scholarly journals Forest Change Detection in Mosul Province using RS and GIS Techniques

2021 ◽  
pp. 3779-3789
Author(s):  
Faisel G. Mohammed ◽  
Maryam H. Ali ◽  
Sajaa G. Mohammed ◽  
Hiba S. Saeed
Keyword(s):  
Software Package ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Decision Makers ◽  
Agricultural Crops ◽  
Forest Change ◽  
Moisture Index ◽  
Tigris River ◽  
Rs And Gis ◽  
Emergency Events

    There are many events that took place in Al Mosul province between 2013 and 2018. These events led to many changes in the area under study. These changes involved a decrease in agricultural crops and water due to the population leaving the area. Therefore, it is imperative that planners, decision-makers, and development officials intervene in order to restore the region's activity in terms of environment and agriculture. The aim of this research is to use remote sensing (RS) technique and geographic information system (GIS) to detect the change that occurred in the mentioned period. This was achieved through the use of the ArcGIS software package for the purpose of assessing the state of lands of agricultural crops and forests. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) were adopted in the current calculations. This can help the decision-maker take the necessary measures to avoid the problems caused by the emergency events. The results obtained through this research showed that the region had rate changes in farms, water, and forests of about 1%, as it was found that there was a decrease in the level of the Tigris River and an increase in the area of ​​ carrot crop farms. Also, the results indicated a decrease in areas of agricultural crops in specific regions, while they increased in others.

scholarly journals Development of a remote sensing-based rice yield forecasting model

2016 ◽  
Vol 14 (3) ◽  
pp. e0907 ◽  
Author(s):  
Mostafa K. Mosleh ◽  
Quazi K. Hassan ◽  
Ehsan H. Chowdhury
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rice Yield ◽  
Decision Makers ◽  
Coefficient Of Determination ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Boro Rice

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

scholarly journals SURFACE TEMPERATURE DYNAMICS IN RESPONSE TO LAND COVER TRANSFORMATION

2020 ◽  
Vol 11 (2) ◽  
pp. 94-110 ◽  
Author(s):  
Syed Riad Morshed Riad Morshed ◽  
Md. Abdul Fattah ◽  
Asma Amin Rimi ◽  
Md. Nazmul Haque
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Moisture Index ◽  
Type Transformation ◽  
Spatial Environment ◽  
Supervised Image Classification

This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand, the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is the transformation of land cover in buildup areas.

scholarly journals Using Synthetic Remote Sensing Indicators to Monitor the Land Degradation in a Salinized Area

2021 ◽  
Vol 13 (15) ◽  
pp. 2851
Author(s):  
Tao Yu ◽  
Guli Jiapaer ◽  
Anming Bao ◽  
Guoxiong Zheng ◽  
Liangliang Jiang ◽  
...  
Keyword(s):  
Land Degradation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Salt Content ◽  
Principal Component ◽  
Water Withdrawal ◽  
Moisture Index ◽  
High Positive Correlation ◽  
Amu Darya

Land degradation poses a critical threat to the stability and security of ecosystems, especially in salinized areas. Monitoring the land degradation of salinized areas facilitates land management and ecological restoration. In this research, we integrated the salinization index (SI), albedo, normalized difference vegetation index (NDVI) and land surface soil moisture index (LSM) through the principal component analysis (PCA) method to establish a salinized land degradation index (SDI). Based on the SDI, the land degradation of a typical salinized area in the Central Asia Amu Darya delta (ADD) was analysed for the period 1990–2019. The results showed that the proposed SDI had a high positive correlation (R2 = 0.89, p < 0.001) with the soil salt content based on field sampling, indicating that the SDI can reveal the land degradation characteristics of the ADD. The SDI indicated that the extreme and strong land degradation areas increased from 1990 to 2019, mainly in the downstream and peripheral regions of the ADD. From 1990 to 2000, land degradation improvement over a larger area than developed, conversely, from 2000 to 2019, and especially, from 2000 to 2010, the proportion of land degradation developed was 32%, which was mainly concentrated in the downstream region of the ADD. The spatial autocorrelation analysis indicated that the SDI values of Moran’s I in 1990, 2000, 2010 and 2019 were 0.82, 0.78, 0.82 and 0.77, respectively, suggesting that the SDI was notably clustered in space rather than randomly distributed. The expansion of unused land due to land use change, water withdrawal from the Amu Darya River and the discharge of salt downstream all contributed to land degradation in the ADD. This study provides several valuable insights into the land degradation monitoring and management of this salinized delta and similar settings worldwide.

