SAVI Indices Research Articles

Nitrogen doses in topdressing affect vegetation indices and corn yield

Nitrogen is the main nutrient required by corn crop, especially in Cerrado soils. Remote sensing techniques can be used to generate additional information now of nitrogen fertilization recommendation. This work investigated the association of plant height and dry matter phenological variables together with NDVI, REDEDGE, SAVI, and IV 760/550 vegetation indices (VIs) with corn grain yield, under different N doses. Sowing occurred in November 2016, at a spacing of 0.45 m between rows and a 60,000 ha-1 plant population. Four N doses (0, 80, 160, and 240 kg of N ha-1) were applied at phenological stage V4. The experimental design consisted of randomized blocks, containing four N doses in topdressing and 16 replications. The active optical sensor Crop Circle ACS-470 was used to obtain the VIs. The NDVI, SAVI, and RE indices have a high positive association with each other and with the variables plant height and dry matter. Polynomial regression equations were adjusted between the variables in response as doses of N. Afterwards, they were estimated as correlations between variables and results expressed through the network of correlations. Finally, a multivariate analysis of canonical variables was performed to understand the interrelationship between the variables and each dose of N applied. NDVI and RE have a positive relationship of moderate magnitude with grain yield in corn crops.

Method of Elaboration of Detailed Existing Land Use (ELU) Map of Poland

Method of Elaboration of Detailed Existing Land Use (ELU) Map of Poland

Mitigation & Identification for Local Aridity, Based of Vegetation Indices Combined with Spatial Statistics & Clustering K Means

This research aims to develop new methods of mapping aridity risk zones and their potential impacts on land fires using a combination of indices to identify land fires such as the CSI and NBR and the indices for NDVI and SAVI. The research location is in Gunung Merapi National Park (TNGM) and Gunung Merbabu National Park (TNGMb), in Central Java Province and Yogyakarta. The data used in this research is Landsat 8 OLI image year 2010-2018, DEM data from ASTER image in TNGM and TNGMb area using landsat 8 OLI image specification. The research was conducted in 3 stages such as pre-processing, image data extraction and post-processing. Global and Local Moran’s Analysis on NDVI, SAVI, CSI and NBR vegetation indices data can be used as an indicator of aridity and potential land fires. The experiments show that the average is in class 4 including the moderate greenish classification. Moderate greenish is interpreted that the study area is overgrown with meadows, shrubs, barren, sandy, rocky areas and a low population of vegetation canopy that shows that the area is on the surrounding mountain peaks. The results of the analysis shows Positive Spatial Autocorrelation, the phenomenon of aridity has spatial connectivity between observed regions. Analysis of K Means on the high vegetation density conditions shows that the weight of the distance between the vegetation data to the centroid is shorter, therefore the data is concentrated on a region. In low vegetation density conditions, the weight of the distance between the data to the centroid is increasingly wide, therefore the data looks more widely distributed.

Assessing the capability of broadband indices derived from Landsat 8 Operational Land Imager to monitor above ground biomass and salinity in semiarid saline environments of the Bahía Blanca Estuary, Argentina

