The impact of climate and land-use changes on the hydrological processes of Owabi catchment from SWAT analysis

Monitoring and understanding the changes in mangrove ecosystems and their surroundings are required to determine how mangrove ecosystems are constantly changing while influenced by anthropogenic, and natural drivers. Cosistency in high spatial resolution (30 m) satellite and high performance computing facilities are limiting factors to the process, with storage and analysis requirements. With this, we present the Google Earth Engine (GEE) based approach for long term mapping of mangrove forests and their surroundings. In this study, we used a GEE based approach: 1) to create atmospheric contamination free data from 1987-2017 from different Landsat satellite imagery; and 2) evaluating the random forest classifier and post classification change detection method. The obtained overall accuracy for the years 1987 and 2017 was determined to be 0.87 and 0.96, followed by a Kappa coefficient 0.80 and 0.94. The change detection results revealed a significant decrease in the agricultural area, while there was an increase in mangrove forest, shrimp/fish farm, and bareland area. The results suggest that interconversion of land use and land cover is affecting the landscape dynamics within the study area.

/pdf/google-earth-engine-based-three-decadal-landsat-imagery-45r871tlru.pdf

Google Earth Engine Based Three Decadal Landsat Imagery Analysis for Mapping of Mangrove Forests and Its Surroundings in the Trat Province of Thailand

Abstract The availability of remote sensing satellite data at various spatial, temporal and spectral resolutions provides enormous opportunity to map the urban sprawl. When coupled with Geographic Information System (GIS) it is possible to evaluate, analyse and integrate large data. We need to understand and quantify the urban sprawl on spatial and temporal scales which forms a basis for better planning and sustainable management of cities and towns. The city of Ranchi has witnessed unprecedented urban growth after assuming the status of a capital of Jharkhand state, India in 2000. The increasing population has put pressure on the natural resources of the city. The urban growth has been in a haphazard manner at the cost of agricultural lands, forest land and open green spaces such as park, garden and recreational forestry. The present study analysed the urban sprawl in Ranchi city, using Landsat data from 1976, 2002 and 2015. The study revealed that the annual urban growth rate was 1.76 ha/yr over the period from 1976 to 2002 whereas the annual growth rate was 2 ha/yr over the period from 2002 to 2015. The northern side of the city has witnessed more expansion in 2002 when compared with the growth in 1976. Increase in urban density was seen at the distances of 3, 4,5,6,7 and 8km between 1976 and 2015 and the rate was higher than 25%.The driving factors of the development were infrastructure, educational and business expansion. Thus, spatial analyses of urban sprawl are a prerequisite for curbing the unplanned urban growth and ensure sustainable living.

https://sciendo.com/pdf/10.1515/jengeo-2016-0002

Analysis of Urban Sprawl Dynamics Using Geospatial Technology in Ranchi City, Jharkhand, India

The present paper is an attempt to integrate a semi-automated object-based image analysis (OBIA) classification framework and a cellular automata-Markov model to study land use/land cover (LULC) changes. Land use maps for the Sarab plain in Iran for the years 2000, 2006, and 2014 were created from Landsat satellite data, by applying an OBIA classification using the normalized difference vegetation index, salinity index, moisture stress index, soil-adjusted vegetation index, and elevation and slope indicators. The classifications yielded overall accuracies of 91, 93, and 94% for 2000, 2006, and 2014, respectively. Finally, using the transition matrix, the spatial distribution of land use was simulated for 2020. The results of the study revealed that the number of orchards with irrigated agriculture and dry-farm agriculture in the Sarab plain is increasing, while the amount of bare land is decreasing. The results of this research are of great importance for regional authorities and decision makers in strategic land use planning.