scholarly journals Remote Detection of Cyanobacterial Blooms and Chlorophyll-a Analysis in a Eutrophic Reservoir Using Sentinel-2

2021 ◽  
Vol 13 (15) ◽  
pp. 8570
Author(s):  
Manuel Viso-Vázquez ◽  
Carolina Acuña-Alonso ◽  
Juan Luis Rodríguez ◽  
Xana Álvarez
Keyword(s):  
Chlorophyll A ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Governance ◽  
Water Security ◽  
Cyanobacterial Blooms ◽  
Innovative Technology ◽  
Moisture Index ◽  
Water Index ◽  
Sentinel 2

Harmful cyanobacterial blooms have been one of the most challenging ecological problems faced by freshwater bodies for more than a century. The use of satellite images as a tool to analyze these blooms is an innovative technology that will facilitate water governance and help develop measures to guarantee water security. To assess the viability of Sentinel-2 for identifying cyanobacterial blooms and chlorophyl-a, different bands of the Sentinel-2 satellite were considered, and those most consistent with cyanobacteria analysis were analyzed. This analysis was supplemented by an assessment of different indices and their respective correlations with the field data. The indices assessed were the following: Normalized Difference Water Index (NDWI), Normalized Differences Vegetation Index (NDVI), green Normalized Difference Vegetation Index (gNDVI), Normalized Soil Moisture Index (NSMI), and Toming’s Index. The green band (B3) obtained the best correlating results for both chlorophyll (R2 = 0.678) and cyanobacteria (R2 = 0.931). The study by bands of cyanobacteria composition can be a powerful tool for assessing the physiology of strains. NDWI gave an R2 value of 0.849 for the downstream point with the concentration of cyanobacteria. Toming’s Index obtained a high R2 of 0.859 with chlorophyll-a and 0.721 for the concentration of cyanobacteria. Notable differences in correlation for the upstream and downstream points were obtained with the indices. These results show that Sentinel-2 will be a valuable tool for lake monitoring and research, especially considering that the data will be routinely available for many years and the images will be frequent and free.

scholarly journals DETECTION AGRICULTURE DEGRADATION FOR THE SOUTH OF BAGHDAD CITY USING REMOTE SENSING DATA FOR YEARS 2010-2019

2020 ◽  
pp. 57-67
Author(s):  
Taif Adil DHAMIN ◽  
Ebtesam F. KHANJER ◽  
Fouad K. MASHEE
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Military Operations ◽  
The South ◽  
Water Index ◽  
Rs And Gis ◽  
Landsat Satellite

Recently, the develop of the science of remote sensing enabled humanity to achieve the accuracy and wide coverage for different natural phenomena, disasters and applications (such as desertification, rainstorms, floods, fires, sweeping torrents, urban planning, and even in military). The main aim of this study is monitoring, highlighting and assessing maps for the degradation of agriculture in the south areas of Baghdad governorate (Al-Rasheed, Al-Yusufiyah, Al-Mahmudiyah, Al-Latifiyah, and Al-Madaen). Based to several factors, including the economic, social and military operations, the area had suffer the lands degradation which led to agriculture retreating. Remote sensing and Geographic information system (GIS) was applied, using ArcGIS 10.4.1 to process, manage, and analysis datasets, beside field verification to estimate the severity assessment of a computerized land degradation. Two satellites were adapted Landsat5 TM+ and Landsat8 OLI/TIRS imageries to assess the extent of land degradation for the study area during the years (5th May 2010 and 2nd May 2019). Two indices used in this research are: The Normalized Difference Vegetation Index “NDVI”, and The Normalized Differential Water Index “NDWI”. The results showed that there is a clear spatial reduction in both NDVI and NDWI, where the NDVI reduced from 2461082400 m2 to 1552698000 m2, accounting for 89.67 and 56.57 percent, respectively, while the NDWI reduced from 14166000 m2 to 12053700 m2, accounting for 0.52, and 0.44 percent, respectively. Keywords: Agriculture Degradation, RS And GIS Techniques, Landsat Satellite Imagery, NDVI And NDWI.