ABSTRACTIn arid and semi-arid ecosystems, salinisation and desertification are the most common processes of land degradation, and satellite data may provide a valuable tool to assess land surface condition and vegetation status. The aim of this study was to evaluate the capability of Landsat 8 OLI (Operational Land Imager) remote sensing information and broadband indices derived from it, to monitor above ground biomass (AGB) and salinity in two different semiarid saline environments (unit a and unit b) in the Bahía Blanca Estuary. Unit a (Ua) is composed of bushes of Cyclolepis genistoides in association with Atriplex undulata and 41% of bare soil. Unit b (Ub) is composed of dense thickets of Allenrolfea patagonica in association with C. genistoides and 34% of bare soil. Pearson’s correlation analyses were performed between field estimates of AGB and salinity (soil salinity and interstitial water salinity) and remote sensing estimates. Satellite data include surface reflectance of individual bands, vegetation indices (NDVI [normalised difference vegetation index], SAVI [soil-adjusted vegetation index], MSAVI2 [modified soil-adjusted vegetation index], NDII [normalised difference infrared index], GNDVI [green normalised difference vegetation index], GRNDI [green-red normalised difference index], OSAVI [optimised soil-adjusted vegetation index], SR [simple ratio]), and salinity indices (SI1, SI2, SI3 [salinity index 1, 2 and 3, respectively] and BI [brightness index]). Correlation analyses involving AGB were performed twice; first considering all months and then again excluding the months with higher soil salinities. In Ua, soil adjusted vegetation indices SAVI and MSAVI2 showed to be suitable to detect changes in the total green AGB and C. genistoides green AGB (the major contributor to total green AGB). After excluding data from December and January (the months with the highest soil salinity), green AGB of A. undulata also showed a significant positive correlation with soil adjusted indices SAVI, MSAVI2 and OSAVI. Although proportionally this species was not a large contributor to the total biomass, it is characterised by a high leaf reflectance, which makes it suitable for biomass retrieval. In Ub, significant positive correlations were obtained between NDVI, SAVI, NDII, OSAVI and SR indices and the AGB green ratio, but significant negative correlations were obtained between A. patagonica red AGB and these vegetation indices. When December and January were excluded from the analysis the negative correlations between vegetation indices NDVI, OSAVI and SR and red AGB remained significant (r = −0.68, −0.76 and −0.7, respectively). The positive correlations between these indices and AGB green ratio (r = 0.73, 0.78 and 0.75, respectively) remained significant as well. Significant negative correlations were also found between NDVI, NDII, GNDVI, OSAVI and SR indices and field salinity estimates. As soil salinisation induces A. patagonica reddening, red AGB and soil salinity covariate in the field, and the negative correlation with vegetation indices may be useful to retrieve information on both variables combined, which are indicative of water stress. Correlation analysis between field estimates of salinity and spectral salinity indices showed significant positive correlation for all the tested indices. The obtained results highlight the importance of a thoughtful selection of remote sensing indices to account for changes in vegetation biomass, especially in arid and semiarid environments particularly sensitive to desertification and salinisation. Also, ground truth cannot be overlooked, and field work is necessary to test index performance in every case.

Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images

ABSTRACT We have developed a simple photogrammetric method to identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an unmanned aerial vehicle (UAV). By comparing NDVI, GNDVI, SAVI, and NDRE vegetation indices, we find that the latter shows irrigation irregularities in an olive grove not discernible with the other indices. This may render the NDRE as particularly useful to identify growth inhomogeneities in crops. Given the fact that few satellite detectors are sensible in the red-edge (RE) band and none with the spatial resolution offered by UAVs, this finding has the potential of turning UAVs into a local farmer’s favourite aid tool.

USE OF VEGETATION INDICES TO ESTIMATE THE LIVE BIOMASS LOSS IN THE PICUÍ MUNICIPALITY

The desertification or land degradation is a phenomenon that has been occurring in many parts of the planet. This process has been accompanied by climatic factors and socio-ecological, and in the Northeast of Brazil is considered a phenomenon resulting from human intervention over the environment. This work was proposition estimate the amount of live biomass lost in the city of Picuí - PB from the multitemporal analysis of vegetation cover using satellite images. For both GIS and Remote Sensing techniques were used, with the purpose of calculate soil degradation over the past three decades, and calculate the NDVI, SAVI and IAF vegetation index, to estimate the amount of living biomass lost over in the period studied. As a result, it was verified that the city has about 30% of its land in degradation level ranging from grave and very grave, and that the living biomass decreased by about 20% in these past three decades, coming to present about 49% of green area, and about 51% of degraded areas considered, a fact that has been proven through technical visits, which identificated that the areas are degraded by vegetal extraction of wood, mining and agriculture.

