Land Use Land Cover Analysis for Godavari Basin in Maharashtra Using Geographical Information System and Remote Sensing

The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.

Simulation of urban affordable housing land-use evolution based on CA-MAS model

In order to simulate the evolution of affordable housing land use a dynamic model that combines cellular automata CA and a multi-agent system MAS is established.This paper aims to utilize the approach of decision factors on site selection of affordable housing through a literature review to construct a hierarchy model of those factors identifying the weight of each factor by an analytic hierarchy process AHP .Based on those weight factors the CA-MAS model is designed. Nanjing city is taken as an example to verify the feasibility of the model.The results show that the CA-MAS model is pragmatic and effective in simulating evolution of affordable housing land use which also promotes the fundamental understanding and perception of the development of affordable housing and urbanization.

Simulation of Land-use Scenarios for Beijing Using CLUE-S and Markov Composite Models

This study investigated and simulated land use patterns in Beijing for the year 2000 and the year 2005 from the actual land use data for the year 1995 and the year 2000,respectively,by combining spatial land allocation simulation using the CLUE-S model,and numerical land demand prediction using the Markov model.The simulations for 2000 and 2005 were confirmed to be generally accurate using Kappa indices.Then the land-use scenarios for Beijing in 2015 were simulated assuming two modes of development:1) urban development following existing trends;and 2) under a strict farmland control.The simulations suggested that under either mode,urbanized areas would expand at the expense of land for other uses.This expansion was predicted to dominate the land-use conversions between 2005 and 2015,and was expected to be accompanied by an extensive loss of farmland.The key susceptible to land-use changes were found to be located at the central urban Beijing and the surrounding regions including Yanqing County,Changping District and Fangshan District.Also,the simulations predicted a considerable expansion of urban/suburban areas in the mountainous regions of Beijing,suggesting a need for priority monitoring and protection.

RESEARCH ACTIVITIES ON LAND USE/COVER CHANGE IN THE PAST TEN YEARS IN CHINA USING SPACE TECHNOLOGY

Land use/cover change, which in China is characterized by urbanization resulting in a decrease in arable land in the east along with a large area of grassland being cultivated in the west, has been accelerated by rapid economic development in the last years. All of the above changes will affect sustainable development in the next century. The Chinese Academy of Sciences is conducting a study of land use/cover change over the last ten years based on the integration of remote sensing and GIS technology to establish a multitemporal database covering all of China. Fundamental data for land use/cover for the year 1996 has already been developed by the Chinese Academy of Sciences. In order to reconstruct fundamental land use/cover data for the year 1986, a central data processing and analyzing system and a regional data acquisition, processing and analyzing system have been established and are joined together as a network. After the 1986 database is established, the comparative research on the reduction in arable land, urbanization, desertification, changes in forest and grassland, and lake and wetland land use/cover change will be carried out. In addition, a transect for a key regional comparative study was selected along the Changjiang (Yangtze) River. The driving forces of these changes also will be extracted. The result of this study will be not only make a contribution to global land use/cover change research, but will also support decision making for sustainable national development.

Quantifying Land Use Change in Zhejiang Coastal Region,China Using Multi-Temporal Landsat TM/ETM+Images

Landsat Thematic Mapper （TM） and Enhanced Thematic Mapper Plus （ETM＋） images in 1985, 1986, 1993, 1994 and 2001 were used to quantify the land use and land cover changes （LUCC） in the Zhejiang coastal region with a stratified unsupervised classification technique in conjunction with visual interpretation and to attempt an identification of the socioeconomic driving forces. In level I an overall accurate classification was achieved using a modified Anderson＇s Ⅰ/Ⅱ/Ⅲ-level classification scheme. The overall accuracy of the land use classification at Anderson level Ⅰ were 89.7% （1985）, 91.6% （1993）, and 90.4% （2001）. The most rapid land use change was a dramatic increase in urban or built-up areas, which quadrupled from 1985 to 2001. Over 90% of this newly expanded built-up area was originally paddy fields or other croplands. In different parts of the Zhejiang coastal region, urban land expansion was spatially uneven. Temporally, land use development did not stabilized, and the two study periods of time （1985-1993 and 1993-2001） had different transition styles. Socioeconomic factors, such as gross domestic product, total population, and financial expenditure, were all highly correlated with the expansion of urban or built-up areas. Based on the degree of urban sprawl and socioeconomic factors, cities and towns were further divided into six subgroups, which may help decision makers improve land use for the region.

