Agricultural Fallow Land Research Articles

EXACERBATING LAND USE PATTERNS IN URBAN AREAS: A SPATIO - TEMPORAL STUDY OF URBANIZATION IN PATAN CITY, GUJARAT

This study examines the urbanisation and alterations in land use in Patan, a city in Gujarat. Rural areas are transformed into urban areas through the process of urbanisation. The study investigates the effects of this procedure on Patan's land use. Satellite images from 2012 and 2022 are used in the study to map and track changes in the study area. The study examines the many land classifications in the region, including water bodies, built-up areas, vegetation, agriculture, agricultural fallow land, riverbeds, and barren land. It does so using tools like QGIS and ArcGIS. The study's conclusions show that Patan's land use has changed significantly during the previous ten years. While agriculture and agricultural fallow have reduced, the built-up area has expanded. These changes are attributable to the urbanisation and expanding population of the region. This study underlines the requirement for efficient land use laws and rules that support Patan's sustainable urban development. Urbanization can have detrimental effects on the ecology, the loss of agricultural land, and other factors if it is not planned for properly. The study emphasises the significance of efficiently managing urban areas through the use of GIS and remote sensing technology. These technologies can offer insightful information about how land use is changing and how urbanisation affects the environment. This study sheds light on Patan's urbanisation and shifting land use patterns and emphasises the necessity of sustainable urban planning and management.

Spatiotemporal Change Detection Analysis of Land Cover and Land Use using Multi-Temporal Remote Sensed Imagery and Landscape Metrics: A Sub-Catchment Level Case Study of the Bhagirathi-Hugli River, West Bengal, India

To analyse and characterise land use-land cover (LULC) change, remote sensing (RS) data and geographic information system (GIS) approaches are required. The landscape metrics are also extensively used to explain the structure and pattern of the spatial landscape. This case study has been conducted in the area of the Bhagirathi-Hugli River banks in West Bengal. The study area’s satellite image data from various periods (1989-2017) were used to analyse the spatiotemporal variation of LULC change. The LULC fragmentation was analysed using a total of six landscape measures. Landscape metrics such as Number of Patches (NP), Largest Patch Index (LPI), Landscape Shape Index (LSI), Mean Shape Index (MSI), Contagion (CONTAG), and Shannon’s Diversity Index were used to determine the changes over four different periods. The main features of land use change were the continuous expansion of built-up area (9.30 km2), vegetation (189.14 km2), and the decrease in agricultural fallow land and water bodies from 1989 to 2017. The landscape pattern in the study area has also changed significantly. The landscape metrics indicate that the studied region had a highly fragmented landscape during the study period. The proposed technique is easy to use and can address more than one metric while selecting the appropriate setting can prove problematic.

Remote Sensing and GIS-aided Land and Water Resources Management Plan for a Prioritized Sub-watershed of the Kotni Watershed, Chhattisgarh, India

Land and water are the prime resources that support all forms of life through various processes. Integration of Remote Sensing and GIS is incredibly helpful in planning and management of this resources, it is superior to the limitation of typical land planning, causes it embraces the metaverse of planning and development of resources through modern technology. In the study area, these resources are in critical condition; approximately 6.5 % of land is agricultural fallow land, 3 % is barren land, and 50 % is current fallow in Rabi season caused by the majority of agricultural land being unproductive due to poor land and water resource management. This research makes prudent and effective use of the watershed resources to increase productivity and production in the study area. The runoff computation of the watershed was done using the SCS curve number approach for 11 years (2009-2019), it reveals average annual rainfall of 1329 mm, with 41% of runoff generation capacity, which is estimated as 483.95 Mm3 of generated runoff. Methodology also includes Multi-Criteria Decision Analysis (MCDA) techniques and weighted overlay analysis (WOA) which effectively uses various thematic maps to delineate suitable zones for water storage and harvesting structures. Finally, suitable sites for 7 check dams, 10 percolation tanks for groundwater recharging and 20 check dams, 61 farm ponds for water harvesting were identified and proposed in the study area. The research findings will help to conserve water and soil resources and may increase the agricultural area as well as the production and productivity of the respected field.