scholarly journals A global approach to estimate irrigated areas – a comparison between different data and statistics

2018 ◽  
Vol 22 (2) ◽  
pp. 1119-1133 ◽  
Author(s):  
Jonas Meier ◽  
Florian Zabel ◽  
Wolfram Mauser
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
Decision Makers ◽  
Data Sets ◽  
Ancillary Data ◽  
Irrigated Area ◽  
Time Period ◽  
Multi Temporal

Abstract. Agriculture is the largest global consumer of water. Irrigated areas constitute 40 % of the total area used for agricultural production (FAO, 2014a) Information on their spatial distribution is highly relevant for regional water management and food security. Spatial information on irrigation is highly important for policy and decision makers, who are facing the transition towards more efficient sustainable agriculture. However, the mapping of irrigated areas still represents a challenge for land use classifications, and existing global data sets differ strongly in their results. The following study tests an existing irrigation map based on statistics and extends the irrigated area using ancillary data. The approach processes and analyzes multi-temporal normalized difference vegetation index (NDVI) SPOT-VGT data and agricultural suitability data – both at a spatial resolution of 30 arcsec – incrementally in a multiple decision tree. It covers the period from 1999 to 2012. The results globally show a 18 % larger irrigated area than existing approaches based on statistical data. The largest differences compared to the official national statistics are found in Asia and particularly in China and India. The additional areas are mainly identified within already known irrigated regions where irrigation is more dense than previously estimated. The validation with global and regional products shows the large divergence of existing data sets with respect to size and distribution of irrigated areas caused by spatial resolution, the considered time period and the input data and assumption made.

scholarly journals PREDICTING HABITAT DISTRIBUTION OF ENDEMIC AND CRITICALLY ENDANGERED DIPTEROCARPUS LITTORALIS IN NUSAKAMBANGAN, INDONESIA

REINWARDTIA ◽  
2017 ◽  
Vol 16 (1) ◽  
pp. 11
Author(s):  
Iyan Robiansyah
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Critically Endangered ◽  
Habitat Distribution ◽  
Moisture Index ◽  
Water Index ◽  
Maxent Model

ROBIANSYAH, I. 2017. Predicting habitat distribution of endemic and critically endangered Dipterocarpus littoralis in Nusakambangan, Indonesia. Reinwardtia 16(1): 11 - 18. - The tree species Dipterocarpus littoralis (Bl.) Kurz. is endemic to Nusakambangan and categorized as critically endangered. In the present study, the habitat suitability of the species in Nusakambangan was predicted using logistic regression analysis and Maxent model. Three topographic variables (elevation, slope, and aspect), distance from river and coastline, and one vegetation index (Normalized Difference Vegetation Index (NDVI)) as well as two water content indexes (Normalized Difference Water Index (NDWI) and Normalized Difference Moisture Index (NDMI)) were used as predictors of the models. Employing initial number of 82 presence and 250 absence data of D. littoralis, both models were able to predict the suitable areas for the species with fairly high success rate. The AUC and Kappa value for logistic regression were 0.77 ± 0.027 and 0.34 ± 0.058, respectively, while the respected values for Maxent were 0.91 ± 0.062 and 0.37 ± 0.025. Logistic regression analysis identified a total area of 26.13 km2 to be suitable for D. littoralis, while a smaller suitable area (7.85 km2) was predicted by Maxent model. Coastal areas in the west part of the island were predicted by both models as areas with high suitability for D. littoralis. Furthermore, distance from coastline and river, elevation, NDVI, NDWI and NDMI were suggested to be very important for the species ecology and distribution. The results of this study may serve as a basis for population reinforcement and reintroduction programs of D. littoralis and guide for ecosystem management of Nusakambangan Island as a whole. 