Monitoring Land Use Dynamics in Chanthaburi Province of Thailand Using Digital Remotely Sensed Images

A comprehensive method of image classification was developed for monitoring land use dynamics in Chanthaburi Province of Tailand. RS (Remote Sensing), GIS (Geographical Information System), GPS (Global Positioning System) and ancillary data were combined by the method which adopts the main idea of classifying images by steps from decision tree method and the hybridized supervised and unsupervised classification. An integration of automatic image interpretation, ancillary materials and expert knowledge was realized. Two subscenes of Landsat 5 Thematic Mapper (TM) images of bands 3, 4 and 5 obtained on December 15, 1992, and January 17, 1999, were used for image processing and spatial data analysis in the study. The overall accuracy of the results of classification reached 90%, which was verified by field check.Results showed that shrimp farm land, urban and traffic land, barren land, bush and agricultural developing area increased in area, mangrove, paddy field, swamp and marsh land, orchard and plantation, and tropical grass land decreased, and the forest land kept almost stable. Ecological analysis on the land use changes showed that more attentions should be paid on the effect of land development on ecological environment in the future land planning and management.

AN ASSESSMENT OF LAND USE CHANGES IN FUQING COUNTYOF CHINA USING REMOTE SENSING TECHNOLOGY

Fuqing County of southeast China has witnessed significant land use changes during the last decade. Re mote sensing technology using multitemporal Landsat TM images was used to characterize land use types and to monitor land use changes in the county. Two TM scenes from 1991 and 1996 were used to cover the county and a five-year time period. Digital image processing was carried out for the remotely sensed data to produce classified images. The images were further processed using GIS software to generate GIS databases so that the data could be further spatially analyzed taking the advantages of the software. Land use change areas were determined by using the change detection technique. The comparison of the two classified TM images using the above technologies reveals that during the five study years, a large area of arable lands in the county has been lost and deforestation has taken place largely because of the dramatic in crease in built-up land and orchard. The conclusive statistical information is useful to understand the processes, causes and impacts of the land use changes in the county. The major driving force to the land use changes in the county ap peared to be the rapid economic development. The decision makers of the county have to pay more attention to the land use changes for the county’s sustainable development.

WISE EXPLOITATION OF NEWLY GROWING LAND RESOURCES——An Assessment on Land-use Change of Chongming Island Using GIS

Chongming Island, the third largest island in China and the largest alluvialisland in the world, is situated in the north of Shanghai Municipality at the mouth of theChangjiang (Yangtze) River. Along the fertile and prosperous sea coast there are a total area ofover 120 x 10~3ha, with a population of 735 000, accruing some 500ha of new tidal land resourcescome from silt, sand and mud carried by the Changjiang River every year, extending about 140m peryear. This dynamic process of alluvial growth has run for some 1500 years. Mudflat on ChongmingIsland at the mouth of the Changjiang River is a resting ground for migratory birds and host morethan a hundred species, including rare cranes and geese. But the local people keep reclaiming thetidal land for economic development. Obviously, it is crucial to have a well-concerted plan forfuture exploitation. In this study, we attempted to investigate the status changes of land use andwild life habitats on Chongming Island in recent 10 years, and then analyzed different humanactivities and their effects on wild life habitats using satellite image data (1990, 1997 and 2000)as well as field survey. Based on the analysis, this study explored the relationships between islandgrowth and land use/cover change (LUCC), predicted what the habitat would be like in the future andtried to find more effective use of this new growing resource. At last, this study provided somepreliminary management plans for Chongming Island that will coordinate the development of localeconomies and the conservation of wild life and their habitats.