Watershed Prioritization based on LULC Characteristics using GIS and TOPSIS: A case of Chathe watershed

Watershed prioritization based on land use and land cover demonstrated the role of different features on possibilities of soil erosion. Sub-watershed prioritization is more crucial to restrict the soil erosion as well as loss of nutrients from the top soil. The study assigned the weight to various LULC features based on their role in soil erosion. Finally with the help of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), LULC-based prioritization index has been constructed and prioritized the seven sub-watersheds.Highest acreage of agricultural fallow land and barren scrubland with less amount of forest coverage in WS5 turns into more susceptible category compared to others, whereas lower acreage of barren scrubland, agricultural fallow land and cropland with higher coverage of forest in WS6 turns into lower susceptible category.

Watershed Prioritization based on LULC Characteristics using GIS and TOPSIS: A case of Chathe watershed

Watershed prioritization based on land use and land cover demonstrated the role of different features on possibilities of soil erosion. Sub-watershed prioritization is more crucial to restrict the soil erosion as well as loss of nutrients from the top soil. The study assigned the weight to various LULC features based on their role in soil erosion. Finally with the help of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), LULC-based prioritization index has been constructed and prioritized the seven sub-watersheds.Highest acreage of agricultural fallow land and barren scrubland with less amount of forest coverage in WS5 turns into more susceptible category compared to others, whereas lower acreage of barren scrubland, agricultural fallow land and cropland with higher coverage of forest in WS6 turns into lower susceptible category.

Land use/land cover change detection using remote sensing and gis based techniques for solani river basin, uttarakhand

Land Use/Land Cover (LU/LC) change detection was performed in the Solani river watershed area using multi-temporal remote sensing images (Landsat 8 ETM+ image of year 2014 and Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) image of year 2021). Image classification and change detection were carried out for both images using Arc Geographic Information System (GIS) 10.1 and Earth Resources Data Analysis System (ERDAS) Imagine 2016 software. High-resolution Google Earth imagery and Land Remote-Sensing Satellite (LANDSAT) images were used for the accuracy assessment of the classification. The results showed major increments of agricultural fallow land and build-up land of 25.19% and 20.69%, respectively, with the highest decrease in forest cover of 29.27%. Also, to analyze the impact of varying spatial resolution on the Topographic Wetness Index (TWI), two digital elevation models (DEMs) of different spatial resolutions (SRTM, 90m, and Cartosat, 30 m) were used. The results of the study indicated that the mean TWI value increases with an increase in grid size.

“Human elephant conflict in changing land-use land-cover scenario in and adjoining region of Buxa tiger reserve, India”

The land use land cover changes (LULCC) in and around wildlife habitat are one of the major driving forces behind frequent human elephant conflict (HEC). The present study aims to determine the spatiotemporal LULC changes and its impact on Human – Elephant conflict in and adjoining Buxa Tiger Reserve (BTR). Landsat TM and OLI images of 1990, 2000, 2010, and 2019 were used and supervised classification using maximum likelihood classification algorithm applied to classify the LULC attributes. Each sign of elephant presence was recorded using GPS used for corridor mapping. The recorded sign of conflict was collected, used for settlement based human elephant conflict probability zone identification mapping by areal interpolation method. We found that (1990 - 2019) dense forest, surface water bodies, scrub land and agricultural fallow land decreased by 184.04 sq km, 21.94 sq km, 142.48 sq km, and 130.21 sq km respectively. On the other hand crop land, sparse vegetation, tea garden, open forest, degraded forest and built up areas increased by 119.74 sq km, 90.96 sq km, 87.05 sq km, 57.02 sq km, 19.07 sq km and 32.21 sq km respectively. The visual overlay method was performed between LULC map, and settlement based HEC probability map, therefore impact of spatiotemporal LULC change on HEC was determined. It finds out moderate to very high HE conflict settlement zone lies where nearby forest tract either degraded or large areas of open forest, scrub land replaced into crop fields and settlements. In addition, elephant sign along the broad gauge rail line were collected on foot, recorded using GPS used to prepared accident prone susceptibility map by Inverse Distance Weighting (IDW) interpolation technique to estimate where accidents were more likely to occur due to the concentration of elephant activity. It finds out high to very high accident prone susceptible zone lies in between Rajabhatkhawa station (st) – Damanpur st, Gorapara st- Rajabhatkhawa st, and railway line within Hamiltonganj range. The study wants to draw attention to the concerned authority and policy makers on that there is a need of long term land use planning to save BTR forest and its biodiversity as well as need for proper mitigate method for HE conflict for peaceful coexistences of both the species.

