TM Data Research Articles

Assessing Alpine Ecosystem Dynamics over the Great Himalayan Mountain Range, Kashmir: Earth Observation and Ecosystem Modeling

Abstract The alpine vegetation distribution in the Greater Himalaya, Kashmir is preseted and its projected behavior under changing climate using LANDSAT TM (2002) and LANDSAT-8 (2014) data, mapped at 1:30000 scale and having spatial resolution of 30 m. The alpine vegetation in the region spans ~2990 km2. SRTM DEM was used to delineate the spatial extents of the alpine landscapes (>2500 m asl). A total of nine land use classes which include Alpine pasture, Alpine scrubs, bare land, dense forest, exposed rocks, lakes, rivers, snow and sparse forest were identified in the area. The results reflect both positive and negative changes in all classes over time. Alpine pastures have increased 60.11 km2 followed by Alpine scrubs, which have increased by 54.70 km2. Exposed rocks have reduced to 29.22 km2, while sparse forests have diminished by 23.62 km2. Bare land has decreased by 14.64 km2 and snow has declined by 2.04 km2. Lakes in the area have shrunk by 0.84 km2 and river by 0.02 km. The overall observed results revealed that Alpine pastures have increased by 17% followed by Alpine scrubs to 15%. The future climate projections from Worldclim (RCP 8.5) and other environmental data used in the MaxEnt model suggest increase in the suitable habitat for the shrub species by the mid of 21st century.

Improved atmospheric weighted mean temperature modeling using sparse kernel learning

As a crucial parameter in the process of converting the zenith wet delay into precipitable water vapor at Global Navigation Satellite System (GNSS) stations, the weighted mean temperature (Tm) influences the accuracy of GNSS-based water vapor retrievals. We propose an improved atmospheric Tm modeling method by introducing sparse kernel learning to obtain high-accuracy and high-temporal-resolution Tm data. To establish the model, the temporal variation characteristics of Tm time series are first analyzed by spectral analysis using the Lomb–Scargle periodogram. Second, the sparse kernel learning method is introduced to model the residuals from spectral analysis, for which the Gauss radial basis function kernel, L1-norm regularization, and the highly efficient fast iterative shrinkage thresholding algorithm are employed. To verify the performance of the proposed method, we used ERA5 hourly data from China’s 9 International GNSS Service stations produced by the European Centre for Medium-Range Weather Forecasts. ERA5 Tm data with a temporal resolution of 12 h are used as the training data, and the ERA5 hourly Tm data (excluding the data employed for modeling) are used for testing. The results show that compared with the spectral analysis accuracy, the accuracy of the calculated Tm can be improved by approximately 48.3% when the residual sparse kernel learning method is used. Thus, this proposed method for GNSS-based water vapor retrievals can provide high-accuracy and high-temporal-resolution Tm data.

The 2-Amino Group of 8-Aza-7-deaza-7-bromopurine-2,6-diamine and Purine-2,6-diamine as Stabilizer for the Adenine-Thymine Base Pair in Heterochiral DNA with Strands in Anomeric Configuration.

Stabilization of DNA is beneficial for many applications in the fields of DNA therapeutics, diagnostics, and materials science. Now, this phenomenon is studied on heterochiral DNA, an autonomous DNA recognition system with complementary strands in α‐D and β‐D configuration showing parallel strand orientation. The 12‐mer heterochiral duplexes were constructed from anomeric (α/β‐D) oligonucleotide single‐strands. Purine‐2,6‐diamine and 8‐aza‐7‐deaza‐7‐bromopurine‐2,6‐diamine 2′‐deoxyribonucleosides having the capability to form tridentate base pairs with dT were used to strengthen the stability of the dA–dT base pair. T m data and thermodynamic values obtained from UV melting profiles indicated that the 8‐aza‐7‐deaza 2′‐deoxyribonucleoside decorated with a bromo substituent is so far the most efficient stabilizer for heterochiral DNA. Compared with that, the stabilizing effect of the purine‐2,6‐diamine 2′‐deoxyribonucleoside is low. Global changes of helix structures were identified by circular dichroism (CD) spectra during melting.