Dry reusing and wet reclaiming of used sodium silicate sand

Based on the characteristics of used sodium silicate sand and the different use requirements for recycled sand, "dry reusing and wet reclaiming of used sodium silicate sand" is considered as the most suitable technique for the used sand. When the recycled sand is used as support sand, the used sand is only reused by dry process including breaking, screening, dust-removal, etc., and it is not necessary that the used sand is reclaimed with strongly rubbing and scraping method, but when the recycled sand is used as facing sand (or single sand), the used sand must be reclaimed by wet method for higher removal rate of the residual binders. The characteristics and the properties of the dry reused sand are compared with the wet reclaimed sand after combining the different use requirements of support sand and facing sand (or single sand), and above the most adaptive scheme has also been validated.

Re-assessing Vegetation Carbon Storage and Emissions from Land Use Change in China Using Surface Area

Land surface area estimation can provide basic information for accurately estimating vegetation carbon storage under complex terrain. This study selected China, a country dominated by mountains, as an example, and calculated terrestrial vegetation carbon storage(VCS) for 2000 and 2015 using land surface area and traditional ellipsoid area. The land surface area is estimated by a triangular network on the high precision digital elevation model.The results showed that: 1) The VCS estimated by the surface area measurement in 2000 and 2015 were 0.676 and0.692 Pg C(1 Pg = 1015 g) higher than the VCS calculated using the ellipsoid area, respectively. 2) As the elevation increases, the differences between VCS estimated by surface area measurement and ellipsoid area measurement are expanding. Specially, a clear gap was present starting from an elevation of 500 m, with the relative error exceeds8.99%. 3) The total amount of carbon emitted due to land use change reached 0.114 Pg C. The conversions of forestland and grassland to other land use type are the main reasons of the loss of vegetation carbon storage, resulting in a total amount of biomass carbon storage decreased by 0.942 and 0.111 Pg C, respectively. This study was a preliminary exploration of incorporating land surface area as a factor in resource estimation, which can help more accurately understand the status of resources and the environment in the region.

Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS

Remote sensing is one of the tool which is very important for the production of Land use and land cover maps through a process called image classification. For the image classification process to be successfully, several factors should be considered including availability of quality Landsat imagery and secondary data, a precise classification process and user’s experiences and expertise of the procedures. The objective of this research was to classify and map land-use/land-cover of the study area using remote sensing and Geospatial Information System (GIS) techniques. This research includes two sections (1) Landuse/Landcover (LULC) classification and (2) accuracy assessment. In this study supervised classification was performed using Non Parametric Rule. The major LULC classified were agriculture (65.0%), water body (4.0%), and built up areas (18.3%), mixed forest (5.2%), shrubs (7.0%), and Barren/bare land (0.5%). The study had an overall classification accuracy of 81.7% and kappa coefficient (K) of 0.722. The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research. This study present essential source of information whereby planners and decision makers can use to sustainably plan the environment.

Urban Land Use Change Detection Using Multisensor Satellite Images

Due to inappropriate planning and management, accelerated urban growth and tremendous loss in land, especially cropland, have become a great challenge for sustainable urban development in China, especially in developed urban area in the coastal regions; therefore, there is an urgent need to effectively detect and monitor the land use changes and provide accurate and timely information for planning and management. In this study a method combining principal component analysis (PCA) of multisensor satellite images from SPOT (systeme pour l'observation de la terre or earth observation satellite)-5 multispectral (XS) and Landsat-7 enhanced thematic mapper (ETM) panchromatic (PAN) data, and supervised classification was used to detect and analyze the dynamics of land use changes in the city proper of Hangzhou. The overall accuracy of the land use change detection was 90.67% and Kappa index was 0.89. The results indicated that there was a considerable land use change (10.03% of the total area) in the study area from 2001 to 2003, with three major types of land use conversions: from cropland into built-up land, construction site, and water area (fish pond). Changes from orchard land into built-up land were also detected. The method described in this study is feasible and useful for detecting rapid land use change in the urban area.

