Monitoring Of Environmental Changes Research Articles

An attention U-Net model for detection of fine-scale hydrologic streamlines

Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundation, and monitoring environmental changes. Conventional approaches to detecting such streamlines cannot adequately incorporate information from the complex three-dimensional (3D) environment of streams and land surface features. Such information is vital to accurately delineate streamlines. In recent years, high accuracy lidar data has become increasingly available for deriving both 3D information and terrestrial surface reflectance. This study develops an attention U-net model to take advantage of high-accuracy lidar data for finely detailed streamline detection and evaluates model results against a baseline of multiple traditional machine learning methods. The evaluation shows that the attention U-net model outperforms the best baseline machine learning method by an average F1 score of 11.25% and achieves significantly better smoothness and connectivity between classified streamline channels. These findings suggest that our deep learning approach can harness high-accuracy lidar data for fine-scale hydrologic streamline detection, and in turn produce desirable benefits for many scientific domains.

Stream biodiversity and monitoring in North Central, Nigeria: the use of macroinvertebrate indicator species as surrogates.

Indicator species (IS) have been employed in modern aquatic research for monitoring of environmental changes and evaluating the efficiency of environmental management procedures. In this study, we evaluated the possibility of developing surrogate indicator groups as tools for the conservation and management of the biodiversity of Northern Nigeria streams by surveying 15 streams in Niger state for benthic macroinvertebrates and environmental variables as data sets, over a period of 24 months (2016 and 2017). Samples were collected in two locations of reference and impacted sites for each of the streams surveyed. The statistically significant (P < 0.05; based on 1000 permutations) indicator species for each of the status classes (reference versus impacted) was identified using the indicator species analysis/indicator value (Indval) method. Canonical correspondence analysis (CCA) was used to evaluate the IS-environment relationships. Indicator value found fifteen species for the reference streams including Ephemeropteran (Bugilliesia sp., Tricorythus sp., Thraulus sp., Crassabwa sp.) and the Tricopteran (Leptonema sp.). Opposite, the Indval found seven (7) indicator species for the impacted streams, which included the Dipteran (Pentaneura sp., Tabanus sp.). Multivariate analysis revealed that species assemblage had wide dispersal patterns in relation to the sites for both status classes. CCA revealed that the reference and impacted indicator species responded to entirely different environmental factors, indicating their preference to particular environmental variables along the ecological gradients. While the indicator species of reference sites were associated with environmental predictors of good water quality such as high DO, increased flow, low conductivity, and low BOD, the indicator species of impacted sites were strongly related to environmental predictors of anthropogenic pollution, including low DO, high BOD, and increased nutrients concentrations. This study has provided a reference point and effective tool to monitor environmental changes, community, and ecosystem dynamics across the Northern Nigeria streams.

Remote Sensing &amp; Geographical Information System in Sudan to determine children'sMalnutrition caused by water &amp; environmental factors.

background: sudan has a huge burden of severe acute malnutrition (sam), khartoum state; where this study was conducted is among the top states with increasing number of severely malnourished children in sudan. rapid urbanization in khartoum state has put huge pressure on water supplies. moreover, the incidence of disease is made worse by unclean water &amp; poor hygiene conditions in most poor neighborhoods. furthermore, flooding in khartoum has a major impact on health as pools of stagnant water, coupled with poor sanitation lead to infectious diseases. remote sensing &amp; geographical information system (rs/gis) are applied as instruments.. the main objectives of this study were to investigate applying the gis technique to map the distribution of young children with sam ages 6-59 months in khartoum state in 2016, &amp; to detect the relationship between environmental deficiencies &amp; sam cases.methods: health data we used included records of 1150 cases of children with complicated sam &amp; 5796 children with non-complicated sam &amp; applying a retrospective study based on hospitals and outpatient therapeutic programme (otps). using rs/gis integrated spatial &amp; non-spatial factors.results: showed variation of distribution of children with sam in khartoum state as shown by thematic maps influenced by several factors such as: demographic, maternal, socioeconomic &amp; environmental factors. sam cases are concentrated in administrative units (aus) areas where poverty, limited drinking water resources &amp; poor hand washing practices is more preeminent such as alnaser &amp; asalam aus. furthermore, the results showed variation in distribution of sam cases in khartoum state over months of the year (attributed to meteorological factors &amp; floods).conclusion: both rs &amp; gis technologies have the capability to link &amp; update the information on disease (sam) &amp; monitoring of environmental changes through mapping of cases.