Coastal morphological changes: Assessing long-term ecological transformations across the northern Bay of Bengal

Major rivers from the Himalayas carry a high volume of sedimentation, and deposit it across the Bay of Bengal in Bangladesh. This has caused significant changes in the morphology of the bay, including the development of islands across the bay area. However, few studies have been carried out on the morphological changes, especially the development of new islands across the northern Bay of Bengal. This study, therefore, aimed to assess the coastal morphological changes and ecological succession of the newly formed islands of the bay area. We used state of the art cloud computing technologies, using the Google Earth Engine (GEE) platform. Publicly available annual composites of Landsat 8, Landsat ETM+, and TM data from 1989 to 2018 were used for analysis. The findings showed significant changes in the morphology of the coastal area over a period of 30 years. There was a 1.15% increase in land area between 1989 and 2018. New islands were formed across the bay, and a few old islands disappeared between 1989 and 2018. The majority of the offshore islands developed in the estuary of the Meghna River. Among the quickly grown islands, Bhashan Char, Char Nizam, Jahajerchar, and Urir Char are prominent. Initially, the islands appeared as barren areas without any vegetation, but different types of vegetation have been observed growing on the newly formed islands recently. The findings of this study are important for the conservation and development planning of newly formed islands.

Spatio-temporal analysis of oil lake and oil-polluted surfaces from remote sensing data in one of the Libyan oil fields

The study area, which is part of the Sirt sedimentary basin in the north-central part of Libya, is characterized by natural resources of important environmental value that need special attention as they are threatened by many human activities. The focus of this study was mainly on the production of high-resolution maps of oil-contaminated surfaces, and the series time maps of events resulting from oil pollution using multi temporal satellite data and validation of the results. Digital image processing techniques were used on satellite-based sensing, whether optical or radar data, which proved to be a cost-effective way to collect information on the volume of lake water, and to assess the depth and concentration of pollution in the study area rich in lakes taken from different periods (1972 to 2006). The area of the oil-contaminated lake, called produced water, was calculated from the 1972 Landsat MSS digital satellite imagery data and was about 1.8 km2 and then increased to 10.7 km2, during 2006 from Landsat digital image TM data. The size change in this area was due to the increase of the quantities of water production that continued to increase as the oil and gas fields reached maturity. The 2019 Landsat satellite imagery reveals a drastic shrinkage in the area of the lake attributed to the suspension of the produced water pumping as well as the cycle of evaporation that resulted to the water led to a limited volume of water remaining in the lake.

Veridical Causal Inference using Propensity Score Methods for Comparative Effectiveness Research with Medical Claims.

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based inference using these datasets require thoughtful modifications to sample selection and analytic strategies relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings using medical claims. This paper offers practical guidance to the analytical process, demonstrates methods for estimating causal treatment effects with propensity score methods for several types of outcomes common to such studies, such as binary, count, time to event and longitudinally-varying measures, and also aims to increase transparency and reproducibility of reporting of results from these investigations. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insurees from 2001-2016.

Armed conflict and land-use changes: Insights from Iraq-Iran war in Zagros forests

