Landsat Thematic Mapper Data Research Articles

Modelling and Mapping of Aboveground Carbon of Oluwa Forest Reserve Using LandSat 8 TM and Forest Inventory Data

This study was conducted in Oluwa Forest Reserve to assess and predict its aboveground carbon sequestration potentials using LandSat Thematic Mapper data. The Oluwa Forest Reserve, Ondo State, Nigeria, is recognized for its rich biodiversity and extensive size. To estimate its forest aboveground biomass and carbon should be complex and costly endeavour requiring the expertise of various professionals and equipment. Consequently, this study explored the use of Geographic Information System (GIS) and Remote Sensing (RS) technology using LandSat bands to estimate spectral indices in fitting linear models to predict the aboveground carbon sequestration potentials of the tropical rainforest ecosystem of Oluwa Forest Reserve. The observed aboveground carbon from sample plots and the estimated spectral indices were used to model the spread of aboveground carbon of Oluwa Forest Reserve. Positive linear relationship exists between the observed and the spectral indices data estimated. Therefore, linear models were fitted and the best-fit was determined using statistical measures. The aboveground carbon average estimated from the sample plots and the predicted were 150.70 t/ha and 149.80 t/ha, respectively. The coefficient of determination 94% and Root Mean Square Error = 6.38E-16, respectively were obtained statistically. The chosen model predicts the aboveground carbon spread of Oluwa Forest Reserve adequately. The study revealed that spectral data, GIS and RS are critical for large forest aboveground carbon modelling and mapping for efficiency.

Unveiling a decade of urban evolution: Analyzing land use changes in Idah (2001-2010) using GIS and remote sensing

The urban landscape of Idah Local Government Area (LGA) witnessed significant land use and land cover (LULC) changes from 2001 to 2010. Employing Geographic Information Systems (GIS) and Remote Sensing techniques, this research scrutinized alterations shaping the physical terrain. The study utilized Landsat Thematic Mapper (TM) data from 2001 and Landsat Enhanced Thematic Mapper Plus (ETM+) data from 2010, facilitating a comprehensive temporal analysis. A pivotal aspect of the research entailed deploying a robust maximum likelihood classifier for supervised classification, precisely categorizing images into distinct LULC classes. This process was fortified by overlay analysis, judiciously applied to gauge alterations within the defined timeframe. The findings from analysis paint a distinct depiction. The built-up area asserts its prominence, engulfing a significant 92.383% of the examined region. In sharp contrast, vegetation claims a mere 0.933% within the same bounds. An unequivocal transformation is evidenced through the conversion from forested expanses to built-up zones, spanning an expansive 23,733.9 square kilometers. Concurrently, a 1.8 square kilometers reduction in forest coverage and an 85.5 square kilometers decrease in vegetation are observed. Remarkably, the shift from forests to vegetation records a noteworthy increase of 11,095.2 square kilometers. Additionally, water bodies expand by 751.5 square kilometers near urban and river sectors. These alterations are attributed to urbanization, reflecting escalating population growth, diverse functional activities, and the burgeoning demand for residential housing. This study underscores the gradual encroachment of urbanization onto minor land uses. Researchers and stakeholders are advised to engage in continuous land use change assessments, offering insights into urban-induced LULC changes. This proactive approach facilitates informed decisions and effective urban planning, aligning development with environmental and societal priorities. The significance lies in fostering a harmonious and sustainable urban landscape.

Confidence Intervals and Regions for Proportions under Various Three-Endmember Linear Mixture Models

Many studies in recent years have been devoted to estimating the per-pixel proportions of three broad classes of materials (e.g., photosynthetic vegetation, non-photosynthetic vegetation and bare soil) using data from multispectral sensors. Oftentimes, the estimated proportions are used to monitor environmental change in both urban and non-urban environments. Many of these papers use proportion estimation methods based on the linear mixture model. Very few of these papers assess the accuracy of their estimators. This paper shows how to produce confidence intervals (CIs) and joint confidence regions (JCRs) for the proportions associated with various linear mixture models. There are two main models, both of which assume that the coefficients in the model are non-negative. The first model assumes that the coefficients sum to 1. The second does not, but uses rescaling of the estimated coefficients to produce estimated proportions. Three variants of these two models are also analysed. JCRs are shown to be particularly informative, because they are typically better at localising the information than CIs are. The methodology is illustrated using examples from Landsat Thematic Mapper data at 1169 locations across Australia, each of which has associated field observations. There is also a discussion about the extent to which the methodology can be extended to hyperspectral data.

