scholarly journals Seasonal Vegetation Response to Climate Variability on Land use Land Cover Changes using In-Situ and Satellite Imagery Observation Data for Semi-Arid Maasai Mara National Reserve Rangeland Ecosystem, Kenya

2021 ◽  
pp. 1-27
Author(s):  
Charles C. Kapkwang ◽  
Japheth O. Onyando ◽  
Peter M. Kundu ◽  
Joost Hoedjes
Keyword(s):  
Land Cover ◽  
Climate Variability ◽  
Biomass Production ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Policy Formulation ◽  
Geographical Information ◽  
Observation Data ◽  
Vegetation Response

Monitoring vegetation response through enhanced change detection by remote sensing and geographical information systems has tremendously improved real time information on surface features. Over the last few decades biomass monitoring at large scale has been made possible from information and metrics derived from satellite sensors. Maasai Mara National Reserve has been utilized in many decades as Kenyan natural grassland for wildlife grazing without periodic assessment of biomass production as affected by impact of climate variability yet it’s a tourism hub and one Kenyan economic contributor. This research evaluates the use of high spatial resolution satellite imagery such as the Moderate Resolution Imaging Spectro-radiometer or the Project for On-Board Autonomy–Vegetation and latest SENTINEL-2 for deriving the Normalized Difference Vegetation Index values in relations to in-situ measurements of biomass production between 2009 and 2019 in Mara, Kenya. Area frame sampling of biomass per unit area in Kgha-1clipped from 50cm by 50cm quadrats were used in destructive sampling. The reserve grassland area coverage was estimated to be 717.203km2 (46.75%) where the in-situ total above ground grass biomass projected in dry season was 35.094 tonha-1. This was approximated as 2,516,952.208 tonnes per the season reserve cover while in wet season, 42.123 tonha-1 was approximated as 3,021,074.197 tonnes. The error matrices developed to assess the accuracies of the ecosystem classification indicated values that ranged between 80-100% and 87.5-100% for producer’s and user’s accuracy respectively. 3 out of 7 satellite imagery maps (2017, 2018, and 2019) were assessed for accuracy using reference data collected during fieldwork in 2018 and 2019 in ecosystem. The overall accuracy was 95.22% with Kappa index of 0.94 for 14 land cover classes shown in table 7. From the findings, potential factors influencing vegetation growth in different climatic regions are varied and complex. It can be noted that climate variability influence vegetation response in spatial scale to supply sustainable quality vegetation/pasture for wildlife feeds and ecosystem development. Vegetation mapping and monitoring of ecosystem behavior help stakeholders with information of vegetation characteristics Decision policy formulation and wildlife planning.

scholarly journals Land use and Land cover Mapping Based on Normalized Difference Vegetation Index using Remote Sensing and Geographical Information System in Banjar River Watershed of Narmada Basin

2017 ◽  
Vol 12 (3) ◽  
pp. 678-684
Author(s):  
Jagriti Tiwari ◽  
S.K. Sharma ◽  
R.J. Patil
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Information System ◽  
Land Cover ◽  
Geographical Information System ◽  
Water Body ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geographical Information ◽  
River Watershed

The spatial analysis of land use and land cover (LULC) dynamics is necessary for sustainable utilization and management of the land resources of an area. Remote sensing along with Geographical Information System emerged as an effective technique for mapping the LU/LC categories of an area in an efficient and cost-effective manner. The present study was conducted in Banjar river watershed located in Balaghat and Mandla district of Madhya Pradesh, India. The Normalized Difference Vegetation Index (NDVI) approach was adopted for LU/LC classification of study area. The Landsat-8 satellite data of year 2013 was selected for the classification purpose. The NDVI values were generated in ERDAS Imagine 2011 software and LU/LC map was prepared in ARC GIS environment. On the basis of NDVI values five LU/LC classes were recognized in the study area namely river & water body, waste land & habitation, forest, agriculture/other vegetation, open land/fallow land/barren land. The forest cover was found to be highly distributed in the study area with an extent of 115811 ha and least area was found to be covered under river and water body (4057.28 ha). This research work will be helpful for the policy makers for proper formulation and implementation of watershed developmental plans.