EOLakeWatch; delivering a comprehensive suite of remote sensing algal bloom indices for enhanced monitoring of Canadian eutrophic lakes

Early detection and comprehensive monitoring of inland water algal blooms is fundamental to their effective management and mitigation of potential ecosystem and public health impacts. With the spatial and temporal limitations of in situ sampling, algal bloom monitoring capabilities have been enhanced greatly by advancements in satellite Earth Observation (EO). Three turbid, eutrophic Canadian lakes (Lake Winnipeg (LW); Lake Erie (LE); Lake of the Woods (LoW)) have been the focus of Environment and Climate Change Canada (ECCC) research and monitoring initiatives due to concerns over persistent degraded water quality from recurring algal blooms. ECCC’s EOLakeWatch was developed to deliver a suite of useful, easily interpretable, and accessible EO-derived products to support algal bloom monitoring on these three lakes. Algal bloom indices, describing bloom spatial extent, intensity, duration, and severity were derived using the European Space Agency’s OLCI (Ocean and Land Colour Instrument) sensor for observations from 2016 to present and its predecessor MERIS (Medium Resolution Imaging Spectrometer) for 2002 to 2011. Results document widespread blooms on each lake, with maximum spatial extent of 21,641 km2 (representing 88.1% of the lake area) on LW, 3070 km2 (79.5%) on LoW and 5257 km2 (19.7%) on LE. Bloom intensity showed seasonal and inter-annual variability on all three lakes, with a suggestion that LoW may be responding to reduced nutrient loads with a recent decrease in bloom intensity. Annual bloom duration on LW and LoW was on average 44 and 47 days respectively, while on LE blooms were significantly shorter in duration at an average of 24 days. Variance among the derived bloom indices was shown to be significant (i.e. the most extensive bloom was not necessarily the longest or most intensive), demonstrating the need for the indices to be used collectively, or for any single comprehensive bloom indicator to capture the variability of all individual metrics. Bloom indices are processed in a fully automated operational capacity, distributed in near-real-time through a web portal and collated into end-user-friendly annual algal bloom reports for each lake. These products go a long way to address existing monitoring gaps, delivering prompt, consistent measures of lake-wide algal bloom conditions required to provide stakeholders with early warning of bloom risks, identify areas of potential concern, quantify spatio-temporal trends, further understand bloom dynamics and drivers, as well as guide and determine the effectiveness of implemented management actions.

Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data

Monitoring of abnormal changes on the earth’s surface (e.g., forest disturbance) has improved greatly in recent years because of satellite remote sensing. However, high computational costs inherently associated with processing and analysis of satellite data often inhibit large-area and sub-annual monitoring. Normal seasonal variations also complicate the detection of abnormal changes at sub-annual scale in the time series of satellite data. Recently, however, computationally powerful platforms, such as the Google Earth Engine (GEE), have been launched to support large-area analysis of satellite data. Change detection methods with the capability to detect abnormal changes in time series data while accounting for normal seasonal variations have also been developed but are computationally intensive. Here, we report an implementation of BFASTmonitor (Breaks For Additive Season and Trend monitor) on GEE to support large-area and sub-annual change monitoring using satellite data available in GEE. BFASTmonitor is a data-driven unsupervised change monitoring approach that detects abnormal changes in time series data, with near real-time monitoring capabilities. Although BFASTmonitor has been widely used in forest cover loss monitoring, it is a generic change monitoring approach that can be used to monitor changes in a various time series data. Using Landsat time series for normalised difference moisture index (NDMI), we evaluated the performance of our GEE BFASTmonitor implementation (GEE BFASTmonitor) by detecting forest disturbance at three forest areas (humid tropical forest, dry tropical forest, and miombo woodland) while comparing it to the original R-based BFASTmonitor implementation (original BFASTmonitor). A map-to-map comparison showed that the spatial and temporal agreements on forest disturbance between the original and our GEE BFASTmonitor implementations were high. At each site, the spatial agreement was more than 97%, whereas the temporal agreement was over 94%. The high spatial and temporal agreement show that we have properly translated and implemented the BFASTmonitor algorithm on GEE. Naturally, due to different numerical solvers being used for regression model fitting in R and GEE, small differences could be observed in the outputs. These differences were most noticeable at the dry tropical forest and miombo woodland sites, where the forest exhibits strong seasonality. To make GEE BFASTmonitor accessible to non-technical users, we developed a web application with simplified user interface. We also created a JavaScript-based GEE BFASTmonitor package that can be imported as a module. Overall, our GEE BFASTmonitor implementation fills an important gap in large-area environmental change monitoring using earth observation data.