Effects of urban land use transitions on ecosystem services: Implication on Chinese urban planning

Urban land use transitions along with the rapid urbanization development cause obvious negative effects on the local ecological system. This paper explored the dynamic patterns of land use in central China’s largest city Wuhan, using high-resolution Landsat Thematic Mapper (TM) data in 1990, 2000 and 2010. The aggregate ecosystem services value, its dynamics, and spatial features were discussed based on the equivalent value per unit area approach, with the revision of Xie et al.’s (2015) unit parameters, adapted to Wuhan’s local situation. The results indicated that Wuhan’s ecosystem services value had been experiencing a continuous decrease during 1990 and 2000 and the following decade. The decrease was mainly due to urban expansion. However, Wuhan’s ecosystem services value decreased less between 2000 and 2010 with a much faster urban expansion pace than those in the former decade. The results illustrated that urban development severely affected ponds, rivers/lakes in their contribution to the ecosystem services that they naturally provided. Ponds and rivers/lakes contributed importantly to urban ecosystem services, even with a small proportion of area. Farmland contributed highly to urban expansion space as well as highly to the loss of related ecosystem services such as food provision and nutrient cycle maintenance. Forest land followed with an important contribution to climate regulation, soil conservation and waste regulation. The optimization of land use structure and configuration could improve urban ecosystem services. The authors conclude that even though the awareness of ecological resources conservation and sustainable development planning is emphasized, the practices are still in a challenge. The integration of ecosystem services dynamics to urban planning is an essential way that the large cities of China can realize sustainable development during its rapid urbanization.

Analysis and Estimation of Net Primary Productivity of Vegetation in Nanjing Using Multi-Sourced Remote Sensing Data

Net primary productivity (NPP) is an important indicator of Earth’s ecosystem. However, NPP estimation of cities by high spatial resolution is limited in China, especially Nanjing, and NPP evolution and quantitative analysis of influencing factors in time series are insufficient. Nanjing, as one of the central cities in China, plays a major role in China’s economic development. In this paper, high spatial resolution data combined with Landsat Thematic Mapper (TM) data are used for NPP estimation and analysis in Nanjing City. The research contents of this paper are as follows: (1) To improve the passive effect of “red border region” on GF-1 WFV forest and farmland classification ability, the multispectral and high-resolution images are fused, and the image fusion effect was also evaluated. (2) The NPP of four seasons in Nanjing in 2017 was estimated using the CASA model. (3) The results of NPP estimation are analyzed based on statistical principles. The advantage of this paper lies in the comprehensive utilization of multi-source data and the quantitative analysis of the response mechanism between meteorological data and the NPP estimation value.

Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas

Abstract. Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, unique temporal coverage (1982–2019), and inter-sensor/satellite consistency.

Land–Lake Linkage and Remote Sensing Application in Water Quality Monitoring in Lake Okeechobee, Florida, USA