Selection of optimal land uses for the reclamation of surface mines by using evolutionary algorithms

A methodology for the selection of the optimal land uses of the reclamation of mined areas is proposed. It takes into consideration several multi-nature criteria and constraints, including spatial constrains related to the permissible land uses in certain parts of the mined area. The methodology combines desirability functions and evolution searching algorithms for selection of the optimal reclamation scheme. Its application for the reclamation planning of the Amynteon lignite surface mine in Greece indicated that it handles effectively spatial and non-spatial constraints and incorporates easily the decision-makers preferences regarding the reclamation strategy in the optimization procedure.

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.

Effects of water application uniformity using a center pivot on winter wheat yield, water and nitrogen use efficiency in the North China Plain

In recent years, the use of fertigation technology with center pivot irrigation systems has increased rapidly in the North China Plain (NCP). The combined effects of water and nitrogen application uniformity on the grain yield, water use efficiency (WUE) and nitrogen use efficiency (NUE) have become a research hotspot. In this study, a two-year field experiment was conducted during the winter wheat growing season in 2016–2018 to evaluate the water application uniformity of a center pivot with two low pressure sprinklers (the R3000 sprinklers were installed in the first span, the corresponding treatment was RS;the D3000 sprinklers were installed in the second span, the corresponding treatment was DS) and a P85A impact sprinkler as the end gun (the corresponding treatment was EG), and to analyze its effects on grain yield, WUE and NUE. The results showed that the water application uniformity coefficients of R3000, D3000 and P85A along the radial direction of the pivot (CUH) were 87.5, 79.5 and 65%, respectively. While the uniformity coefficients along the traveling direction of the pivot (CUC) were all higher than 85%. The effects of water application uniformity of the R3000 and D3000 sprinklers on grain yield were not significant (P>0.05);however, the average grain yield of EG was significantly lower (P<0.05) than those of RS and DS, by 9.4 and 11.1% during two growing seasons, respectively. The coefficients of variation (CV) of the grain yield had a negative correlation with the uniformity coefficient. The CV of WUE was more strongly affected by the water application uniformity, compared with the WUE value, among the three treatments. The NUE of RS was higher than those of DS and EG by about 6.1 and 4.8%, respectively, but there were no significant differences in NUE among the three treatments during the two growing seasons. Although the CUH of the D3000 sprinklers was lower than that of the R3000, it had only limited effects on the grain yield, WUE and NUE. However, the cost of D3000 sprinklers is lower than that of R3000 sprinklers. Therefore, the D3000 sprinklers are recommended for winter wheat irrigation and fertigation in the NCP.

Using irrigation intervals to optimize water-use efficiency and maize yield in Xinjiang,northwest China

Worldwide, scarce water resources and substantial food demands require efficient water use and high yield.This study investigated whether irrigation frequency can be used to adjust soil moisture to increase grain yield and water use efficiency(WUE) of high-yield maize under conditions of mulching and drip irrigation.A field experiment was conducted using three irrigation intervals in 2016: 6, 9, and 12 days(labeled D6, D9, and D12) and five irrigation intervals in 2017: 3, 6, 9, 12, and 15 days(D3, D6, D9, D12, and D15).In Xinjiang, an optimal irrigation quota is 540 mm for high-yield maize.The D3, D6, D9, D12, and D15 irrigation intervals gave grain yields of 19.7, 19.1–21.0, 18.8–20.0, 18.2–19.2, and 17.2 Mg ha^-1 and a WUE of 2.48, 2.53–2.80, 2.47–2.63, 2.34–2.45, and 2.08 kg m-3, respectively.Treatment D6 led to the highest soil water storage, but evapotranspiration and soil-water evaporation were lower than other treatments.These results show that irrigation interval D6 can help maintain a favorable soil-moisture environment in the upper-60-cm soil layer, reduce soilwater evaporation and evapotranspiration, and produce the highest yield and WUE.In this arid region and in other regions with similar soil and climate conditions, a similar irrigation interval would thus be beneficial for adjusting soil moisture to increase maize yield and WUE under conditions of mulching and drip irrigation.