The role of armed conflicts on land use/cover changes (LUCC), especially in Zagros forests of Iran, remains ambiguous after 30 years of the Iraq-Iran war. Our goal in this study was to assess LUCC in Sardasht related to the Iraq-Iran war in a 22 year period (1976–1998). LUCC of Sardasht city was evaluated using Landsat satellite image time series of MSS and TM data. We classified multi-temporal Landsat imagery using Random Forest classifier, then Land Change Modeler (LCM) was used to change detection and analysis. Change detection results showed that during 1976–1998, 5363.37 ha of forest areas were declined and converted to the croplands, rangelands and built-up areas. The highest decrease of forest areas was in periods of before (1976–1984) and after the war (1988–1993 and 1993–1998), 1331, 1734, and 2066 ha, respectively. While, during the war (1984–1988), only 54 ha decrease has taken place in forest lands of Sardasht. Also, increasing in other land uses during this period was significantly less than other periods. Calculation of annual rate of deforestation showed that the period of 1993–1998, has the highest rate of degradation in forest areas with a rate of −0.45%. While, during and before the war, it was −0.01% and − 0.20%, respectively. With the start of the war, residents of the region migrated to safe areas. The result of this migration was reducing forest conversion to other land uses. Trends in deforestation and forest degradation intensified after the end of the war. The causes of this destruction were destroying regulatory and control infrastructure on forests and natural resources in addition to the return of the inhabitants and an increase in demand for food and agricultural development. Moreover, after the war, the attention of the government was towards supplying the needs of human societies. Therefore, not enough attention was paid to monitoring and controlling over the degradation of natural resources. It is therefore necessary to reduce residents' dependence on natural resources through accurate and detailed planning and to increase their participation in forest conservation. In this regard, conservation programs should be continued strongly.

The long-term evolution of landslide activity near the epicentral area of the 2008 Wenchuan earthquake in China

The Wenchuan earthquake triggered numerous landslides and damaged vast areas of vegetation. Strong mountain earthquakes have long-term impacts on the activities of subsequent landslides. It is crucial for long-term geohazard risk assessment to investigate the spatio-temporal evolution of post-earthquake landslides. We used multi-year images from 2008 to 2018 to track the reactivation of coseismic landslides and newly occurring landslides to evaluate the influence of the Wenchuan earthquake on subsequent rain-induced landslides. With the aid of a GIS platform combined with field investigations, we analyzed the temporal and spatial evolutions of landslide activities over the past ten years and the factors controlling these changes. In addition, the process of vegetation recovery at landslide sites in the epicentral area was detected based on multi-year Landsat TM data after the Wenchuan earthquake. We observed that the total area of active landslides decreased significantly with time. In the first 5 years (2008–2013), the vegetation recovered quickly but was not well rehabilitated, and post-seismic landslides generally maintained high activity levels. Due to progressive revegetation and consolidation, most of the coseismic deposits along the hillslopes gradually stabilized. Subsequently (2013–2018), landslide activity weakened and mainly occurred in high and steep areas with poor vegetation recovery, particularly in proximity to the drainage network. Decreasing occurrence of rain-induced landslides was closely related to vegetation recovery in both time and space, and vegetation recovery could promote the reduction in post-earthquake landslide activities. We also proposed a quantitative model for the landslide rate in the epicentral area of the Wenchuan earthquake, which shows that the enhanced landslide rate reached its peak after the earthquake and will decrease to the pre-earthquake level in 25 years. The evolution of the post-seismic landslide activity can be categorized into four stages: high active period (2008–2013), low active period (2013–2018), recovery period (2018–2033), and stable period (after 2033).

Snow cover mapping, topographic controls and integration of meteorological data sets in Din-Gad Basin, Central Himalaya

Din-Gad basin, located in central Himalaya is occupied by the Dokriani glacier in upper reaches. Landsat TM, OLI and Sentinel MSI data sets of seven years from 2009 to 2011 and 2013 to 2016 are utilized to map the areal extent of snow-cover, its seasonal variability and topographical controls (elevation, aspect, and slope). Coefficient of Variation (CV) in Snow Cover Area (SCA) below the elevation of ~5000 m asl is high (~2.0), and is low at higher elevations. CV is also higher for southern as compared to northern aspects. Meteorological data (temperature, relative humidity, and solar radiation) from Automatic Weather Stations (AWSs) is utilized to compute isotherms (~0, 1, and 2 °C) by extrapolation method using Near-surface Temperature Lapse Rate (NSTLR). These data sets are integrated and presented by graphical as well as on Geographical Information System (GIS). Month-wise isotherms closely correspond with snow-cover extent during ablation season, suggesting that the extrapolated temperatures represent a significant proxy for depletion pattern, while month-wise and hourly isotherms deviate largely from the SCA during winter months due to snow events. Elevation zones above 5200 m asl retain snow throughout the year, indicating proximity with Equilibrium Line Altitude (ELA). The effects of Relative Humidity (RH) and solar radiation on melting history during different seasons have also emerged. The proposed methodology is efficient and reliable in terms of estimating the depletion patterns, and deriving the isotherms that can be applied to other comparable snow-covered areas.