Spatial modeling of land use and land cover change in Sulaimani, Iraq, using multitemporal satellite data.

Land use/land cover (LULC) change is an important indicator used for assessing the function and health of ecosystems. Understanding the patterns of LULC change assists in managing natural resources effectively, especially for regions where there are minimal or no reported data on the status of LULC. In this study, remotely sensed Landsat satellite imagery from 5years (i.e., 1988, 1996, 2002, 2008, and 2017), geographic information systems (GIS), and the hybrid cellular automata (CA)-Markov model were used to (i) quantify the past and present LULC changes and (ii) model the future changes in Sulaimani Province in the Kurdistan region of Iraq (KRI). To accomplish these objectives, five LULC maps with various class categories were generated using the maximum likelihood classifier (MCL). The classified maps for 1996, 2002, 2008, and 2017 were used in the hybrid model to simulate LULC maps for 2017 and 2037. The map simulated for 2017 was validated with the classified 2017 LULC map. The change analysis demonstrated that between 1988 and 2017, the built-up areas and agricultural fallow land increased by 419% and 226%, respectively. In the future predictions for 2037, built-up areas and agricultural fallow land showed increasing trends of 5.5% and 26.5%, respectively. In contrast, agricultural land, plantation land, and sparse vegetation areas were predicted to decrease by 29.4%, 65.8%, and 36.9%, respectively. In addition, in 2008, waterbodies shrank by 43.36% in comparison with their status in 1988, suggesting that 2008 was a severe drought year. These findings provide invaluable baseline information with which conservation biologists, agricultural engineers, urban planners, and decision makers can better manage natural resources and monitor environmental changes. Based on these results, sustainable development actions and an early warning system can be established.

CA MARKOV MODELING OF LAND USE LAND COVER DYNAMICS AND SENSITIVITY ANALYSIS TO IDENTIFY SENSITIVE PARAMETER(S)

Abstract. An attempt has been made to explore, evaluate and identify the sensitive parameter(s) of Cellular Automata Markov chain modeling to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India. For this purpose, land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. Sensitivity analysis has been carried out to identify the land use land cover parameter(s), which have the highest, lowest or intermediate influence on predicted results. The validity of the Cellular Automata Markov process for projecting future land use and cover changes in the study area calculates various Kappa Indices of Agreement (Kstandard) which indicate how well the comparison map agrees and disagrees with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The results shows that the land with or without scrub appeared to be most sensitive parameter as it has highest influences on predicted results of land use land cover of 2007. The second most sensitive parameter was lakes / reservoirs / ponds to predict land use land cover of 2007, followed by river, agricultural crop land, plantation, open land, marshy / swampy, sandy area, aquatic vegetation, built up land, dense forest, degraded forest, waterlogged area and agricultural fallow land. The least sensitive parameter is agricultural fallow land, which has minimum influence on predicted results of land use land cover of 2007. The validation of CA Markov land use land cover prediction results shows Kstandard is 0.7928.