Impacts of land use/cover change on water balance by using the SWAT model in a typical loess hilly watershed of China

Land use/cover change(LUCC)plays a key role in altering surface hydrology and water balance,finally affect-ing the security and availability of water resources.However,mechanisms underlying LUCC determination of water-balance processes at the basin scale remain unclear.In this study,the Soil and Water Assessment Tool(SWAT)model and partial least squares regression were used to detect the effects of LUCC on hydrology and water components in the Zuli River Basin(ZRB),a typical watershed of the Yellow River Basin.In general,three recommended coefficients(R^(2)and E ns greater than 0.5,and P bias less than 20%)indicated that the output results of the SWAT model were reliable and that the model was effective for the ZRB.Then,several key findings were obtained.First,LUCC in the ZRB was characterized by a significant increase in forest(21.61%)and settlement(23.52%)and a slight reduction in cropland(-1.35%),resulting in a 4.93%increase in evapotranspiration and a clear decline in surface runoffand water yield by 15.68%and 2.95%at the whole basin scale,respectively.Second,at the sub-basin scale,surface runoffand water yield increased by 14.26%-36.15%and 5.13%-15.55%,respectively,mainly due to settlement increases.Last,partial least squares regression indicated that urbanization was the most significant contributor to runoffchange,and evapotranspiration change was mainly driven by forest expansion.These conclusions are significant for understanding the relationship between LUCC and water balance,which can provide meaningful information for managing water resources and the long-term sustainability of such watersheds.

Land-Use/Land-Cover Dynamics in Calabar Metropolis Using a Combined Approach of Remote Sensing and GIS

This paper assessed the dynamics in the land use/land cover (LULC) within patterns of the land use/land cover (LULC) in Calabar metropolis. The thermal imageries for 2002, 2006, 2008, 2010, 2012, 2014 and 2016 were obtained and processed using remote sensing and Arc GIS software package in order to determine the changes that have occurred in the LULC in study area. The result of the LULC thematic maps overall accuracies was computed above 80 percent, which indicates an almost perfect agreement. The findings of this study reveal that, LULC classes by the year 2016 have assumed different dimensions of change from the sizes of their previous sizes in comparison to their current sizes. Land-use pattern changes in the study area were characterized by an increase in the built up class, waterbody (though with a slightly negative change from 2010 to 2012) and a predominant negative trend in dense vegetation and bare land classes;thus, indicating that the future changing trends will pose a depleting threat to the overall LULC. This study has shown that, the changing land use pattern of the area is capable affecting certain characteristics of the environment such as surface temperature. The study recommends that effort should be made by the government to increase urban vegetation around city centers and outliers by embarking on reforestation.

Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation： A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level（surface-sensitive）channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets.Here, we used an improved land use and leaf area index（LAI） dataset in the WRF-3 DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels（e.g., channel 3）,the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.

Genetic dissection of N use efficiency using maize inbred lines and testcrosses

Although the use of heterosis in maize breeding has increased crop productivity,the genetic causes underlying heterosis for nitrogen(N) use efficiency(NUE) have been insufficiently investigated.In this study,five N-response traits and five low-N-tolerance traits were investigated using two inbred line populations(ILs) consisting of recombinant inbred lines(RIL) and advanced backcross(ABL) populations,derived from crossing Ye478 with Wu312.Both populations were crossed with P178 to construct two testcross populations.IL populations,their testcross populations,and the midparent heterosis(MPH)for NUE were investigated.Kernel weight,kernel number,and kernel number per row were sensitive to N level and ILs showed higher N response than did the testcross populations.Based on a highdensity linkage map,138 quantitative trait loci(QTL) were mapped,each explaining 5.6%–38.8% of genetic variation.There were 52,34 and 52 QTL for IL populations,MPH,and testcross populations,respectively.The finding that 7.6% of QTL were common to the ILs and their testcross populations and that 11.7% were common to the MPH and testcross population indicated that heterosis for NUE traits was regulated by non-additive and non-dominant loci.A QTL on chromosome 5 explained 27% of genetic variation in all of the traits and Gln1-3 was identified as a candidate gene for this QTL.Genome-wide prediction of NUE traits in the testcross populations showed 14%–51% accuracy.Our results may be useful for clarifying the genetic basis of heterosis for NUE traits and the candidate gene may be used for genetic improvement of maize NUE.