The state of water quality of lakes is highly related to watershed processes which will be responsible for the delivery of sediment, nutrients, and other pollutants to receiving water bodies. The spatiotemporal variability of water quality parameters along with the seasonal changes were studied for Lake Okeechobee, South Florida. The dynamics of selected four water quality parameters: total phosphate (TP), total Kjeldahl nitrogen (TKN), total suspended solid (TSS), and chlorophyll-a (chl-a) were analyzed using data from satellites and water quality monitoring stations. Statistical approaches were used to establish correlation between reflectance and observed water quality records. Landsat Thematic Mapper (TM) data (2000 and 2007) and Landsat Operational Land Imager (OLI) in 2015 in dry and wet seasons were used in the analysis of water quality variability in Lake Okeechobee. Water quality parameters were collected from twenty-six (26) monitoring stations for model development and validation. In the regression model developed, individual bands, band ratios and various combination of bands were used to establish correlation, and hence generate the models. A stepwise multiple linear regression (MLR) approach was employed and the results showed that for the dry season, higher coefficient of determination (R2) were found (R2 = 0.84 for chl-a and R2 = 0.67 for TSS) between observed water quality data and the reflectance data from the remotely-sensed data. For the wet season, the R2 values were moderate (R2 = 0.48 for chl-a and R2 = 0.60 for TSS). It was also found that strong correlation was found for TP and TKN with chl-a, TSS, and selected band ratios. Total phosphate and TKN were estimated using best-fit multiple linear regression models as a function of reflectance data from Landsat TM and OLI, and ground data. This analysis showed a high coefficient of determination in dry season (R2 = 0.92 for TP and R2 = 0.94 for TKN) and in wet season (R2 = 0.89 for TP and R2 = 0.93 for TKN). Based on the findings, the Multiple linear regression (MLR) model can be a useful tool for monitoring large lakes like Lake Okeechobee and also predict the spatiotemporal variability of both optically active (Chl-a and TSS) and inactive water (nutrients) quality parameters.

Assessing Drivers of Deforestation and Forest Degradation in PirPanjal Region of Kashmir Himalayas using Geospatial Approach

Global deforestation is a major contributor of CO 2 emission despite registering slowdown in the past decade. Regional forest cover change, a cause of climatic concern is attributed to increasing anthropogenic pressure on forests. Focus on drivers of forest cover change has become inevitable due to global emphasis on role of socio-economic framework in conserving forests. Keeping in view, the study was aimed to assess deforestation and forest degradation and identify drivers of change in PirPanjal region of Western Himalaya using Landsat Thematic Mapper (TM) data of 2003 and 2013. Satellite data was delineated into different categories of forest cover namely closed forest, open forest, forest scrub, grasslands and non-forest using on-screen visual interpretation. Mapping was supplemented with extensive field validation points for accuracy assessment. Forest cover change map and change matrix were generated to assess rates of deforestation. Drivers of change were assessed through a detailed semi-structured interview schedule. The results revealed a gross annual deforestation of -0.46% and a net annual deforestation of -0.45%. Illicit felling, poverty and lack of employment were identified to be the top drivers of deforestation and forest degradation while as plantation and afforestation reassured forest growth at some places. There is a need to prioritize actions for restoration and conservation of some areas based on identified drivers to reverse the process of deforestation and forest degradation. The locations indicating negative change can be strategized under REDD+ mechanism for reclamation of forests with integrated social benefits.

Spark-based adaptive Mapreduce data processing method for remote sensing imagery

ABSTRACT Existing Hadoop-based remote sensing data processing approaches are insufficient for efficiently meeting the requirements of applications, especially when large remote sensing datasets are involved. This paper proposes an adaptive Spark-based remote sensing data processing method on the cloud that achieves improved efficiency and stability. The method includes a remote sensing data storage scheme on the cloud that employs the Hadoop Distributed File System (HDFS) and adaptive MapReduce mechanisms for use with remote sensing data; specifically, a mapping strategy for use with image tiles, a reducing strategy for use with adjacent tiles, and a mechanism for merging the results are proposed. An image classification experiment is conducted using Land Remote-Sensing Satellite System (Landsat) Thematic Mapper (TM) data, and the proposed method displays improved performance, stability and scalability compared to the existing Hadoop-based method. Hence, the proposed method is more suitable for processing large volumes of remote sensing data.