Urban Change Monitoring with the Help of Remote Sensing and GIS of Ranchi City, Jharkhand

Fast urbanisation is a pattern seen over the developing world. Quite a bit of this is because of provincial to urban movement of individuals looking for employments, education and the higher expectation for everyday life. This is the essential cause for urban sprawl in Ranchi city and its encompassing zone. Over the most recent 25 years, real change is found in land use, an increment in population, natural forest cover, ecological degradation and impact on the atmosphere and so on. With the number of inhabitants in Ranchi city anticipated that would become much further, there is a need to evaluate the degree of urban sprawl and future manageable urban planning. The assessment in this study included land use and land cover change analysis, land surface temperature change analysis and vegetation change analysis (from normalized difference vegetation index [NDVI]) between two different years. The satellite image of two unique years (1992 and 2017) was classified into seven classes specifically water bodies, built-up land, agriculture, vegetation, agricultural fallow land, barren land and scrub land. The classified image of two distinct years was utilized to discover the change in urban sprawl in the last 25 years. The final outcome found that urban sprawl expanded 78.07 km2 about 26.25% from 1992 to 2017. In addition, the temperature of the city has been expanded to 5°C found in settlement area in between 25 years from the highest temperature being 35°C of 1992 to 40°C of 2017. The NDVI guide of two distinct years (1992 and 2017) shows that the vegetation cover is decreased 35.06 km2 around 11.58% because of urbanisation.

Soil organic carbon stock explored by geo-spatial modeling at Sagar Island, India

Soil organic carbon (SOC) and organic carbon density (OCD) is an active indicator of soil fertility and productivity, and it fluctuates spatially and temporally. The main objective of the present study is to evaluate the spatial variability of SOC and OCD using geostatistics model as well as field methods at Sagar Island (Sundarban), India. Soil samples at the depths of 0–20 cm were collected from twenty plots at different season to estimate organic carbon concentrations and carbon density in the laboratory. Spatial distributions of SOC and OCD concentrations were estimated using ordinary kriging method. Remote sensing imagery and ground truth helped in identifying different features as well as land use/land cover correctly. Landuse are characterized by coastal water, sand, coastal wetland, salt marsh, agricultural fallow land, sparse vegetation, crop/paddy field, and mixed settlement area. The average value SOC were recorded as 2.80, 1.83, and 2.069 % in post-monsoon, pre-monsoon and monsoon season respectively. Semivariograms models of SOC and OCD in all seasons illustrated strong positive nugget values indicated short scale variability and sampling error. The coefficient of determination (R2) of the SOC model denoted as 0.71, 0.68 and 0.74 and for OCD calculated as 0.78, 0.67 and 0.83 at pre-monsoon, monsoon and post-monsoon season respectively. The results illustrated that mangrove forest portrayed highest amount of SOC concentration in both the pre-monsoon (1.05 kg/m2) and post-monsoon (0.94 kg/m2) season.

A mesoscale modeling study of wind blown dust on the Mexico City Basin

The latest phase of the program to improve the air quality in the Valley of Mexico, also known, as Pro Aire is about to go into effect for the next 10 years. Pro Aire puts emphasis on agricultural wind erosion and associated dust emissions impacting downwind air quality. The main objective of this investigation was to use an empirical USEPA erosion model coupled to a meteorological/transport-dispersion prediction model, CALMET/CALPUFF, to estimate dust emissions and concentrations in the Mexico City Basin. The model simulations for particulate matter (PM 10) are validated against observations taken at the most recent research field study, the IMADA–AVER field campaign, conducted during the spring of 1997 to provide information about high ozone, particulate matter concentrations and visibility impairment. The spatial and temporal PM distribution in the region is presented for a specific wind blown dust event consisting of two IMADA days, in order to understand how soil dust emissions from agricultural fallow land affect downwind areas during the dry season. Results show good agreement with the main spatial features of the local wind circulation and wind blown dust concentrations. A correlation coefficient of nearly 0.8 between predictions and observations for a modeled day suggests that an important portion of the total measured concentration had geological origin. This work constitutes an essential advancement on the mesoscale air quality problem on the MCMA due to wind erosion.