Rice extraction in the Central Valley using multi-temporal Landsat images

Author(s):  
Liang Wang
2017 ◽  
Vol 8 (2) ◽  
pp. 288-292 ◽  
Author(s):  
R. Casa ◽  
F. Pelosi ◽  
S. Pascucci ◽  
F. Fontana ◽  
F. Castaldi ◽  
...  

Nitrogen fertilization of silage maize in Central Italy is typically carried out with two applications at early stages of crop development: 2nd (V2) and 6th (V6) leaf respectively. In such conditions, the crop has not yet fully covered the soil and proximal or remote sensing of the canopy is hindered by the strong soil background signal. There is thus great interest in rapid and inexpensive approaches to N fertilization prescription. Therefore, an indirect method for inferring information on yield potential and soil variability, through a field-based clustering of multi-temporal satellite data, has been developed using archive Landsat images to identify temporally constant patterns. This method is potentially useful for the creation of prescription maps. The usefulness of the method was evaluated during an N fertilisation field trial in Maccarese (Central Italy), in 2016. At the V2 stage, both uniform and variable rate applications were performed and compared. A pseudo-cross variogram and a standardized ordinary co-kriging methodology was used to highlight spatially variable significant differences among the treatments.


2019 ◽  
Vol 4 (1) ◽  
pp. 61-63
Author(s):  
Alhaji Mustapha Isa

Deforestation and climate change have become global environmental issues. The detection of forest changes in association with climate change can be successfully carried out by the use of multi-temporal remote sensing and modelling. This study undertook analysis of the past and present condition of the forest from the pattern changes of the Kota tinggi district johor state Malaysia, using landsat images of three different periods. These are thematic mapper (TM) data of 1998; enhanced thematic mapper (ETM+) image of 2008 and the operation land imager (OLI) of 2018 were collectively used. The images were geometrically and atmospherically pre-processed then classified, using maximum likelihood (M/C) algorithm to produce thematic land use/cover maps of the district. The accuracy of the classification was assessed through ground truthing and confusion matrices which revealed an accuracy of above 90% and kappa coefficient at 0.9 respectively.


2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst

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.


2019 ◽  
pp. 1624-1644
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.


Author(s):  
Carmelo Riccardo Fichera ◽  
Giuseppe Modica ◽  
Maurizio Pollino

One of the most relevant applications of Remote Sensing (RS) techniques is related to the analysis and the characterization of Land Cover (LC) and its change, very useful to efficiently undertake land planning and management policies. Here, a case study is described, conducted in the area of Avellino (Southern Italy) by means of RS in combination with GIS and landscape metrics. A multi-temporal dataset of RS imagery has been used: aerial photos (1954, 1974, 1990), Landsat images (MSS 1975, TM 1985 and 1993, ETM+ 2004), and digital orthophotos (1994 and 2006). To characterize the dynamics of changes during a fifty year period (1954-2004), the approach has integrated temporal trend analysis and landscape metrics, focusing on the urban-rural gradient. Aerial photos and satellite images have been classified to obtain maps of LC changes, for fixed intervals: 1954-1985 and 1985-2004. LC pattern and its change are linked to both natural and social processes, whose driving role has been clearly demonstrated in the case analysed. In fact, after the disastrous Irpinia earthquake (1980), the local specific zoning laws and urban plans have significantly addressed landscape changes.


GEOMATICA ◽  
2020 ◽  
Author(s):  
Liyuan Qing ◽  
Hasti A. Petrosian ◽  
Sarah N. Fatholahi ◽  
Michael A. Chapman ◽  
Jonathan Li

Urbanization is considered as one of the main factors affecting global change. The Halton Region as part of the Great Toronto Area (GTA), is regarded as one of the fastest growing regions in Canada, generating 20% of national GDP. It is also one of the most desirable places for living and thriving business. This research attempts to assess the urban expansion in the Halton Region, Ontario, Canada from 1989 to 2019 using satellite images, analysis approaches and landscape metrics. Multi-temporal Landsat images, and the supervised learning algorithms in GIS software were used to explore the dynamic changes, and to classify the urban and non-urban areas. The temporal urban expansion in the Halton Region experienced a dramatic rise, and mainly occurred from the centre of the area. The analysis of landscape metrics based on different methods, including Land Use in Central Indiana (LUCI) model, Vegetation-Impervious Surface-soil (V-I-S) model, and the census data of Canada was carried out to understand the transition mode of the urbanization in the Halton Region. Also, the population growth in the centre of the Halton Region was considered as one of driven forces affecting urban expansion. The results showed that most of the landscape metrics rose between 1989 and 2019, indicating leapfrog pattern of urbanization occurred over the entire period. The contribution of this research is to evaluate the urbanization in the Halton Region, and give the city managers a clear mind to make appropriate decisions in further urban planning.