Assessing Climate Change Impact on Soil Salinity Dynamics between 1987–2017 in Arid Landscape Using Landsat TM, ETM+ and OLI Data

This paper examines the climate change impact on the spatiotemporal soil salinity dynamics during the last 30 years (1987–2017) in the arid landscape. The state of Kuwait, located at the northwest Arabian Peninsula, was selected as a pilot study area. To achieve this, a Landsat- Operational Land Imager (OLI) image acquired thereabouts simultaneously to a field survey was preprocessed and processed to derive a soil salinity map using a previously developed semi-empirical predictive model (SEPM). During the field survey, 100 geo-referenced soil samples were collected representing different soil salinity classes (non-saline, low, moderate, high, very high and extreme salinity). The laboratory analysis of soil samples was accomplished to measure the electrical conductivity (EC-Lab) to validate the selected and used SEPM. The results are statistically analyzed (p ˂ 0.05) to determine whether the differences are significant between the predicted salinity (EC-Predicted) and the measured ground truth (EC-Lab). Subsequently, the Landsat serial time’s datasets acquired over the study area with the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and OLI sensors during the last three decades over the intervals (1987, 1992, 1998, 2000, 2002, 2006, 2009, 2013, 2016 and 2017) were radiometrically calibrated. Likewise, the datasets were atmospherically and spectrally normalized by applying a semi-empirical line approach (SELA) based on the pseudo-invariant targets. Afterwards, a series of soil salinity maps were derived through the application of the SEPM on the images sequence. The trend of salinity changes was statistically tested according to climatic variables (temperatures and precipitations). The results revealed that the EC-Predicted validation display a best fits in comparison to the EC-Lab by indicating a good index of agreement (D = 0.84), an excellent correlation coefficient (R2 = 0.97) and low overall root mean square error (RMSE) (13%). This also demonstrates the validity of SEPM to be applicable to the other images acquired multi-temporally. For cross-calibration among the Landsat serial time’s datasets, the SELA performed significantly with an RMSE ≤ ± 5% between all homologous spectral reflectances bands of the considered sensors. This accuracy is considered suitable and fits well the calibration standards of TM, ETM+ and OLI sensors for multi-temporal studies. Moreover, remarkable changes of soil salinity were observed in response to changes in climate that have warmed by more than 1.1 °C with a drastic decrease in precipitations during the last 30 years over the study area. Thus, salinized soils have expanded continuously in space and time and significantly correlated to precipitation rates (R2 = 0.73 and D = 0.85).

Estimation of Shortwave Solar Radiation on Clear-Sky Days for a Valley Glacier with Sentinel-2 Time Series

Downward surface shortwave radiation (DSSR) is the main energy source for most glacial melting, and Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) data have been used extensively in the inversion of input parameters for estimating DSSR. However, for valley glaciers under complex climatic conditions, the values of MODIS atmospheric products, especially aerosol products, are often invalid, and TM images are always saturated with snow. Furthermore, an estimation model based on optical satellite images must simultaneously consider terrain and atmospheric effects and the transient nature of ice/snow albedo. Based on a high-resolution (12 m) digital elevation model (DEM), the newly launched Sentinel-2 satellites, rather than MODIS and TM, were used to provide input data for our published mountain radiation scheme in a valley glacier. Considering Laohugou Glacier No. 12 as the study area, 62 typical Sentinel-2 scenes were selected and spatiotemporal DSSR variations on the glacier surface were obtained with a 10 m spatial resolution during a mass-balance year from September 2017 to August 2018. Ground-based measurements on 52 clear-sky days were used for validation and the mean bias error (MBE = −16.0 W/m2) and root-mean-square difference (RMSD = 73.6 W/m2) were relatively low. The results confirm that DSSR is affected mainly by the solar zenith angle and atmospheric attenuation in flat areas of valley glaciers, while in areas with complex terrain, the DSSR received by the glacier surface is affected primarily by the terrain and ice/snow albedo, which exhibits very high spatial heterogeneity.

Discrete Cosine Transformation based Image Data Compression Considering Image Restoration

Discrete Cosine Transformation (DCT) based image data compression considering image restoration is proposed. An image data compression method based on the compression (DCT) featuring an image restoration method is proposed. DCT image compression is widely used and has four major image defects. In order to reduce the noise and distortions, the proposed method expresses a set of parameters for the assumed distortion model based on an image restoration method. The results from the experiment with Landsat TM (Thematic Mapper) data of Saga show a good image compression performance of compression factor and image quality, namely, the proposed method achieved 25% of improvement of the compression factor compared to the existing method of DCT with almost comparable image quality between both methods.