Joint multifractal approach to characterize nonlinear relationships of climate and cereal growth in semiarid

2021 ◽  
Author(s):  
David Rivas-Tabares ◽  
Ana María Tarquis Alfonso
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Complex Networks ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Observation Data ◽  
Central Spain ◽  
Nonlinear Relationships ◽  
Fractal Properties

<p>Rainfed crops as cereals in the semiarid are common and extensive land cover in which climate, soils and atmosphere interact trough nonlinear relationships. Earth Observations coupled to ground monitoring network allow to improve the understanding of these relationships during each cropping season. However, novel analysis is required to understand these relationships in larger periods to improve sustainability and suitability of the productive areas in the semiarid.</p><p>The aim of this work is to use a joint multifractal approach using vegetation indices, precipitation, and temperatures to analyze atmosphere-plant nonlinear relationships. For this, time series of 20 cropping seasons were used to characterize these relationships in central Spain. The Generalized Structure Function and the derived Generalized Hurst Exponent analysis were implemented to investigate precipitation, vegetation indices and temperature time series. For this, an exhaustive selection based on land use and a land cover change analysis was performed to detect plots in which cereal crop sequences are dedicated to barley and wheat over the period 2000 to 2020.</p><p>As a result, two agro zones were characterized by different multifractal properties. Precipitation series show antipersistent characteristics and fractal properties between zones while original vegetation indices show trending behavior but shifted between analyzed zones. Nonetheless, soils and rainfall events can vary interannual conditions in which the crop is developing. For vegetation indices long-term series the trend is persistent. Even so, the dynamics of vegetation indices also provide more information when annual patterns are extracted from the series, exhibiting fractal properties mainly from rainfall pattern of each zone. Finally, in this case, the joint multifractal analysis served to characterize agro zones using earth observation and climate data for extensive cereals in Central Spain.</p><p><strong>Reference</strong></p><p>Rivas-Tabares D., Tarquis A.M. (2021) Towards Understanding Complex Interactions of Normalized Difference Vegetation Index Measurements Network and Precipitation Gauges of Cereal Growth System. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_51</p><p><strong>Acknowledgements</strong></p><p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330 and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020.</p>

scholarly journals Digital aerial images land cover classification based on vegetation indices

2012 ◽  
Vol 31 (3) ◽  
pp. 5-23
Author(s):  
Maciej Dzieszko ◽  
Piotr Dzieszko ◽  
Sławomir Królewicz
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Geographical Information Systems ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Aerial Images ◽  
Geographical Information ◽  
Wide Availability

Abstract . Knowledge of how land cover has changed over time improve assessments of the changes in the future. Wide availability of remote sensed data and relatively low cost of their acquisition make them very attractive data source for Geographical Information Systems (GIS). The main goal of this paper is to prepare, run and evaluate image classification using a block of raw aerial images obtained from Digital Mapping Camera (DMC). Classification was preceded by preparation of raw images. It contained geometric and radiometric correction of every image in block. Initial images processing lead to compensate their brightness differences. It was obtained by calculating two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Green Normalized Vegetation Index (gNDVI). These vegetation indices were the foundation of image classification. PCI Geomatics Geomatica 10.2 and Microimages TNT Mips software platforms were used for this purpose.

Topographic normalization of satellite imagery for image classification in northeast India

2009 ◽  
Vol 33 (6) ◽  
pp. 815-836 ◽  
Author(s):  
M. Balakrishna Reddy ◽  
Baiantimon Blah
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Resource Planning ◽  
Northeast India ◽  
Training Data ◽  
Vegetation Types ◽  
Bivariate Correlation ◽  
Liss Iii

IRS-LISS-III satellite imagery covering Nongkhyllem Wildlife Sanctuary area located within the Ri-Bhoi District of Meghalaya State, northeast India, was used for analysis of the landcover pattern and vegetation types occurring there. A maximum likelihood classifier was used to generate a supervised classification into land-cover types and the vegetation types within the forested area. The preparation of training data sets used thematic maps of the area, and knowledge accruing from extensive personal field visits. Sample field plots were located at 30 different places in the Sanctuary for classification accuracy assessment. The Normalized Difference Vegetation Index (NDVI) was also computed from LISS-III satellite imagery. A digital elevation model (DEM) of the Sanctuary was generated using a GIS. The DEM was used to test the hypothesis that its joint use with the satellite data would increase classification accuracy. This proved to be the case. Bivariate correlation analysis was performed between spectral and DEM variables to cross-check the results. In the example used, that of the rugged terrain in mountainous parts of northeast India, such integration of satellite land-cover data and DEM information appears to be a necessity in improved land-cover mapping for resource planning and utilization.

scholarly journals Exploring Land use and Land cover change in the mining areas of Wa East District, Ghana using Satellite Imagery

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Prosper Laari Basommi ◽  
Qingfeng Guan ◽  
Dandan Cheng
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Geographical Information ◽  
Kappa Statistics ◽  
Image Differencing ◽  
Mining Areas ◽  
Land Cover Dynamics ◽  
Change Matrix ◽  
Landsat Satellite