scholarly journals MONITORING POST-DISASTER MANGROVE FOREST RECOVERIES IN LAWAAN-BALANGIGA, EASTERN SAMAR USING TIME SERIES ANALYSIS OF MOISTURE AND VEGETATION INDICES

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 295-301
Author(s):  
K. V. Ticman ◽  
S. G. Salmo III ◽  
K. E. Cabello ◽  
M. Q. Germentil ◽  
D. M. Burgos ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Landsat 8 ◽  
Positive Trend ◽  
Mangrove Forests ◽  
Moisture Index ◽  
Biophysical Parameters ◽  
Spatial Coverage

Abstract. The mangrove forests of Lawaan-Balangiga in Eastern Samar lost significant cover due to the Typhoon Haiyan that struck the region in 2013. The mangroves in the area have since shown signs of recovery in terms of growth and spatial coverage, but these widely varied with locations. This study aims to further examine the status of recovery of mangroves across different locations by analysing the time series trends of selected vegetation and moisture indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), and Normalized Difference Moisture Index (NDMI). These indices were extracted from Landsat 8 surface reflectance images, spanning 2014 to 2020, using Google Earth Engine (GEE). The time series analyses showed similar NDVI, MSAVI and NDMI values and trends after the 2013 typhoon event. The trend slopes also indicated high correlation (0.91 – 1.00) between and among the indices, with NDVI having the highest correlation with MSAVI (∼1.00). The study was able to corroborate the previous study on mangroves in Lawaan-Balangiga, by presenting positive trend results in the identified recovered areas. These trends, however, would still have to be validated by collecting and comparing biophysical parameters in the field. The next step of the research would be to identify the factors that contribute to the varying rates of recovery in the areas and to evaluate how this can affect the carbon sequestration rates of recovering mangroves.

scholarly journals Environmental sensitivity to mosquito transmitted diseases in El-Fayoum using spatial analyses

2020 ◽  
Vol 167 ◽  
pp. 03002
Author(s):  
Asmaa M. El-Hefni ◽  
Ahmed M. El-Zeiny ◽  
Hala A. Effat
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Malaria Vectors ◽  
Moisture Index ◽  
Risk Area ◽  
Gis Modeling ◽  
Breeding Sites ◽  
Mosquito Breeding ◽  
Fayoum Governorate

El-Fayoum governorate has unique characteristics which induces mosquito proliferation and thus increased the risk arisen from diseases transmission. Present study explores the role of remote sensing and GIS modeling integrated with field survey for mapping mosquito breeding sites and the areas under risk of diseases transmission in El-Fayoum governorate. Entomological surveys were conducted for a total number of 40 accessible breeding sites during the period 12-16 November 2017. A calibrated Landsat OLI image, synchronized with the field trip, was processed to produce Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). A cartographic GIS model was generated to predict breeding sites in the whole governorate and to assess the potential risk. The main filarial disease vector (Culex pipiens) was abundant at Atsa district, while Malaria vectors (Anopheles sergentii and Anopheles multicolor) were mainly distributed in El-Fayoum and Youssef El-Seddiq districts. Means levels of NDVI, NDMI and LST at breeding habitats were recorded; 0.18, 0.08 and 21.75° C, respectively. Results of the model showed that the highest predicted risk area was reported at Atsa district (94.4 km2) and Yousef El-Sediq (81.8 km2) while the lowest prediction was observed at Abshawai district (35.9 km2). It can be concluded that Atsa, Yousef El-Sedik and El-Fayoum districts are more vulnerable to Malaria and Filaria diseases outbreaks, thus precaution and pest control methods must be applied to mitigate the possible risks.

Sign in / Sign up

Export Citation Format

Share Document