A Review of Landsat False Color Composite Images for Lithological Mapping of Pre-Cambrian to Recent Rocks: A Case Study of Pail/Padhrar Area in Punjab Province, Pakistan

This research is based to review the efficacy of Landsat thematic mapper (TM) on different band combinations for mapping of different geological formations ranging in age from Pre-Cambrian to most recent rocks. These band combinations include 742, 247, 564, 456, 567 and 765. Out of these, the most suitable false color composite combination is assessed for differentiation of lithology. To differentiate formations, the Landsat TM image is processed in Erdas imagine software and accordingly different lithological formations are identified on the basis of their tonal variations. Findings of the research show that 742 and 247 are the most suitable false color composite for sedimentary rocks of lower eocene (Nammal and Sakaser Formations), middle and lower Miocen (Murree and Kamlial Formations) and Pre-Cambrian (Salt Range Formation) lithological formations. The 564 and 456 FCC combinations have complexities in the identification of different lithologies; however, 456 FCC combination identified Paleocene rocks of Makarwal group (Patala Formation) and Pre-Cambrian (Salt Range Formation). The other two combinations 567 and 765 differentiate only middle Pliocene (Chinji and Nagri Formations), whereas lower eocene (Chorgali Formation) reflects fluctuating tonal behavior. The other lithologies are unidentifiable in these two combinations. Findings of the research are the base for the utilization of Landsat TM data in identification of similar lithologies in the salt range as well as in other areas having sedimentary rocks.

Characterization of short-term salinity fluctuations in the Neretva River Delta situated in the southern Adriatic Croatia using Landsat-5 TM

Abstract Elevated soil and water salinity can lead to increased risk of crop yield and quality reduction as well as land degradation. This is especially emphasised in arid and semi-arid regions with high levels of evaporation and irrigation practice. In this study, we assessed the potential of applying Landsat-5 Thematic Mapper (TM) to evaluate low to moderately salt-affected agricultural areas of the Neretva River Delta, situated in the southern Adriatic region of Croatia. Monthly measurements of water electrical conductivity (ECw), sampled from drainage/irrigation canals, were used as in situ salinity indicators. The relationship between agricultural plots reflectance (32 Landsat-5 TM images, distributed over 22 months from May 2009 to October 2011) and water salinity (measured at ten monitoring stations) was analysed using six Landsat-5 TM bands and spectral indices. Correlation analysis, simple (SLR) and multiple linear regression (MLR) models were implemented to predict ECw values using the satellite data. Very strong correlations were achieved in the spatial domain for August 2009, September 2010 and September and October 2011, while correlations in the temporal domain had relatively lower values. The Landsat-5 TM data managed to explain 43% of ECw variance using the SLR model, and 62% of the variance using the MLR model with three regressors. Landsat-5 TM remotely sensed data, paired with the in situ observations, showed a promising potential in enhancing the quality of environmental monitoring of low to moderately salt-affected agricultural fields in the irrigated deltas.

Characterization of Combined Effects of Urban Built-Up and Vegetated Areas on Long-Term Urban Heat Islands in Beijing

With the development of urbanization and industrialization, megacities have experienced more severe surface urban heat island (SUHI) effects. Land surface temperatures (LSTs) are retrieved; spatial distribution of temperature is characterized, and the relationship among temperatures or SUHIs and land-use and land cover (LULC) in Beijing City are discussed. The changing LSTs in Beijing, from 1990 to 2017, were calculated by a radiative transfer equation and mono-window algorithm. To estimate the effect of SUHI, Landsat-8 Thermal Infrared Sensor (TRIS) and Landsat-5 Thematic Mapper (TM) data were selected. There is an increasing trend toward high LSTs for different LULC types. The connection with building and vegetation density is analyzed. Results indicate that for every 1% increase in the density of buildings, the increase in amplitude of temperature in 2017 was twice as large as it was in 1995 for the study area. In terms of normalized difference vegetation index (NDVI) values, the decrease in amplitude of LST was 10 times that of the year 1995, where there is only a slight increase in the NDVI values of the area.

Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme

Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

Simulating land use for protecting food crop areas in northeast Thailand using GIS and Dyna-CLUE

Land use in the northeast region of Thailand has changed dramatically in the past two decades. These changes are mainly due to the government policies, which launched a scheme to promote rubber plantation during 2003–2013 targeting to solve the problem of poverty in the region. At least 50,000 ha of paddy fields were found to be converted to other land use types between 2002 and 2012. This study was conducted in Nong Khai and Bueng Kan province of northeast Thailand, where massive rubber plantation is going on prompting significant amount of land-use change, with the objective of investigating how land-use changes will affect on food availability in future. We analyzed land-use changes of the past and simulated future land uses using GIS and Landsat Thematic Mapper Data. The most obvious change was the decrease in paddy field and an increase in rubber plantation. This eventually leads to decreased paddy production affecting food supply of farm households. The land use projections for 2032 were done for three scenarios using Dyna-CLUE model. Unlike business as usual scenario, which will further decrease the paddy area, other scenarios with different land use policies if implemented will help protect paddy areas and thus achieving higher food production locally. The lack of implementation of proper spatial policies will lead to a further loss of paddy areas at macro level. The smallholder farmers may be highly vulnerable to land use-change and experience significant food crop losses, food insecurity and income loss when they change the land to rubber and there is market failure.

Automated mapping of rice fields using multi-year training sample normalization

ABSTRACT Rice agriculture is of great ecological, environmental, and socioeconomic importance in the Lower Mississippi Alluvial Valley, as its distribution and size heavily impact food production and a number of ecosystem services. Long-term rice mapping is challenging as a result of insufficient training data – both in spatial amount and in temporal coverage, the high cost of powerful geospatial data processing platforms, and incomplete image coverage during the critical window to capture the unique rice signals. Here, we developed a simple yet effective method for rice field extraction without heavy reliance on the complete profiles of Landsat time series or repeated training data. The core is a multiple-year training sample normalization that extends the samples obtained in one year for classification in another year. Pseudo-invariant objects and a set of linear regressions were used to predict what the given vegetation index values of training samples would be if they had been acquired under the same conditions in a different mapping year. The generated pseudo training samples were further utilized to classify the mapping image. We experimented with four years’ Landsat Thematic Mapper and Operational Land Imager data and achieved comparable accuracies as the single-year classification. Because of its simplicity and low computational requirements, it can be efficiently implemented on cloud computing platforms, such as Google Earth Engine platform. This technique provides an affordable and effective solution to derive crop distribution information on a large-scale basis.

Analysis of land use/land cover changes using geospatial techniques in Salem district, Tamil Nadu, South India

Urbanization is a global phenomenon, but the rate of urban growth is relatively faster in developing countries like India. The drastic urban growth in India has resulted in a huge loss of agricultural land and water bodies, which needs to be quantified for future planning and development. In this context, the present study depicts a combined approach of remote sensing and GIS techniques for delineating the land use/land cover dynamics of Salem district of Tamil Nadu in South India. The Landsat Thematic Mapper data of the years 1992, 2001 and 2010 and Operational Land Imager data of the year 2015 were used to assess the changes in the land use and land cover. The supervised classification technique-maximum likelihood algorithm, was used in this study to classify the remote sensing data. The results indicate that the deciduous forests, croplands, cultivation and plantation lands and water bodies have decreased from 1992 to 2015 by about 398 km2, 250 km2, 45 km2 and 16 km2, respectively. At the same time, evergreen and semi-evergreen forests, built-up lands and mines and barren lands have increased by about 288 km2, 293 km2 and 128 km2, respectively. The accuracy of the present study has been assessed by using error matrix method. The overall accuracy of the land use/land cover classification of the years 1992, 2001, 2010 and 2015 is about 88%, 95%, 93% and 93%, respectively. The study showed that the combination of remote sensing and GIS is an efficient tool to detect the changes in land use/land cover. The land use/land cover information thus obtained can be used to the decision-makers for effective land use planning and management.

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.