Rapid Diffusion of Combine Harvesters in Cambodian Rice Farming: A Business Analysis

Combine harvesters have become widely used in recent years in rice farming in Cambodia on a custom-hiring basis. This study examines factors promoting investment in combine harvesters and the effects of the surge in new entrants in the custom-harvesting business. Analysis of 30 custom harvesters’ data collected by the researcher in Takeo province indicates that the high profitability of the business, decrease in interest rate, and increasing price of land used as collateral prompted land-rich households to enter the custom-harvesting business by taking large loans from financial institutions. The secondhand combine harvesters’ market in Vietnam has helped custom harvesters to sell their used machines to buy new ones. The surge in new entrants to the business, however, has driven down custom-harvesting service fees and has, thus, made the business unprofitable. This has partly been caused by financial institutions that have continued to provide funds to new entrants without anticipating excessive entry into the custom-harvesting business.

Rapid diffusion of combine harvesters in Cambodian rice farming: a business analysis.

Combine harvesters have become widely used in recent years in rice farming in Cambodia on a custom-hiring basis. This study examines factors promoting investment in combine harvesters and the effects of the surge in new entrants in the custom-harvesting business. Analysis of 30 custom harvesters’ data collected by the researcher in Takeo province indicates that the high profitability of the business, decrease in interest rate, and increasing price of land used as collateral prompted land-rich households to enter the custom-harvesting business by taking large loans from financial institutions. The secondhand combine harvesters’ market in Vietnam has helped custom harvesters to sell their used machines to buy new ones. The surge in new entrants to the business, however, has driven down custom-harvesting service fees and has, thus, made the business unprofitable. This has partly been caused by financial institutions that have continued to provide funds to new entrants without anticipating excessive entry into the custom-harvesting business.

Glacier Changes and Their Linkage to the Climate-Topographic Context in the Borohoro Mountains, Tian Shan 1977–2018

Against the backdrop of climate change and socio-ecological sustainability, studying glacier changes provides essential knowledge to the basic water needs and security for regions and populations under such threats, such as Central Asia. Little attention has focused on glaciers in the northern periphery of the Chinese Tian Shan. This study aims to map a recent glacier inventory and examine the glacier area shrinkage and surface elevation change for the central massif of the Borohoro Mountains in the past 41 years. Using declassified Hexagon images (1977), Landsat 5 TM (1994 and 2007), Sentinel 2A (2018) and altimetry data from the Ice, Cloud and Land Elevation Satellite (ICESat) over 2003–2009 with the 30-m Shuttle Radar Topography Mission (SRTM) digital elevation model, multi-temporal glacier fluctuations and the influence of topographic and climatic factors were investigated. Results show that the glacier area decreased from 287.5 ± 8.2 km2 in 1977 to 215.8 ± 4.1 km2 in 2018, at a rate of 0.61 ± 0.01% year−1. Glacier disintegration has led to a gradual increase in the number of glaciers and reached 224 glaciers in 2018. The shrinkage was at the highest rate during the 1994–2007 period and the smallest during 1977–1994. Glacier size, hypsometry, and median, maximum, and range of elevation are the most significantly correlated parameters with the relative area change. The surface elevation changes from two of the largest glaciers revealed a stronger thinning on the southern slope compared to the northern slope. These observations of glacier loss are primarily driven by the marked warming trend since the 1970s and confirmed with the overall pattern of glacier retreat in the Tian Shan from previous studies.