New records of seaweeds and filamentous cyanobacteria from Trindade Island: an updated checklist to support conservation guidelines and monitoring of environmental changes in the southern Atlantic archipelagos

Oceanic islands are natural laboratories for investigating species diversity and richness patterns. Changes in abiotic parameters may induce shifts in marine biota. Seaweeds are recognized as bioindicators, though those from remote tropical islands have been rarely studied. This study updates the diversity, richness and distribution of macroalgae from Trindade, a Brazilian volcanic island located 1140 km off the coast. Biotic data, obtained in a global database and in situ and compiled in a new records list, were associated with abiotic parameters. Conservation and ecological issues were discussed in the context of the observed greater richness, expansion of the distributional range and low endemism. A total of 141 species were identified, including 60 new records and 20 taxa of filamentous cyanobacteria. The greater richness, including potential cryptogenic species, may primarily be associated with past incomplete samplings, current new techniques and combined taxonomical methods, including molecular analysis for cryptic species. However, on the macroscale, this study provides information for the re-evaluation of aspects of endemism, connections and biogeographical distribution shifts of seaweed as­semblages, considering environmental changes. In addition, this updated checklist establishes a baseline for further compara­tive studies, reinforcing the hypothesis that biogeographical isolation can be disrupted by meteorological and oceanographic shifts, altering dispersal patterns and resulting in higher ecosystems connectivity.

Investigation of Pseudomonas aeruginosa biofilm formation within washing machines using novel nanosensor technology

Biofilms are communities of microorganisms that attach to various surfaces and are widely associated with infections. Our investigation is focussed on a current and growing concern: the formation of biofilms in washing machines. Many countries wash clothes at reduced temperatures (30°C to 40°C) rather than at higher temperatures above 60°C that would kill the bacteria. Survival of the bacteria is associated with biofouling, malodour and an increased infection risk due to the distribution of human pathogens such as Pseudomonas aeruginosa, one of the predominant bacteria found in washing machines. Little is known about environmental microniches present in biofilms. Here, we focus on the pH variation throughout P. aeruginosa biofilms knowing that the pH can influence biofilm formation and could be an important aspect for the prevention of biofilms. We use novel pH-sensitive optical nanosensors that penetrate P. aeruginosa biofilms and emit fluorescence in response to pH variation. Using time lapse imaging, pH changes were tracked in real time at a single cell level which will ultimately facilitate monitoring of environmental changes induced as biocides penetrate biofilms. We also look at the isolation and identification of P. aeruginosa from household washing machines. Whole genome analysis was performed to identify different genomic features relevant to antimicrobial resistance (AMR) and biofilm formation. Furthermore, testing of different washing detergent formulations revealed a range of abilities to disrupt biofilm formation or kill P. aeruginosa, which will facilitate the development of more effective washing agents to limit the emergence of AMR within biofilms.

Remote Estimation of Gross Primary Production (GPP) using MODIS data in Kelantan: A Preliminary Assessment

Kelantan state is located in the north-eastern of Peninsular Malaysia, with 5.8 million hectares is covered by forested areas. Gross primary production (GPP) is the amount of carbon captured by plants and it is an important biophysical parameter in the spatio-temporal dynamics of CO2. GPP estimates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are converted to the forest area and compared. GPP is estimated through a light use efficiency model (LUE) and enhanced vegetation index (EVI) as a proxy of greenness to address spatial and temporal variation in terrestrial photosynthetic activities. This study is important because the output will be used as a reference to estimate the approximate total carbon in Kelantan. Estimation GPP of Kelantan state is also useful for natural resource and land management, regional carbon cycle analysis, ecosystem status assessment, and environmental change monitoring. The objective of this study is to estimate Gross Primary Production (GPP) value of forested area in Kelantan using MODIS data. MODIS data was preprocessed, classified and analysed to get the estimated GPP of study site. The results show that average GPP value of Kelantan was estimated within the range of 0 to 646 gC/m2/month. In average, GPP in Kelantan was at 428.63 gC/m2/month. Gua Musang recorded the highest GPP value, while the lowest GPP value detected were in Kota Bharu, Tumpat and Bachok where lot of development are found at the area. The estimation of GPP in Kelantan using MODIS data is a timely significant method. However, the requirement of more study that uses MODIS data in different terrestrial types and vegetation is needed for future study.

Can indigenous community-based monitoring act as a tool for sustainable self-determination?