AbstractSatellite imagery has been widely used to monitor the extent of environmental change in both mine and post mine areas. This study uses Remote sensing and Geographical Information System techniques for the assessment of land use/land cover dynamics of mine related areas in Wa East District of Ghana. Landsat satellite imageries of three different time periods, i.e., 1991, 2000 and 2014 were used to quantify the land use/cover changes in the area. Supervised Classification using Maximum Likelihood Technique in ERDAS was utilized. The images were categorized into five different classes: Open Savannah, Closed Savannah, Bare Areas, Settlement and Water. Image differencing method of change detection was used to investigate the changes. Normalized Differential Vegetative Index valueswere used to correlate the state of healthy vegetation. The image differencing showed a positive correlation to the changes in the Land use and Land cover classes. NDVI values reduced from 0.48 to 0.11. The land use change matrix also showed conversion of savannah areas into bare ground and settlement. Open and close savannah reduced from 50.80% to 36.5% and 27.80% to 22.67% respectively whiles bare land and settlement increased. Overall accuracy of classified 2014 image and kappa statistics was 83.20% and 0.761 respectively. The study revealed the declining nature of the vegetation and the significance of using satellite imagery. A higher resolution satellite Imagery is however needed to satisfactorily delineate mine areas from other bare areas in such Savannah zones.

scholarly journals Evaluating Vegetation Response to Climate Variability over Japan

2021 ◽  
pp. 30-41
Author(s):  
Adigun Paul Ayodele ◽  
Adawa Ifeoluwa Seun
Keyword(s):  
Standard Deviation ◽  
Climate Variability ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climate Adaptation ◽  
Temporal Distribution ◽  
Northern Region ◽  
Vegetation Response ◽  
Increasing Trend

Vegetation plays a significant role in the exchange of energy, water and carbon between the atmosphere and land surface, understanding its response to climate variability is of great importance for climate adaptation studies. This study examined Seasonal June-July- August, and December-January-February(JJA and DJF) vegetation response to Temperature(T) and Rainfall(R) variability. Vegetation response to climate dynamics over Japan are still poorly understood, in other to quantify these response spatio-temporal distribution of T and R were investigated, vegetations changes was  also accessed utilizing MODIS Normalized Difference Vegetation Index (NDVI) data from 2007-2016(10 years) along with T and R datasets from 1987-2017 (31 years), The NDVI patterns show a checked heterogeneity relating to seasonal variations in climates,  our findings further reveals Northern region record an  increasing trend in T and R, standard deviation of 0.48, 9.66, with CV of 6.63%, 9.25% respectively were recorded. Also, an increasing  trend in T and R  was equally observed in the southern region with standard deviation of 0.43, 28.5, by a CV of 2.47% and 15.05. Further analysis revealed critical patterns in the NDVI during DJF months and then afterward  NDVI was seen with critical expanding values during the JJA month and diminishing NDVI patterns were seen over similar districts. The result further made it clear that NDVI changes were highly connected to different  T and R  patterns over the region while seasonal mean NDVI showed a critical increment for JJA in the North and DJA in the south.

scholarly journals Estimation and monitoring of aboveground carbon stocks using spatial technology

2015 ◽  
Vol 111 (9/10) ◽  
Author(s):  
Adolph Nyamugama ◽  
Vincent Kakembo
Keyword(s):  
Regression Model ◽  
Satellite Imagery ◽  
Geographical Information Systems ◽  
Vegetation Index ◽  
Anthropogenic Activities ◽  
Temporal Changes ◽  
Geographical Information ◽  
Aboveground Carbon ◽  
Normalised Difference Vegetation Index ◽  
Spot 5

Monitoring temporal changes of aboveground carbon (AGC) stocks distribution in subtropical thicket is key to understanding the role of vegetation in carbon sequestration. The main objectives of this research paper were to model and quantify the temporal changes of AGC stocks between 1972 and 2010 in the Great Fish River Nature Reserve and its environs, Eastern Cape Province, South Africa. We used a method based on the integration of remote sensing and geographical information systems to estimate AGC stocks in a time series framework. A non-linear regression model was developed using Normalised Difference Vegetation Index values generated from SPOT 5 High Resolution Geometric satellite imagery of 2010 as an independent variable and AGC stock estimates from field plots as the dependent variable. The regression model was used to estimate AGC stocks from satellite imagery for 1972 (Landsat TM), 1982 (Landsat 4 TM), 1992 (Landsat 7 ETM), 2002 (Landsat ETM+) and 2010 (SPOT 5) satellite imagery. AGC stocks for the respective years were compared by means of change detection analysis at the subtropical thicket class level. The results showed a decline of AGC stocks in all the classes from 1972 to 2010. Degraded and transformed thicket classes had the highest AGC stock losses. The decline of AGC stocks was attributed to thicket transformation and degradation, which were attributed to anthropogenic activities.

