scholarly journals Monitoring the Land Covers Around Al- Razaza Lake/ Iraq Based Upon Multi-Temporal Analysis Technique

2021 ◽  
Vol 32 (2) ◽  
pp. 18
Author(s):  
Sabah Noori Kadhum ◽  
Esraa Salam Alsudani
Keyword(s):  
Climate Changes ◽  
Temporal Analysis ◽  
High Increase ◽  
Lake Region ◽  
Analysis Technique ◽  
Multi Temporal ◽  
Different Seasons ◽  
Land Covers ◽  
Remote Sensing Techniques ◽  
Large Decline

Remote sensing techniques are very important for the identification of land cover patterns and monitoring changes that occurred, thus three different periods were taken for the Al-Razaza lake region.  The Summer and spring months were chosen because climate changes were observed and their effect on land covers was monitored.  According to the applied supervised classification, the study area was divided into four land covers, nearly closed Al – Razaza Lake was deeper in its western portions, thick vegetation cover was found. The eastern and southern portion of the lake was bounded by desertic and semi-desertic land. The water level in the lake was fluctuating with different seasons. During the period 2000 and 2015 the area of the lake was height decreased around 320 Km2.   Climate changes led to increasing drying out water areas and a high increase in the number of saline areas in 2015. This study shows a significant decrease in water cover In terms of depth and area of the lake due to the large decline in the value of the NDWI index.

scholarly journals TECHNIQUE FOR IDENTIFYING BURNED VEGETATION AREA USING LANDSAT 8 DATA

2017 ◽  
Vol 13 (2) ◽  
pp. 121
Author(s):  
Bambang Trisakti ◽  
Udhi Catur Nugroho ◽  
Ani Zubaidah
Keyword(s):  
Forest Fires ◽  
Large Scale ◽  
Temporal Analysis ◽  
Burned Area ◽  
Landsat 8 ◽  
Bare Area ◽  
Spectral Bands ◽  
Swir Band ◽  
Multi Temporal ◽  
Land Covers

During the last two decades, forest and land fire is a catastrophic event that happens almost every year in Indonesia.  Therefore, it is necessary to develop a technic to monitor forest fires using satellite data to obtain the latest information of burned area in a large scale area. The objective of this research is to develop a method for burned area mapping that happened between two Landsat 8 data recording on August 13rd and September 14th 2015. Burned area was defined as a burned area of vegetation. The hotspot distribution during the period August - September 2015 was used to help visual identification of burned area on the Landsat image and to verify the burned area resulted from this research. Samples were taken at several land covers to determine the spectral pattern differences among burned area, bare area and other land covers, and then the analysis was performed to determine the suitable spectral bands or indices and threshold values that will be used in the model. Landsat recorded on August 13rd before the fire was extracted for soil, while Landsat recorded on September 14th after the fire was extracted for burned area. Multi-temporal analysis was done to get the burned area occurring during the certain period. The results showed that the clouds could be separated using combination of ocean blue and cirrus bands, the burned area was extracted using a combination of NIR and SWIR band, while soil was extracted using ratio SWIR / NIR. Burned area obtained in this study had high correlation with the hotspot density of MODIS with the accuracy was around 82,4 %.

Multi-temporal analysis of the role of check dams in a debris-flow channel: Linking structural and functional connectivity

Geomorphology ◽  
2019 ◽  
Vol 345 ◽  
pp. 106844 ◽  
Author(s):  
Sara Cucchiaro ◽  
Federico Cazorzi ◽  
Lorenzo Marchi ◽  
Stefano Crema ◽  
Alberto Beinat ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Debris Flow ◽  
Temporal Analysis ◽  
Flow Channel ◽  
Check Dams ◽  
Multi Temporal

scholarly journals Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Optimal Time ◽  
Quantitative Detection ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

scholarly journals Remote Sensing in Predicting Development Patterns of Built-up Land

