LiDAR derived Canopy Height Model (CHM)) were used in conjunction with three different machine learning (ML) regression techniques (Support Vector Machines (SVM), Random Forests (RF) and Artificial Neural Networks (ANN)). These ML methods were implemented on (a) Landsat spectral data, (b) Landsat spectral band & ALOS backscatter data, and (c) Landsat spectral band, ALOS backscatter data, & LiDAR CHM data. The Landsat-ALOS combination produced more accurate classification results (95% overall accuracy with SVM) compared to Landsat-only bands for all ML models. Inclusion of LiDAR CHM (which is a proxy for vertical canopy heights) improved the overall accuracy to 98%. The research establishes that majority of PKNP is dominated by cashew plantations and the nearly intact forests are concentrated in the more inaccessible parts of the park. The findings demonstrate how different RS datasets can be used in conjunction with different ML models to map forests that had undergone varying levels of degradation and plantations.

The Influence of Underlying Surface on Land Surface Temperature -- A Case Study of Urban Green Space in Harbin

In recent years, the expansion of Harbin’s main urban area, the capital city of a northeast province in china, has resulted in the increase of impervious surface, the decrease of green space and the significant change of urban thermal environment. In order to further reveal the comprehensive effects of underlying surface especially green space and impervious surface on urban thermal environment in the severe cold region, this paper adopts remote sensing technology to extract and analyze the Landsat spectral imaging data of Harbin’s main urban area in summers and winters from 2004 to 2015, and thus puts forward the regulations of effects of different underlying surface types on land surface temperature (LST). It is found that a large amount of permeable underlying surface like green space in Harbin has been continuously transformed into impervious surface, which has led to a continuous rise of urban land surface temperature in recent years. Urban green space plays a certain role in improving urban thermal environment in both summers and winters. We can find especial rules on the distribution of the urban loops and the cooling ranges in summers. As for winters, under the influence of the main heat source and the cooling mode, there is no remarkable regularity.

Characterizing historical fire patterns as a guide for harvesting planning using landscape metrics derived from long term satellite imagery

Forest managers across the Canadian boreal require detailed fire pattern information to support disturbance-based management. However, there are no consistent classifications of post-fire patterns, and those that exist rely on field-data that is both expensive and lacking in spatial representation. As a result, across the managed boreal forest there is limited fire pattern information, no standardized protocols to derive the quantitative fire pattern information nor agreement on the most appropriate metrics needed to implement management planning based on emulating natural disturbances. In this paper we first derived three classes of tree mortality based on Landsat spectral data and aerially photo-interpreted (API) polygons. Next we used these derived classes to compute seven fire pattern metrics over 14 fires that occurred from 1984 to 2006 in the Canadian provinces of Alberta and Saskatchewan, Canada. We then compared the metrics derived from the Landsat data to those computed from the API coverage. Lastly, we assessed the influence of a suite of environmental and fire pattern variables on the Landsat-derived indices. Overall we found very close correspondence for the two event-scale indices: the total area affected by fire and the complexity of the perimeter. The more detailed within-fire event metrics presented more varied results and were somewhat less precise. For example, the total amount of residuals or the largest disturbed patch were accurately captured. Other indices such as the number of disturbed patches or total amount of island remnants presented moderate systematic biases, but still might be serviceable given that the biases were predictable in direction. Fire size and seasonal drought variables were the most correlated with the Landsat derived pattern indices and their inclusion as model variables is likely to increase overall prediction accuracy. A pan-boreal geospatial database of detailed fire pattern metrics would be an invaluable tool to support the implementation of disturbance-based management approaches. Here we demonstrate that a cost-effective Landsat methods produces comparable fire pattern information to conventional approaches based on manual interpretation of aerial photographs. Building upon the standardized methods proposed here hundreds of fires could be mapped, to potentially, create a comprehensive national fire pattern database.

Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers

The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM) system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) to identify areas of change and no change. The system then automatically generates large amounts of training samples (n > 1 million) in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and 2011 and Landsat ETM+ data for 2011. The entire system took 9.5 h to process. We expected that the use of the change mask would improve classification accuracy by reducing the number of mislabeled training data caused by land cover change between 2008 and 2011. However, this was not the case due to exceptional robustness of Random Forest classifier to mislabeled training samples. The system achieved an overall accuracy of 65%–67% using 22 detailed classes and 72%–74% using 12 aggregated national classes. “Water”, “Plantations”, “Plantations—clearfelled”, “Orchards—trees”, “Sugarcane”, “Built-up/dense settlement”, “Cultivation—Irrigated” and “Forest (indigenous)” had user’s accuracies above 70%. Other detailed classes (e.g., “Low density settlements”, “Mines and Quarries”, and “Cultivation, subsistence, drylands”) which are required for operational, provincial-scale land use planning and are usually mapped using manual image interpretation, could not be mapped using Landsat spectral data alone. However, the system was able to map the 12 national classes, at a sufficiently high level of accuracy for national scale land cover monitoring. This update approach and the highly automated, scalable LALCUM system can improve the efficiency and update rate of regional land cover mapping.

Soil Mapping in Chongwe, Zambia by Digital Analysis of Landsat Data

<p>Designing a methodology for mapping and studying soils in a quick and inexpensive way is critical especially in developing countries like Zambia, which lack detailed soil surveys. Therefore, this study was conducted to determine the potential of Landsat 7 ETM+ data (Enhanced Thematic Mapper plus) in mapping soils in Chongwe, a semi-arid region in Zambia. In addition, the study attempted to establish how accurate spectral soil maps produced by digital analysis of Landsat data can be and how such maps compared with field observation data. Also, in situations where there was poor agreement between Landsat data and field observation data, possible causes of such discrepancies where determined.</p><p>A soil inventory of the Chongwe region of Zambia was prepared using computer-aided digital analysis of two Landsat 7 ETM+ satellite images acquired in the dry and rainy seasons to investigate the hypothesis that there is a relationship between Landsat spectral reflectance and certain soil types and that this relationship can be used to map soils with reasonable accuracy.</p><p>The study revealed that digital analysis of Landsat 7 ETM images has the capacity to map and delineate soil patterns with reasonable accuracy, especially when acquired during the dry season when there are long periods of cloud free skies, low soil moisture and minimal vegetation cover. The overall agreement between the Landsat classification and reference data was 72%, indicating a definite relationship between Landsat imagery and soil types.</p><p>In terms of soilscape boundary delineation, the Landsat derived map was had a higher level of agreement with field observations than the conventional soil map. In addition, the study showed that overall, upland areas have a better agreement with Landsat spectral data compared to lowland areas, probably due to the diverse origin of sediments and low spatial extent of most landforms in lowland areas.</p>

Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery

Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover at the sub-pixel level of Landsat for a single year, and the Landtrendr algorithm was applied to Landsat spectral data to explore the temporal information of forest cover change. Four different approaches were employed to model the relationship between forest cover and Landsat spectral data. The result shows incorporating the historic information using the temporal trajectory fitting process could infuse the model with better prediction power. Random forest modeling performs the best for quantitative forest cover estimation. Temporal trajectory fitting with random forest model shows the best agreement with validation data (R2 = 0.82 and RMSE = 5.19%). We applied our approach to Youyu county in Shanxi province of China, as part of the Three North Shelter Forest Program, to map multi-decadal forest cover dynamics. With the availability of global time-series Landsat imagery and affordable airborne LiDAR data, the approach we developed has the potential to derive large-scale forest cover dynamics.

Prediction of soil surface salinity in arid region of central Iran using auxiliary variables and genetic programming

ABSTRACTSpatial information on soil salinity is increasingly needed for decision making and management practices in arid environments. In this article, we attempted to investigate soil salinity variation via a digital soil mapping approach and genetic programming in an arid region, Chah-Afzal, located in central Iran. A grid sampling strategy with 2-km distance was used. In total, 180 soil surface samples were collected and then analyzed. A symbolic regression was then adopted to correlate electrical conductivity (ECe) with a suite of auxiliary data including predicted maps of apparent electrical conductivity (vertical: ECav and horizontal: ECah), Landsat spectral data and terrain attributes derived from a digital elevation model. The accuracy of the genetic programming model was evaluated using root mean square error (RMSE), mean error (ME), and coefficient of determination (R2) based on an independent validation data set (20% of database or thirty soil samples). In general, results showed that ECah had the strongest influence on the prediction of soil salinity followed by salinity index wetness index, Landsat Band 3, multi-resolution valley bottom flatness index, elevation, and normalized difference vegetation index. Furthermore, results indicated that the genetic programming model predicted ECe over the study area accurately (R2 = 0.87, ME = −1.04 and RMSE = 16.36 dSm−1). Overall, it is suggested that similar applications of this technique could be used for mapping soil salinity in other arid regions of Iran.