This study seeks to gain a better understanding of the implications of Indigenous community-based monitoring (ICBM) for Indigenous governance in resource extractive regions. Using a comprehensive review of the literature and the author team's personal involvement, we review an ICBM program in the oil sands region of Alberta, Canada. We use sustainable self-determination, as a sub-set of Indigenous governance, as a critical theoretical lens to assess outcomes of this program and its role in the broader environmental governance of the oil sands region. To conclude, we propose some recommendations to advance a sustainable self-determination lens for ICBM. As these programs continue to proliferate across the country, now is the time to incorporate such a lens in order to simultaneously support meaningful monitoring of environmental, economic, and social change, while also advancing the resurgence of Indigenous Nations.

Sentinel-1 Image Classification Using Machine Learning Algorithms Based on the Support Vector Machine and Random Forest

Due to concerns of recent earth climate changes such as an increase of earth surface temperature and monitoring its effect on the earth's surface, environmental monitoring is a necessity. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modeling to investigate the impact of climate change phenomena such as droughts and floods on earth surface land cover. As land cover has a direct effect on Land Surface Temperature (LST), the Land cover mapping is an essential part of climate change modeling. In this paper, for land use land cover mapping (LULCM), image classification of Sentinel-1A Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) data using two machine learning algorithms including Support Vector Machine (SVM) and Random Forest (RF) are implemented in R programming language and compared in terms of overall accuracy for image classification. With seven different scenarios defined in this research, RF and SVM classification methods show their best performance with overall accuracies of 75.30 and 75.35 percent, respectively.

Near real-time mapping of air temperature at high spatiotemporal resolutions in Tasmania, Australia

Surface air temperature (Ta) is important for a wide variety of applications that require real-time monitoring of environmental change; yet, few studies have attempted to deliver such products at a resolution appropriate for local-scale analysis. We investigated the feasibility of producing hourly Ta maps in near real time at a spatial grid resolution of 80 m across the state of Tasmania (area: 68,401 km2), Australia. We first assimilate 267 non-telemetered logger recording sites for data recorded in the 1-year period from September 2013 to 2014 and statistically calibrate them to 43-telemetered Australian Bureau of Meteorology (BoM) weather station sites for real-time application. This was evaluated in the following year using held back recordings in addition to evaluating regression trees (RT) and thin plate splines (TPS) to interpolate the hourly Ta estimates. For real-time operational mapping, the system was fully automated in the R programming language and hosted on a cloud-based computing platform to assess performance over a 7-day period in February 2020. The calibration procedure yielded accurate results with the root mean square error (RMSE) ranging between 1.33 °C in summer and 1.29 °C in winter. The TPS interpolation method was optimal in summer with an RMSE of 1.35 °C compared to RT with 1.39 °C; however, RT performed better in winter with an RMSE of 1.34 °C compared to 1.42 °C. The mapping system was capable of producing spatial outputs within the hour of the BoM observations becoming available with the TPS interpolation proving to be more efficient at producing outputs in a timelier manner.

Changes in the community structure of anurans in the Coastal plain forest, southeastern Brazil

AbstractIn this study, we analyzed how variations in species richness, composition and abundance of anurans in a community could be explained by temporal environmental factors such as temperature and precipitation and spatially between different vegetation types. The study was carried out in Vale Natural Reserve, state of Espírito Santo, Brazil, from June 2015 to October 2016. We sampled the anuran community in 21 plots of 250 m in length, with 10 m of width. We recorded a total of 326 individuals of anurans, corresponding to 23 species in six families. We concluded that the anurans community of the Vale Natural Reserve remained relatively stable throughout the seasons and along vegetation types in terms of richness but differed in composition and abundance. The anurans were more abundant in the rainy season, with precipitation being the most important factor for anuran density in the area. Anuran community composition varied among vegetation types due to differences in complexity and structure of each vegetation type. Our study showed that different factors were determinant for the variation in species abundance and composition in the area. Therefore, the conservation of fragments and their associated habitat and the monitoring of environmental changes is of fundamental importance in anuran conservation in the area.

Development of the unit for testing the methods of remote computerized monitoring of environmental changes in the “IKI-Monitoring” center for collective use

The impact of hazardous waste on people and the environment from agricultural, industrial, military and mining activities form one of the most significant global environmental challenges. These wastes often include heavy metals, acid precipitates (sulfur oxide solutions), hydrocarbons, and other organic chemicals. Creation of the tools of the VEGA-Science service (http://sci-vega.ru/) to control the state of vegetation for the monitoring the impact of technogenic waste and dumps sources (TWDS) on the environment is the main objective of this work. To achieve this goal, proposals for the modernization and development of the VEGA-Science services have been defined to provide the organization and analysis of long-term variability of the vegetation cover caused by the influence of TWDS.

