Land use change characteristics affected by water saving practices in Manas River Basin,China using Landsat satellite images

The characteristics and influencing factors of land use change under arid conditions were studied in the Manas River Basin in Xinjiang Region,Northwest China.Landsat satellite images acquired in 1976,1990,2000,2010 and 2015 over the study area were used as basic data.Land use change,the rate of change of land use,land use transfer and other aspects revealed the characteristics of land use change and related factors as influenced by water conditions in the basin.The results showed that:(1)Over nearly 50 years,land reclamation in the Manas River Basin resulted in the rapid expansion of an artificial oasis area,and promoted the process of‘oasis urbanization’,and accelerated the development of the river basin economy.(2)In 2000,the popularization of drip irrigation under mulch technology led to the rapid growth of cultivated land and development land in the watershed.Meanwhile,the water table declined in the desert area of the lower reaches of the river basin,and the area occupied by sparse shrub forest and grassland decreased.(3)Before popularization of water-saving technology,woodland,grassland and development land transformed to cultivated land in the amounts of 93.46 km^(2),2542.93 km^(2) and 137.53 km^(2),respectively,and woodland transformed in the amount of 189.64 km^(2).After water-saving technology was popularized,woodland,grassland and development land were transformed into cultivated land in the amounts of 567.41 km^(2),1756.2 km^(2) and 37.36 km^(2),respectively.(4)The popularization of water-saving technology made the dynamic degree of cultivated land and development land more active,and further increased landscape fragmentation and landscape heterogeneity.The level of urbanization development,the level of economic development and the dry humidity of the basin became the main factors affecting the change of land use in the basin.

Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana

This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software were used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands i.e. Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985oC to 46.1314oC, and, 18.3437oC to 30.9693oC respectively. Results of Kumasi also show a higher range of temperatures from 32.6986oC to 19.1077oC during the dry season. In the wet season, temperatures ranged from 26.4142oC to -0.898728oC. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.

Evaluation of Actual Evapotranspiration and Crop Coefficient in Carrot by Remote Sensing Methodology Using Drainage and River Water to Overcome Reduced Water Availability

Searching for alternative methods for traditional irrigation is World trend at days due to a reduction in water and increased of drought due to climate changes therefore farmers need use modern methods of scheduling water and minimizing water losses while also increasing yield. To meet the future increasing demands water and food there is a need to utilize alternative methods to reduce evaporation, transpiration and deep percolation of water. Any countries use recycled water (drain and sewage) and desalination water from the sea or drains to irrigate crops plus computing actual crop evapotranspiration (ETc) so as to calculate the amount of water to apply to a crop. The paper aims to assess the actual evaporation and evaporation coefficient of carrots, by planting carrots in a field and the crop was exposed to several sources of water (DW and RW) and comparing ETc, Kc and production among plots of three sites (A, B and C). The study used two types of irrigation water (drain water (DW) and river water (RW)). The results were to monthly rate and accumulated actual evapotranspiration to C (irrigation by RW only) more than A (67% RW and 33% DW) and B (17% RW and 83% DW) via 7% and 58%, respectively. The yield to C more than A and B by 17% and 75%, respectively. In conclusion the use of DW can cause a reduction in crop consumptive of carrot crops also causes a reduction in yield, crop length, root length, root size, canopy of crop, number of leaves and biomass of the plant therefore, the drainage water needs to treated before irrigating crops And making use of it to irrigate the fields and fill the shortfall in the amount of water from the river. The drain water helped on filling the water shortage due to climate changes and giving production of carrot crop but less than river water.

A Cloud Detection Method for Landsat 8 Satellite Remote Sensing Images Based on Improved CDNet Model

Cloud detection in remote sensing images is a crucial task in various applications,such as meteorological disaster prediction and earth resource exploration,which require accurate cloud identi¯cation.This work proposes a cloud detection model based on the Cloud Detection neural Network(CDNet),incorporating a fusion mechanism of channel and spatial attention.Depthwise separable convolution is adopted to achieve a lightweight network model and enhance the e±ciency of network training and detection.In addition,the Convolutional Block Attention Module(CBAM)is integrated into the network to train the cloud detection model with attention features in channel and spatial dimensions.Experiments were conducted on Landsat 8 imagery to validate the proposed improved CDNet.Averaged over all testing images,the overall accuracy(OA),mean Pixel Accuracy(mPA),Kappa coe±cient and Mean Intersection over Union(MIoU)of improved CDNet were 96.38%,81.18%,96.05%,and 84.69%,respectively.Those results were better than the original CDNet and DeeplabV3+.Experiment results show that the improved CDNet is e®ective and robust for cloud detection in remote sensing images.

Glacial Cover Assessment from 1993 to 2021 Using Normalized Difference Snow Index with Landsat Imagery in the Colorado Front Range, USA

Cirque glaciers found on the Colorado Front Range, USA are sensitive to climate change and are important for water supply and the delivery of water downstream. These glaciers are shrinking at a rapid rate in response to the global climate change, and thus it is important to track and analyze them. One of the ways to monitor these glaciers is by using multispectral satellite imagery (Landsat imagery). In this article, the feasibility of tracking glacial area via Landsat satellites has been discussed and the trend of 13 glaciers in the Colorado Front Range Region has been analyzed from 1993 to 2021. In this period, the total glacial area across 13 glaciers in the Colorado Front Range Region has decreased by 63.6%, and a significant correlation (p-value p < 0.05) found with the annual global average temperature indicates that the retraction of glaciers is likely a response to the global warming.