Combination of proximal and remote sensing methods for rapid soil salinity quantification

Salt affected soils are pervasive in semiarid and arid regions worldwide. Traditionally, soil salinity has been measured via electrical conductivity (EC). This study evaluated the feasibility of using three different methods for prediction of surface soil salinity, namely visible near infrared diffuse reflectance spectroscopy (VisNIR DRS), portable x-ray fluorescence (PXRF) spectrometry, and remote sensing (RS). Two saline playas were evaluated in West Texas, USA featuring 91 and 74 soils collected via random stratified sampling at 0–5cm and representing a wide variety of soil salinity from high levels inside the playa bottoms to lower levels on the annulus and surrounding uplands. Samples were subjected to PXRF and VisNIR DRS scanning under laboratory conditions, and compared to Landsat spectral data and traditional laboratory analyses of salinity (e.g., 1:5 v/v suspensions). Results showed a broad range of EC (1:5) (0.028 to 43.41dSm−1). Derived from PXRF, both Cl and S were significantly and positively correlated with log10 transformed EC (1:5). VisNIR partial least squares prediction models produced strong residual prediction deviations (RPDs) of 2.49–2.91. Validation statistics of Savitzky–Golay support vector regression outperformed all other VisNIR models tested with an RPD of 3.1. The model using Landsat band reflectance alone produced lowest prediction accuracy (RPD=1.27). While the performance of each technique produced variable success independently, combining the three techniques produced the highest predictability (RPD=3.35). Given that, laboratory determination of EC (1:5) is time consuming and all three types of data (VisNIR DRS, PXRF, and RS) are being quick and easy to collect, their synthesis in predictive models offers excellent potential for providing soil salinity measurements comparable to standard, laboratory derived data. Furthermore, remotely sensed data can potentially be used to map topsoil salinity across large areas with suitable calibrations.

Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR

AbstractImproved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world's most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR‐derived measures of three‐dimensional canopy structure and subcanopy topography using classification‐tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDAR‐based results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar‐based topographic data. The accuracy of the LiDAR‐based forest type classification (including native‐ and exotic‐dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR‐derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR‐derived variables of vegetation structure were better predictors of forest successional status (i.e., mid‐secondary, late‐secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification‐tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.

Water Quality Monitoring Using Remote Sensing and an Artificial Neural Network

In remotely located watersheds or large waterbodies, monitoring water quality parameters is often not feasible because of high costs and site inaccessibility. A cost-effective remote sensing-based methodology was developed to predict water quality parameters over a large and logistically difficult area. Landsat spectral data were used as a proxy, and a neural network model was developed to quantify water quality parameters, namely chlorophyll-a, turbidity, and phosphorus before and after ecosystem restoration and during the wet and dry seasons. The results demonstrate that the developed neural network model provided an excellent relationship between the observed and simulated water quality parameters. These correlated for a specific region in the greater Florida Everglades at R2 > 0.95 in 1998–1999 and in 2009–2010 (dry and wet seasons). Moreover, the root mean square error values for phosphorus, turbidity, and chlorophyll-a were below 0.03 mg L−1, 0.5 NTU, and 0.17 mg m−3, respectively, at the neural network training and validation phases. Using the developed methodology, the trends for temporal and spatial dynamics of the selected water quality parameters were investigated. In addition, the amounts of phosphorus and chlorophyll-a stored in the water column were calculated demonstrating the usefulness of this methodology to predict water quality parameters in complex ecosystems.

Assessing the seasonal dynamics of inundation, turbidity, and aquatic vegetation in the Australian wet–dry tropics using optical remote sensing

ABSTRACTFloodplain wetlands in the wet–dry tropics are under increasing pressure from water resource development, and there is a need for methods to assess the biophysical dynamics of these extensive and often remote ecosystems. This study assessed the capacity of optical remote sensing methods to monitor the seasonal dynamics of inundation, turbidity, and aquatic vegetation cover for a northern Australian savanna catchment. MODIS data were used to map seasonal flood inundation patterns, and Landsat 5 TM data were used to map dry‐season waterbody dynamics. A network of water‐depth loggers and temperature sensors provided ground observations of surface inundation dynamics, and was used to validate the inundation mapping. Post‐flood waterbody surface area declined by 89% over the dry season, with 70% of the decline occurring for Palustrine (floodplain) waterbodies. All aquatic systems became increasingly disconnected as the dry season progressed. Statistical relationships were developed between seasonal measurements of turbidity, aquatic vegetation cover, and Landsat spectral data. Catchment wide predictions showed that turbidity increased and macrophyte cover decreased for the Palustrine and Lacustrine (lake) systems, while the Riverine systems became less turbid over the dry season. These results show that, for open savanna landscapes where cloud cover does not limit waterbody detection, optical remote sensing methods can be effectively applied to assess seasonal patterns of inundation and accompanying biophysical dynamics. This provides an effective tool to evaluate the impact of river flow regime changes from water resource use or climate change in these regions. Copyright © 2012 John Wiley & Sons, Ltd.