Automated training sample definition for seasonal burned area mapping

Monitoring of environmental change can benefit from the increasing availability of multitemporal satellite imagery, and efficient and effective analysis tools are needed to generate relevant spatio-temporal land cover datasets. We present a data driven approach for automatic training sample selection to support supervised spatio-temporal mapping of seasonally burned areas in the semi-arid savannas of Southern Africa. Our approach leveraged the distinctive spectral-temporal trajectories associated with areas on the landscape burned at different times or areas remaining unburned over time. Using fuzzy c-means clustering, we extracted distinctive trajectories from the multitemporal mid-infrared burn index (MIRBI) data derived from Landsat data and characterized them based on empirically developed labeling rules. The selected training trajectories captured both the burn condition (burned or unburned) and if burned, the timeframe of the burn event. We assessed the approach by training a Random Forests model using over 2500 automatically selected training data and validated the model against ground truth for years 2009 and 2014. Based on over 1000 validation points in each year, we obtained overall accuracies above 90% showing reliable and consistent training data were supplied by our automatic training sample selection approach. The method provides a data driven and automatic approach which can reduce the time-consuming and expensive training task, enabling quicker generation of relevant burned area information that can support fire monitoring programs and climate change research.

Hydrometeorological and gravity signals at the Argentine-German Geodetic Observatory (AGGO) in La Plata

Abstract. The Argentine-German Geodetic Observatory (AGGO) is one of the very few sites in the Southern Hemisphere equipped with comprehensive cutting-edge geodetic instrumentation. The employed observation techniques are used for a wide range of geophysical applications. The data set provides gravity time series and selected gravity models together with the hydrometeorological monitoring data of the observatory. These parameters are of great interest to the scientific community, e.g. for achieving accurate realization of terrestrial and celestial reference frames. Moreover, the availability of the hydrometeorological products is beneficial to inhabitants of the region as they allow for monitoring of environmental changes and natural hazards including extreme events. The hydrological data set is composed of time series of groundwater level, modelled and observed soil moisture content, soil temperature, and physical soil properties and aquifer properties. The meteorological time series include air temperature, humidity, pressure, wind speed, solar radiation, precipitation, and derived reference evapotranspiration. These data products are extended by gravity models of hydrological, oceanic, La Plata estuary, and atmospheric effects. The quality of the provided meteorological time series is tested via comparison to the two closest WMO (World Meteorological Organization) sites where data are available only in an inferior temporal resolution. The hydrological series are validated by comparing the respective forward-modelled gravity effects to independent gravity observations reduced up to a signal corresponding to local water storage variation. Most of the time series cover the time span between April 2016 and November 2018 with either no or only few missing data points. The data set is available at https://doi.org/10.5880/GFZ.5.4.2018.001 (Mikolaj et al., 2018).

Remote Sensing and Geographical Information System to map environmental factors associated with Children Malnutrition Severity in Khartoum State, Sudan

TPS 723: Low and middle income countries: sanitation infections and other health effects, Exhibition Hall, Ground floor, August 26, 2019, 3:00 PM - 4:30 PM Severe Acute Malnutrition (SAM) is a major health problem in the World. The available data on nutritional status of children under 5 years old in Sudan reflects the worse and miserable situation, especially in the marginalized rural areas. Khartoum State; where this study was conducted is among the top five States with increasing number of severely malnourished children in Sudan. Remote Sensing and Geographical Information System (RS/GIS) are gradually becoming popularly applied in many researches including epidemiology, and have been indispensably available instruments environmental resource management and programming. The main objectives of this study were to investigate applying GIS technique to map the distribution of children with SAM age (6-59) months in Khartoum State in 2016 (Jan-Dec), and to detect the relationship between environmental deficiencies and SAM cases using RS data and GIS. This study sited in Khartoum State including 36 Administrative Units, 14 sited hospitals and 21 of Outpatient Therapeutic Programme (OTP). The health data used included records of 1150 children cases with complicated SAM and 5796 children with non-complicated SAM over the study period. Retrospective study, based on Hospitals and OTPs. Using RS/GIS integrated spatial and non-spatial factors. The study used different RS and GIS technologies Software. The result showed thematic maps which present the variation of distribution of SAM children in Khartoum State. Influence by several factors such as socioeconomic and environmental realities of Khartoum State. SAM cases are concentrated in AUs areas where poverty, worse drinking water resources and lack of sanitation such as Alnaser AU and Asalam AU. This research concluded that both RS and GIS technologies have the capability to link and update the information on disease (SAM) and monitoring of environmental changes through mapping of cases.