scholarly journals Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India

2018 ◽  
Vol 11 (1) ◽  
pp. 5-18 ◽  
Author(s):  
Sunita Singh ◽  
Praveen Kumar Rai
Keyword(s):  
Land Cover ◽  
Supervised Classification ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Landsat 8 ◽  
Observation Data ◽  
Agriculture Land

Abstract Digital change detection is the process that helps in shaping the changes associated with land use land cover (LULC) properties with reference to geo-registered multi-temporal remote sensing data. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time (1980-2016). The methods represented in this study are vegetation indices, image differencing and supervised classification. These methods gave different results in terms of land cover area. Urban expansion has brought serious losses of agriculture land, vegetation and water bodies. The present study demonstrates changes in land trajectories of Varanasi district, India using Landsat MSS (1980), TM (1990 and 2010), ETM+ (2000) and Landsat-8 OLI data (2016). The LULC classes in the study area are divided into eight categories using supervised classification method. Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are also calculated to estimate the changes in LULC classes during these time periods. Major changes are seen from 2000 to 2016 for the built-up, agriculture land, water bodies and wasteland.

scholarly journals FUSION OF SENTINEL-2 AND PLANETSCOPE IMAGERY FOR VEGETATION DETECTION AND MONITORING

2018 ◽  
Vol XLII-1 ◽  
pp. 155-160 ◽  
Author(s):  
M. Gašparović ◽  
D. Medak ◽  
I. Pilaš ◽  
L. Jurjević ◽  
I. Balenović
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Characteristics ◽  
Land Cover Classification ◽  
Vegetation Monitoring ◽  
Sentinel 2

<p><strong>Abstract.</strong> Different spatial resolutions satellite imagery with global almost daily revisit time provide valuable information about the earth surface in a short time. Based on the remote sensing methods satellite imagery can have different applications like environmental development, urban monitoring, etc. For accurate vegetation detection and monitoring, especially in urban areas, spectral characteristics, as well as the spatial resolution of satellite imagery is important. In this research, 10-m and 20-m Sentinel-2 and 3.7-m PlanetScope satellite imagery were used. Although in nowadays research Sentinel-2 satellite imagery is often used for land-cover classification or vegetation detection and monitoring, we decided to test a fusion of Sentinel-2 imagery with PlanetScope because of its higher spatial resolution. The main goal of this research is a new method for Sentinel-2 and PlanetScope imagery fusion. The fusion method validation was provided based on the land-cover classification accuracy. Three land-cover classifications were made based on the Sentinel-2, PlanetScope and fused imagery. As expected, results show better accuracy for PS and fused imagery than the Sentinel-2 imagery. PlanetScope and fused imagery have almost the same accuracy. For the vegetation monitoring testing, the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and fused imagery was calculated and mutually compared. In this research, all methods and tests, image fusion and satellite imagery classification were made in the free and open source programs. The method developed and presented in this paper can easily be applied to other sciences, such as urbanism, forestry, agronomy, ecology and geology.</p>

scholarly journals Vegetation density analysis in Banda Aceh city before and after the tsunami using satellite imagery data

2022 ◽  
Vol 951 (1) ◽  
pp. 012073
Author(s):  
M Trishiani ◽  
S Sugianto ◽  
T Arabia ◽  
M Rusdi
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Landsat 8 ◽  
Vegetation Density ◽  
Physical Damage ◽  
Tsunami Waves ◽  
Tsunami Disaster ◽  
Before And After ◽  
Banda Aceh

Abstract Vegetation density in Banda Aceh is an important aspect of monitoring the recovery process after being hit by a tsunami on December 26, 2004. The tsunami disaster had a tremendous impact on Banda Aceh city, both physical and non-physical damage. As a result, a lot of vegetation was swept away by the tsunami waves. After the tsunami disaster, Banda Aceh City carried out rehabilitation and reconstruction to change the land cover. The increasing population growth in the city also has affected land cover. Changes in land use not following the spatial plan of the Banda Aceh can reduce the quality of the environment, e.g., reducing the vegetation density in some areas. This paper presents the utilization of Landsat 7 and Landsat 8 images to analyze the vegetation density in Banda Aceh city before dan after the tsunami in the last 15 years. This study aims to determine the ability of satellite imagery to detect vegetation density in Banda Aceh in designated years before and after the tsunami. This study uses the Normalized Difference Vegetation Index analysis to observe the trend of vegetation density in the Banda Aceh. Results show that the vegetation density in Banda Aceh City in 2004, 2005, 2009, 2015, and 2020 was dominated by low-density classes. Still, in 2015 and 2020, there was an increase in medium and high vegetation density classes. This finding shows the pattern of the vegetation density follows the progress of the recovery after 15 years hit by a tsunami.

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