2021 ◽  
Vol 2 (2) ◽  
pp. 56-64
Author(s):  
Iqbal Eko Noviandi ◽  
Ramadhan Alvien Hanif ◽  
Hasanah Rahma Nur ◽  
Nandi
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Population Growth Rate ◽  
Landsat Imagery ◽  
Temporal Analysis ◽  
West Java ◽  
Development Patterns ◽  
Human Needs ◽  
Multi Temporal ◽  
Total Growth

Indonesia is a developing country whose construction and development are centered on the island of Java, especially in West Java Province. Sukabumi City is one of the areas in West Java. The development of urban areas is expanding due to various human needs to carry out the construction of buildings. Remote sensing that can be used to store developments with multi-temporal analysis with materials is Landsat imagery from 2001 to 2020. The method used is the Normalized Difference Built-up Index (NDBI). The purpose of this study is to map the development of the built-up land from year to year and predict the following years. The results of the research on the significant changes in built-up land occurred between 2013-2020, while from 2001 to 2013 there was not much change. Based on the research results, the total growth of built-up land was 1.539% per year with a population growth rate of 1.4% per year. The results of the analysis show that the area of ​​land built in Sukabumi City in 2028 is 186,7194 km2 or has increased by 21,2808 km2 since 2020.

Remote sensing of Australian wetlands: An evaluation of Landsat TM data for inventory and classification

1993 ◽  
Vol 44 (2) ◽  
pp. 235 ◽  
Author(s):  
RM Johnston ◽  
MM Barson
Keyword(s):  
Remote Sensing ◽  
Water Regime ◽  
Landsat Tm ◽  
Classification Methods ◽  
Vegetation Types ◽  
Infrared Band ◽  
South Wales ◽  
Multi Temporal ◽  
Density Slicing ◽  
Remote Sensing Techniques

This study aimed to develop simple remote-sensing techniques suitable for mapping and monitoring wetlands, using Landsat TM imagery of inland wetland sites in Victoria and New South Wales. A range of classification methods was examined in attempts to map the location and extent of wetlands and their vegetation types. Multi-temporal imagery (winter/spring and summer) was used to display seasonal variability in water regime and vegetation status. Simple density slicing of the mid-infrared band (TM5) from imagery taken during wet conditions was useful for mapping the location and extent of inundated areas. None of the classification methods tested reproduced field maps of dominant vegetation species; however, density slicing of multi-temporal imagery produced classes based on seasonal variation in water regime and vegetation status that are useful for reconnaissance mapping and for examining variability in previously mapped units. Satellite imagery is unlikely to replace aerial photography for detailed mapping of wetland vegetation types, particularly where ecological gradients are steep, as in many riverine systems. However, it has much to offer in monitoring changes in water regime and in reconnaissance mapping at regional scales.

Land Planning, Agro-forest Systems, and Implications for Ecosystem Services: Insights from Northern Italy

2021 ◽  
Author(s):  
Antonio Tomao ◽  
Barbara Ermini ◽  
Marcela Prokopov ◽  
Adriano Conte
Keyword(s):  
Land Use ◽  
Environmental Changes ◽  
Land Use Changes ◽  
Value Added ◽  
Temporal Analysis ◽  
Land Use Land Cover ◽  
Development Patterns ◽  
The North ◽  
Multi Temporal ◽  
Potential Impact

Negative environmental changes generally addressed as ‘syndromes’ are evaluated in the context of Soil Degradation (SD) and interpreted by using a ‘Land-Use/Land Cover Changes’ (LULCCs) framework in order to disentangle ‘past trajectories’, ‘present patterns’, and ‘future changes’. This approach allows to discuss the potential impact on SD processes and it represents an informed basis for identifying measurable outcomes of SD. This study focuses on the case of Emilia Romagna, a region located in the North of Italy with high-value added agricultural productions. A multi-temporal analysis of land-use changes between 1954 and 2008 has been proposed, discussing the evolution of associated SD syndromes in Emilia Romagna. The contributing information have been used as a baseline for Sustainable Land Management (SLM) strategies. This framework of analysis provides useful tools to investigate and to monitor the effects of SD in the Mediterranean basin where several regions underwent common development patterns yelding global pathological symptoms of environmental degradation.

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