A machine learning approach to estimation of phase diagrams for three-component lipid mixtures

The plasma membrane of eukaryotic cells is commonly believed to contain ordered lipid domains. The interest in understanding the origin of such domains has led to extensive studies on the phase behavior of mixed lipid systems. Three-component phase diagrams, composed of a high melting temperature (Tm) lipid, cholesterol, and a low Tm lipid have been valuable in studying lipid phase behavior. However, developing phase diagrams over the entire composition space and with precise tie-lines requires significant experimental effort. In this study, a machine learning approach was used to predict the Tm of lipids and generate phase diagrams from lipid mixtures. First, artificial neural network (ANN) was used for the prediction of Tm. The network was trained using available Tm data and was able to generate Tm values that closely matched literature results for its testing dataset. This model was then used to predict the Tm for lipids that have not yet been experimentally tested. Then, random forests (RF) and support vector machines (SVM) were trained and tested for their ability to predict a test three-component phase diagram. The model from the RF algorithm was able to generate a diagram that closely matched published results. This model was then used to generate phase diagrams for lipid mixtures at various temperatures and various degrees of unsaturation. This machine learning approach to the generation of lipid phase diagrams has the potential to save significant time and resources in studies of lipid phase behavior.

Remote Sensing-Based Mapping of Senescent Leaf C:N Ratio in the Sundarbans Reserved Forest Using Machine Learning Techniques

Carbon to nitrogen ratio (C:N) of senescent leaf is a crucial functional trait and indicator of litter quality that affects belowground carbon and nitrogen cycles, especially soil decomposition. Although mapping the C:N ratio of fresh mature canopies has been attempted, few studies have attempted to map the C:N ratio of senescent leaves, particularly in mangroves. In this study, four machine learning models (Stochastic Gradient Boosting, SGB; Random Forest, RF; Support Vector Machine, SVM; and Partial Least Square Regression, PLSR) were compared for testing the predictability of using the Landsat TM 5 (LTM5) and Landsat 8 to map spatial and temporal distribution of C:N ratio of senescent leaves in Sundarbans Reserved Forest (SRF), Bangladesh. Surface reflectance of bands, texture metrics of bands and vegetation indices of LTM5 and Landsat 8 yearly composite images were extracted using Google Earth Engine for 2009–2010 and 2019. We found SGB, RF and SVM were significant different from PLSR based on MAE, RMSE, and R2 (p < 0.05). Our results indicate that remote sensing data, such as Landsat TM data, can be used to map the C:N ratio of senescent leaves in mangroves with reasonable accuracy. We also found that the mangroves had a high spatial variation of C:N ratio and the C:N ratio map developed in the current study can be used for improving the biogeochemical and ecosystem models in the mangroves.

A second generation of the neural network model for predicting weighted mean temperature

In global navigation satellite system (GNSS) meteorology, the weighted mean temperature (Tm) is a variable parameter in the conversion between zenith wet delay errors of GNSS and precipitable water vapor. The combined models of Tm, which are modeled with a combination of Tm seasonal variations and relationships between Tm and site meteorological measurements (mainly site measured temperature), have been proven to be of relatively higher accuracy. In this study, an improved combined model for Tm called the NN-II model was developed and is the second generation of the NN model. Similar to the NN model, NN-II is a combined model and is modeled by using the neural network model. The NN model was only designed for Tm estimates near the surface, while NN-II was designed for Tm estimates from the surface to almost the top of the troposphere. Compared with the NN model, the NN-II model shows some advanced features in terms of model design: modeled Tm data cover from the surface to almost the top of the troposphere, a more accurate seasonal Tm from the GTrop-Tm model is used, and the input variables are refined. Due to these refinements, the bias and RMSE of NN-II for global Tm from the surface to almost the top of the troposphere are 0.08 K and 3.34 K, respectively, and this new model shows 29.1% and 40.6% improved accuracies compared to those of the GTrop-Tm model and the NN model, respectively. The accuracy advantage is maintained over different heights of the troposphere on a global scale.