Utilisation of Ground and Airborne Optical Sensors for Nitrogen Level Identification and Yield Prediction in Wheat

A healthy crop growth ensures a good biomass development for optimal yield amounts and qualities. This can only be achieved with sufficient knowledge about field conditions. In this study we investigated the performance of optical sensors in large field trails, to predict yield and biomass characteristics. This publication investigated how information fusion can support farming decisions. We present the results of four site-year studies with one fluorescence sensor and two spectrometers mounted on a ground sensor platform, and one spectrometer built into a fixed-wing unmanned aerial vehicle (UAV). The measurements have been carried out in three winter wheat fields (Triticum aestivum L.) with different Nitrogen (N) levels. The sensor raw data have been processed and converted to features (indices and ratios) that correlate with field information and biological parameters. The aerial spectrometer indices showed correlations with the ground truth data only for site-year 2. FERARI (Fluorescence Excitation Ratio Anthocyanin Relative Index) and SFR (Simple Fluorescence Ratio) from the Multiplex® Research fluorometer (MP) in 2012 showed significant correlations with yield (Adj. r 2 ≤ 0.63), and the NDVI (Normalised Difference Vegetation Index) and OSAVI (Optimized Soil-Adjusted Vegetation Index) of the FieldSpec HandHeld sensor (FS) even higher correlations with an Adj. r 2 ≤ 0.67. Concerning the available N (N avail ), the REIP (Red-Edge Inflection Point) and CropSpec indices from the FS sensor had a high correlation (Adj. r 2 ≤ 0.86), while the MP ratio SFR was slightly lower (Adj. r 2 ≤ 0.67). Concerning the biomass weight, the REIP and SAVI indices had an Adj. r 2 ≤ 0.78, and the FERARI and SFR ratios an Adj. r 2 ≤ 0.85. The indices of the HandySpec Field ® spectrometer gave a lower significance level than the FS sensor, and lower correlations (Adj. r 2 ≤ 0.64) over all field measurements. The features of MP and FS sensor have been used to create a feature fusion model. A developed linear model for site-year 4 has been used for evaluating the rest of the data sets. The used model did not correlate on a significant de novo level but by changing only one parameter, it resulted in a significant correlation. The data analysis reveals that by increasing mixed features from different sensors in a model, the higher and more robust the r 2 values became. New advanced algorithms, in combination with existent map overlay approaches, have the potential of complete and weighted decision fusion, to ensure the maximum yield for each specific field condition.

RELATIONSHIP BETWEEN COFFEE CROP PRODUCTIVITY AND VEGETATION INDEXES DERIVED FROM OLI / LANDSAT-8 SENSOR DATA WITH AND WITHOUT TOPOGRAPHIC CORRECTION

ABSTRACT The reflectance values of a coffee crop are influenced by several factors such as planting direction, crop spacing, time of the year, plant age and topography which reduces the accuracy of the estimates derived from remote sensing data. In this context were evaluated the relationships between coffee productivity and values of NDVI, SAVI and NDWI vegetation indexes with and without topographic reflectance correction for different coffee phenological phases for the crop years 2013/2014 (low productivity) and 2014/2015 (high productivity). The evaluations were made through the standard deviation of vegetation indices (VIs), linear relationship between the cosine factor and the VIs and between VIs and coffee productivity. The best phenological phases of coffee to determine productivity from spectral indexes were the stages of dormancy and flowering. The results indicated that the NDVI was the best index to estimate the productivity of coffee trees with coefficient of determination (R2) that ranged from 0.58 to 0.90. There was an increase in R2 between productivity and NDVI with topographic correction in the dormancy phase in the year of low productivity; between productivity and NDVI with topographic correction in the flowering phase in the year of high productivity; and between productivity and SAVI and NDWI with topographic corrections in the flowering phase in the year of high productivity.