2016 ◽  
Author(s):  
Anwar Abdelrahman Aly ◽  
Abdulrasoul Mosa Al-Omran ◽  
Abdulazeam Shahwan Sallam ◽  
Mohammad Ibrahim Al-Wabel ◽  
Mohammad Shayaa Al-Shayaa

Abstract. Vegetation cover (VC) changes detection is essential for a better understanding of the interactions and interrelationships between humans and their ecosystem. Remote sensing (RS) technology is one of the most beneficial tools to study spatial and temporal changes of VC. A case study has been conducted in the agro-ecosystem (AE) of Al-Kharj, in the centre of Saudi Arabia. Characteristics and dynamics of VC changes during a period of 26 years (1987–2013) were investigated. A multi-temporal set of images was processed using Landsat images; Landsat4 TM 1987, Landsat7 ETM+ 2000, and Landsat8 2013. The VC pattern and changes were linked to both natural and social processes to investigate the drivers responsible for the change. The analyses of the three satellite images concluded that the surface area of the VC increased by 107.4 % between 1987 and 2000, it was decreased by 27.5 % between years 2000 and 2013. The field study, review of secondary data and community problem diagnosis using the participatory rural appraisal (PRA) method suggested that the drivers for this change are the deterioration and salinization of both soil and water resources. Ground truth data indicated that the deteriorated soils in the eastern part of the Al-Kharj AE are frequently subjected to sand dune encroachment; while the south-western part is frequently subjected to soil and groundwater salinization. The groundwater in the western part of the ecosystem is highly saline, with a salinity ≥ 6 dS m−1. The ecosystem management approach applied in this study can be used to alike AE worldwide.


2020 ◽  
Author(s):  
Georg Veh ◽  
Daniel Garcia-Castellano ◽  
Oliver Korup

<p>The ongoing retreat of glaciers has formed several thousands of meltwater lakes in the Himalayas. Hundreds of these lakes have grown rapidly in area and volume in past decades, raising widely publicised concerns of an increasing hazard from sudden glacier lake outburst floods (GLOFs). Some 40 catastrophic lake outbursts have claimed thousands of fatalities and high losses in the Himalayas, mostly as a consequence of moraine-dam failures. Human and public safety along densely populated river reaches may thus be prone to changes in the lake size-distribution and the frequency of outburst floods. Yet multi-temporal inventories of Himalayan glacier lakes and associated outburst floods that we need for hazard appraisals have been collated only for selected basins with few standardised rules. Objectively tracing changes in regional GLOF hazard through time has thus remained elusive.</p><p>Here we meet this urgent demand for an improved GLOF hazard assessment. We estimate changes in the 100-year GLOF peak discharge from the late 1980s towards a scenario of completely ice-free Himalayas. We use a Random Forest model to predict land cover from seasonal Landsat images, and automatically extract glacier lakes for four time intervals. We obtain credible lake depths and volumes for each interval from a linear model learned from published bathymetric surveys. We further project possible sites for future Himalayan meltwater lakes from three published models of subglacial topography. We assume that these presently ice-covered depressions could fill completely with water though sediment and debris could decrease the storage space for future lakes. We simulate distributions of peak discharge for historic, present, and future lakes, accounting for different combinations of lake area, breach depth, and dam lithology. Most barrier types are unknown and could range from intact metamorphic bedrock to unconsolidated moraine debris. These two end members help to constrain the physically possible boundaries of GLOF peak discharges, which is supported by data from 82 natural dam breaks with known values of erodibility. To estimate the return periods of outburst floods, we used an extreme-value model to couple our simulations of peak discharge with mean annual rates of outburst floods, which remained unchanged in the Himalayas in the past three decades.</p><p>Given this constant rate of outburst floods, we report how hazard—expressed as the 100-year GLOF discharge—varied with regionally changing lake-size distributions in the past decades. We show that the southern Himalayas of Nepal and Bhutan had the largest increase of lake area, feeding notions of a rising GLOF hazard in this region. Hazard in the Western Himalaya, Karakoram, and Hindu Kush increased marginally, in line with the smallest historic abundance of glacier lakes and outburst floods. Future lake abundance and volumes may increase at least six-fold, with the largest lakes appearing in regions that have large glaciers today such as the Western Himalaya and the Karakoram. All other controls held constant, we find that hazard from these future lakes will largely rest on the erodibility of the barrier type, which needs to be acknowledged better in hazard appraisals.</p>


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