Shoreline changes analysis of Kendal Coastal Area

Shoreline changes occur due to abrasion and accretion processes that are triggered due to intensive human activities in coastal areas. The shoreline in the Kendal region is dynamic, and shows mixed results from tidal and dominated by riverine activity. The Shoreline changes have different impacts, all of which may not be visible. Coastal dynamics in the Kendal region cause spatial-temporal shoreline changes. To observe these we need a long and continuous set of data. The purpose of this study is to determine changes in the shoreline in parts of the Kendal coastal district and to calculate the value of the changes. The data used were derived from TM data 1990, to OLI data 2017. To derive the shoreline from the satellite data, a band ratio of Red band to IR band was employed. The shorelines were digitized and saved along with the appropriate MM/DD/YYYY added to the attribute table. The Digital Shoreline Analysis System (DSAS) was used to compute rate-of-change statistics. The Digital Shoreline Analysis System (DSAS) is an extension of ArcGIS, developed by USGS. Using the extension, transects were laid every 100 m. Then, using NET SHORELINE MOVEMENT (NSM), LINEAR REGRESSION RATE (LRR) and END POINT RATE (EPR), the changes every 100 meters were analyzed and stored in a table. Subsequently, using the table, erosion, and accretion, were analyzed, and understood. The farthest accretion process was 1763.29 m and 792.14 m for the abrasion processes.

Determinant Factors of Supply Chain Technology Adoption among Sabah Small and Medium Enterprises (SMEs)

Small and Medium Enterprises (SMEs) are the key industry in Malaysia which they play an important economic and social role and often contributed to innovation to the country. However, the SMEs in Malaysia still face great challenges within their supply chain such as limited technology access, low productivity, and poor managerial capabilities. Sabah SMEs has low production levels and had to slow down the development of products and services. It will continue affecting the overall operation of Sabah SMEs. As Sabah SMEs have the biggest contribution to Sabah’s development and provide many job opportunities, thus, it is needed for this study to identify determinant factors that influence the adoption of supply chain technology within Sabah SMEs such as perceived usefulness, complexity, compatibility and top management support. This study use diffusion of innovation (DoI) theory as underpinning theory to explain the adoption of supply chain technology according to the aspects of innovation attributes and organizational factor. This study employs a quantitative approach through a survey questionnaire distributed purposefully to Sabah SMEs in all sectors (manufacturing, services, and agriculture). The respondents’ data obtained will be analysed through Statistical Package for Social Sciences (SPSS) for descriptive analysis while statistical data will be analysed through Structural Equation Modelling (SEM) via Partial Least Squares (PLS). The outcome is to examine the relationship between the variables and the extent to which the relationship between the variables was related to each other. Hence,this finding may help the SMEs entrepreneur, researchers, and policy-makers to realize and make a refinement on supply chain technology adoption among Sabah SMEs.

AGA-SVR-based selection of feature subsets and optimization of parameter in regional soil salinization monitoring