An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain

Geo-informatics technology has dynamic role in mapping, monitoring and management of forest resources. The transformation of forest cover and its analysis on the earth’s surface are essential for understanding the associations and interactions between natural phenomena and living organism, especially human. Also deforestation is a major reason for global warming and one of the origin keys for the enhancement of greenhouse effect and climate change. The present study is grounded on the 3S technology in assessment of LU/LC changes within the forest cover area in Dalma Wildlife Sanctuary (DWLS) Jamshedpur-Jharkhand, India. The movement of forest-cover variation over the years 2009, 2011, 2013, 2015 and 2016 is precisely studied using high resolution Satellite Data (SD). It is noted that, due to shifting cultivation, forest fire, and conversion of forest cover into crop land/bare land and settlement encroachments in forest region by villagers are rapidly increasing; as a result deforestation is taking place. It is predicted that the study would demonstrate the effectiveness of 3S technology in forest renovation, planning and management.

LU/LC Change Detection and Forest Degradation Analysis in Dalma Wildlife Sanctuary Using 3S Technology: A Case Study in Jamshedpur-India

While the concepts of change detection analysis is not new, the emergence of new imaging sensors and geospatial technologies has created a need for image processing techniques that can integrate observation from a variety of different sensors and datasets to map, detect and monitor forest resources. In addition to timber, forests provide such resources as grazing land for animals, wildlife habitat, water resources and recreation areas and these are threatened constantly by both human impacts like forest fires, air pollution, clearing for agricultural uses, and illegal cutting. Farming activities, continued sand winning operations and the allocation of plots of land to prospective developers in and around the catchments of the Owabi dam pose a serious threat to the forest covers and the lifespan of the dam. The overall objective of this study is to map out and analyze the structural changes of forest cover using Landsat and ASTER imageries of the study area. A supervised classification was performed on three multi-temporal satellite imageries and a total of eight major land use and land cover (LULC) classes were identified and mapped. By using post-classification techniques, from 1986 to 2002 and 2002 to 2007 the forest cover has decreased by an amount of 2136.6 ha and 1231.56 ha respectively representing 24.7% and 14.2%. Generally, the results indicate that from 1986 to 2007, forest cover reduced by 3368.16 ha, representing 38.9%. Decrease in vegetation has been as a result of anthropogenic activities in the study area. An NDVI analysis was performed on these images and it was noted that there was no significant difference between the NDVI classification and the supervised classification of the images. Overlay of the reserved forest of the 1974 and the classified map of 2007 shows vegetation changed during 1986-2007 remarkably.

Analysis of Forest Cover Change Detection

This study is aimed at establishing a comparative estimate of air temperature (Tair) from Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) between 1 km and 6 km spatial resolutions and to investigate and quantify spatial variability of LST—Tair relationships. The test area is Republic of Ghana located in the sub-region of West Africa. The purpose of the research is to establish an optimal performance of Tair between two spatial resolutions. Arithmetic Mean method was employed for computation of the datasets. Seventy (70) pairs of composite LSTs consisting of Terra and Aqua MODIS observations which correspond to 70 meteorological stations were used. Due to problem of missing data and outliers, 277 pairs of LST data points per resolution were used. Application of Regression Analyses revealed a good direct correlation existing between values of Tair and LSTs in 1 km with coefficient of determination, R2 = 0.8124 while, 6 Km resolution recorded R2 = 0.8031. Hence, MODIS LST in 1 km is a better estimator of air temperature in Linear Regression study. Statistical indices of 1 km procedure were found to be lower than the 6 km procedure. The 1 km resolution registered a Root Mean Square Error (RMSE) of 1.97°C, Standard Error of regression, 1.14°C and Coefficient of Variation, CV of 10.19%. Meanwhile, 6 km resolution yielded a RMSE of 2.13°C, Standard Error of regression, 1.22°C and CV of 10.68% respectively. Therefore, 1 km resolution datasets show better accuracy and performance of air temperature estimation than 6 km data products. Hence, a good result would be achieved when 1 km datasets are processed. 

https://www.tandfonline.com/doi/pdf/10.1080/23311916.2023.2216047?needAccess=true&role=button

Adubofour Frimpong

Papers

Analysis of Forest Cover Change Detection

A comparative estimate of air temperature from modis land surface temperatures in Ghana