SENTINEL-1 IMAGE CLASSIFICATION FOR CITY EXTRACTION BASED ON THE SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS

Abstract. Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modeling to investigate the impact of climate change phenomena such as droughts and floods on earth surface land cover. As land cover has a direct impact on Land Surface Temperature (LST), the Land cover mapping is an essential part of climate change modeling. In this paper, for land use land cover mapping (LULCM), image classification of Sentinel-1A Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) data using two machine learning algorithms including Support Vector Machine (SVM) and Random Forest (RF) are implemented in R programming language and compared in terms of overall accuracy for image classification. Considering eight different scenarios defined in this research, RF and SVM classification methods show their best performance with overall accuracies of 90.81 and 92.09 percent respectively.

Urban Impervious Surface Detection From Remote Sensing Images: A review of the methods and challenges

Urban impervious surface (UIS) offers potentially valuable information for the development of sustainable urban management strategies and environmental change monitoring actions. In this article, we focus on UIS-detection-related issues, identify and analyze systematic challenges, construct a general processing scheme for UIS detection, provide four categories of major methods, and propose a deep compressive-based convolutional deep belief network (dc-CDBN) method and a novel classification strategy for multisource data fusion models.

Intercomparison of AVHRR GIMMS3g, Terra MODIS, and SPOT-VGT NDVI Products over the Mongolian Plateau

The rapid development of remote sensing technology has promoted the generation of different vegetation index products, resulting in substantive accomplishment in comprehensive economic development and monitoring of natural environmental changes. The results of scientific experiments based on various vegetation index products are also different with the variation of time and space. In this work, the consistency characteristics among three global normalized difference vegetation index (NDVI) products, namely, GIMMS3g NDVI, MOD13A3 NDVI, and SPOT-VGT NDVI, are intercompared and validated based on Landsat 8 NDVI at biome and regional scale over the Mongolian Plateau (MP) from 2000 to 2014 by decomposing time series datasets. The agreement coefficient (AC) and statistical scores such as Pearson correlation coefficient, root mean square error (RMSE), mean bias error (MBE), and standard deviation (STD) are used to evaluate the consistency between three NDVI datasets. Intercomparison results reveal that GIMMS3g NDVI has the highest values basically over the MP, while SPOT-VGT NDVI has the lowest values. The spatial distribution of AC values between various NDVI products indicates that the three NDVI datasets are highly consistent with each other in the northern regions of the MP, and MOD13A3 NDVI and SPOT-VGT NDVI have better consistency in expressing vegetation cover and change trends due to the highest proportions of pixels with AC values greater than 0.6. However, the trend components of decomposed NDVI sequences show that SPOT-VGT NDVI values are about 0.02 lower than the other two datasets in the whole variation periods. The zonal characteristics show that GIMMS3g NDVI in January 2013 is significantly higher than those of the other two datasets. However, in July 2013, the three datasets are remarkably consistent because of the greater vegetation coverage. Consistency validation results show that values of SPOT-VGT NDVI agree more with Landsat 8 NDVI than GIMMS3g NDVI and MOD13A3 NDVI, and the consistencies in the northeast of the MP are higher than northwest regions.

Identification and temporally-spatial quantification of geomorphic relevant changes by construction projects in loess landscapes: case study Lanzhou City, NW China

ABSTRACTSustainable development of urban areas demands, among others, a holistic approach for identification and monitoring of environmental changes caused by human activities. In this study, the human-made impact on the geo-environment is studied through the application of the change detection study based on Principal Component Analysis on Landsat imageries and comparison of the digital elevation models taken at different times. The analysis is set up to identify and quantify the anthropogenic geomorphological changes in the loess landscape in Lanzhou city, Northwest China. Since 2002 Lanzhou has been undergoing a rapid economic development associated with construction boom. Due to limited flat building ground in the narrowed Yellow River Valley and subsequent expansion into the surrounding loess mountains, massive earthworks are conducted for reclamation of the suitable building ground. The results of the change detection analysis show that approximately 10% of the semi-natural study area corresponding to 35 km2 has been reshaped by leveling and terracing since 1994. In particular, the geomorphology was significantly changed in these and adjacent areas. For the single developing area Taipingyang, a moving volume of up to 57 million m3 was roughly estimated.