ABSTRACTSalinization is a major eco-environmental threat in arid and semi-arid regions. Machine learning combined with salinization-related factors extracted from satellite images and Digital Elevation Model (DEM) data to monitor salinization is popular in recent years. The hyper-parameters referring to the parameters prior to fitting the model to the data and the features referring to the factors used for model establishment are vital to modelling accuracy, while the optimization of the above two was not given enough attention. In this study, we proposed a novel approach for simultaneously identifying the input features and hyper-parameters of Support Vector Regression (SVR) based on Adaptive Genetic Algorithm (AGA) for quantitative assessment of salinization in Weigan-Kuqa river delta oasis (Wei-Ku oasis), Sangong River Basin, and Qitai oasis of Xinjiang. First, a total of 41 salinization-related factors of 7 categories were extracted from Landsat 5 TM and DEM data. In each sub-region, the Pearson’s correlation analysis was developed between Soil Salt Content (SSC) and salinization-related factors, and the factors significantly correlated with SSC were arranged in descending order of absolute correlation coefficient to form the Candidate Feature Variables (CFVs). The ration of Coefficient of Determination (R2) and Root Mean Square Error (RMSE) multiplied by 1000 was considered as the fitness function of Genetic Algorithm (GA) and AGA. The CFVs and hyper-parameters were combined together and binary coded, then brought into the AGA and GA for simultaneous feature selection and hyper-parameters optimization of SVR, and established salinization monitoring models (AGA-SVR, GA-SVR). In order to highlight the importance of identifying the input features and hyper-parameter to modelling accuracy, the GS-SVR with all the CFVs as input was established using Grid Search (GS) algorithm to optimize the hyper-parameters. Finally, the salinization maps predicted by three models were compared. The results showed that the sensitivity of salinization-related factors to SSC varied with regions, and 25, 16, 24 salinization-related factors were selected as CFVs in Wei-Ku oasis, Sangong River Basin, and Qitai oasis, respectively. Compared with GS-SVR, the GA-SVR and AGA-SVR got more accurate salinization monitoring with fewer features as input, and fitness generated by AGA-SVR increased by 25.968% in Wei-Ku oasis, 25.159% in Sangong River Basin, 27.568% in Qitai oasis, respectively. Both nature and human factors lead to salinization. The difference of Land Surface Temperature (LST) was the main contributor of different salinization between Wei-Ku oasis of southern Xinjiang and the other two sub-regions of northern Xinjiang. The differences in soil texture, irrigation methods, and livestock carrying capacity were the main factors resulting in differences of salinization in Qitai oasis and Sangong River Basin. Our study shows that the proposed approach can provide technical support for accurate salinization monitoring.

Quantifying the Relative Importance of Climate Change and Human Activities on Selected Wetland Ecosystems in China

Climate change and human activities are important factors driving changes in wetland ecosystems. It is therefore crucial to quantitatively characterize the relative importance of these stressors in wetlands. Previous such analyses have generally not distinguished between wetland types, or have focused on individual wetland types. In this study, three representative wetland areas of the upper, middle and lower reaches of the Heilongjiang River Basin (HRB) were selected as the study area. An object-based classification was used with Landsat TM data to extract the spatial distribution of wetland in 1990, 2000 and 2010. We then quantified the relative importance of climate change and human activities on the wetlands by using the R package “relaimpo” package. The results indicated that: (1) the effects of human activities on wetland changes were greater (contribution rate of 63.57%) than climate change in the HRB. Specifically, there were differences in the relative importance of climate change and human activities for wetlands in different regions. Wetlands of the upper reaches were more affected by climate change, while wetlands in the middle and lower reaches were more affected by human activities; (2) climate change had a greater impact (contribution rate of 65.72%) on low intensity wetland loss, while human activities had a greater impact on moderate and severe intensity wetland loss, with respective contribution rates of 57.22% and 70.35%; (3) climate change had a larger effect on the shrub and forested wetland changes, with respective contribution rates of 58.33% and 52.58%. However, human activities had a larger effect on herbaceous wetland changes, with a contribution rate of 72.28%. Our study provides a useful framework for wetland assessment and management, and could be a useful tool for developing wetland utilization and protection approaches, particularly in sensitive environments in mid- and high-latitude areas.

Technical Report: A Remote Sensing Study of Selected Areas in Dogtown Using Multi-Temporal Landsat Data

This report presents a preliminary remote sensing study of an expanse of woodlands in the middle of Cape Ann, Massachusetts known as Dogtown is described. Using 35 years of Landsat TM data provided through Google’s Earth Engine we compare and contrast vegetation changes at selected study areas in Dogtown. Successional forests, swamps, fens, and other areas are examined. Preliminary results reveal anomalous changes in two fens, one of which may have been caused by the operation of a nearby municipal compost facility.