Possibility investigation of tree diversity mapping using Landsat ETM+ data in the Hyrcanian forests of Iran

Abstract Lack of data often limits understanding and management of biodiversity in forested areas. Remote sensing imagery has considerable potential to aid in the monitoring and prediction of biodiversity across many spatial and temporal scales. In this paper, we explored the possibility of defining relationships between species diversity indices and Landsat ETM+ reflectance values for Hyrcanian forests in Golestan province of Iran. We used the COST model for atmospheric correction of the imagery. Linear regression models were implemented to predict measures of biodiversity (species richness and reciprocal of Simpson indices) using various combinations of Landsat spectral data. Species richness was modeled using the band set ETM5, ETM7, DVI, wetness and variances of ETM1, ETM2 and ETM5 (adjusted R2 = 0.59, RMSE = 1.51). Reciprocal of Simpson index was modeled using the band set NDVI, brightness, greenness, variances of ETM2, ETM5 and ETM7 (adjusted R2 = 0.459 RMSE = 1.15). The results demonstrated that spectral reflectance from Landsat can be used to effectively model tree species diversity. Predictive map derived from the presented methodology can help evaluate spatial aspects and monitor tree species diversity of the studied forest. The methodology also facilitates the evaluation of forest management and conservation strategies in northern Iran.

Mapping mountain vegetation using species distribution modeling, image‐based texture analysis, and object‐based classification

AbstractObjective: The objective of this study was to map vegetation composition across a 24 000 ha watershed.Location: The study was conducted on the western slope of the Sierra Nevada mountain range of California, USA.Methods: Automated image segmentation was used to delineate image objects representing vegetation patches of similar physiognomy and structure. Image objects were classified using a decision tree and data sources extracted from individual species distribution models, Landsat spectral data, and life form cover estimates derived from aerial image‐based texture variables.Results: A total of 12 plant communities were mapped with an overall accuracy of 75% and a χ‐value of 0.69. Species distribution model inputs improved map accuracy by approximately 15% over maps derived solely from image data. Automated mapping of existing vegetation distributions, based solely on predictive distribution model results, proved to be more accurate than mapping based on Landsat data, and equivalent in accuracy to mapping based on all image data sources.Conclusions: Results highlight the importance of terrain, edaphic, and bioclimatic variables when mapping vegetation communities in complex terrain. Mapping errors stemmed from the lack of spectral discernability between vegetation classes, and the inability to account for the confounding effects of land use history and disturbance within a static distribution modeling framework.

The application of Landsat image data to rangeland assessment and monitoring: the development and demonstration of a land image-based resource information system (LIBRIS)

A land image-based resource information system (LIBRIS) has been developed to support rangeland resource management on both regional and local scales. The study area is centred on the mining town of Broken Hill (lat. 32°S, long. 141°30’E) which lies within the semi-arid rangelands of southern Australia. LIBRIS is a geographic information system developed to integrate Landsat spectral data with other relevant spatial data sets (tenure, rangeland type, elevation and slope) not only to improve the interpretation of Landsat data for rangeland applications, but also for report generation and the modelling and derivation of other landscape variables such as susceptibility to erosion. A simple vegetation–Landsat response model is used within LIBRIS. The model, based on field reflectance data, has two indices representing the proportions of ‘cover’ and ‘greenness’ (vigour) of the sparse rangeland vegetation. The index of ‘cover’ is used extensively in this report. Classifications of cover classes derived from Landsat are integrated with land tenure boundaries to present reports of land cover classes and changes in these classes over time (1972–80) for pastoral properties and for the various rangeland types. Finally, the land cover information has been incorporated with slope to derive a map and report of susceptibility to erosion; an index which is of fundamental importance to arid land management. The utility of LIBRIS in integrating Landsat data with collateral data for arid land management is thus demonstrated.