ESTIMATING MANGROVE FOREST DENSITY USING GAP FRACTION METHOD AND VEGETATION TRANSFORMATION INDICES APPROACH

Mangrove forest represented a coastal ecosystem in Indonesia. Theoretically, mangrove forest mapping took a lot of cost and long time. The objectives of the study were (1) to examine the acuracy of the estimation of the mangrove forest density using vegetation index transformation, and (2) to map the mangrove forest condition. The location of the study was Alas Purwo Resort Grajagan National Park area. The material of the study was Landsat-8 OLI image recorded on January 19th, 2016 using SAVI vegetation index transformation method. Gap fraction filed measurement method was a new method in Indonesia. The results of the study showed that the regression of the SAVI index between index transformation value and in-field condition (R2) was 0.566, the forest density estimation resulting from the SAVI index transformation had the RMSE of 2.334178 and the density of the mangrove forest in Grajagan Bay of the Alas Purwo National Park included low density of 0-12.5% (30.42 ha), medium density of 12.6-25% (116.55 ha), and high density of 25.1-37.6% (463.68 ha).

Understanding the spatial distribution of eroded areas in the former rural homelands of South Africa: Comparative evidence from two new non-commercial multispectral sensors

In this study, we determine the most suitable multispectral sensor that can accurately detect and map eroded areas from other land cover types in Sekhukhune rural district, Limpopo Province, South Africa. Specifically, the study tested the ability of multi-date (wet and dry season) Landsat 8 OLI and Sentinel-2 MSI images in detecting and mapping eroded areas. The implementation was done, using a robust non-parametric classification ensemble: Discriminant Analysis (DA). Three sets of analysis were applied (Analysis 1: Spectral bands as independent dataset; Analysis 2: Spectral vegetation indices as independent and Analysis 3: Combined spectral bands and spectral vegetation indices). Overall classification accuracies ranging between 80% to 81.90% for MSI and 75.71%–80.95% for OLI were derived for the wet and dry season, respectively. The integration of spectral bands and spectral vegetation indices showed that Sentinel-2 (OA = 83, 81%), slightly performed better than Landsat 8, with 82, 86%. The use of bands and vegetation indices as independent dataset resulted in slightly weaker results for both sensors. Sentinel-2 MSI bands located in the NIR (0.785–0.900 μm), red edge (0.698–0.785 μm) and SWIR (1.565–2.280 μm) regions were selected as the most optimal for discriminating degraded soils from other land cover types. However, for Landsat 8OLI, only the SWIR (1.560–2.300 μm), NIR (0.845–0.885 μm) region were selected as the best regions. Of the eighteen spectral vegetation indices computed, NDVI and SAVI and SAVI and Global Environmental Monitoring Index (GEMI) were ranked selected as the most suitable for detecting and mapping soil erosion. Additionally, SRTM DEM derived information illustrates that for both sensors eroded areas occur on sites that are 600 m and 900 m of altitude with similar trends observed in both dry and wet season maps. Findings of this work emphasize the importance of free and readily available new generation sensors in continuous landscape-scale soil erosion monitoring. Besides, such information can help to identify hotspots and potentially vulnerable areas, as well as aid in developing possible control and mitigation measures.

Analysis of the impact of construction of selected water reservoirs on the surrounding environment made on the basis of satellite data

The authors aimed at showing in the publication the impact of the construction of selected water reservoirs in Poland on the surrounding environment, basing on the satellite imagery. For this purpose, for four test objects, i.e. the Dobczyce Reservoir, the Klimkow Reservoir, the Czorsztyn Reservoir and the Domaniowski Reservoir analyzes were carried out concerning the changes in the structure of land use for the state before and after reservoir construction, and indicators such as NDWI, SAVI and TSAVI were calculated. In the case of the analysis of changes in the land use structure, the direction of these changes was determined first of all, and for SAVI and TSAVI indices, the percentage change in their value and the direction of these changes were calculated.

Combination of lumped hydrological and remote-sensing models to evaluate water resources in a semi-arid high altitude ungauged watershed of Sierra Nevada (Southern Spain)

Assessing water resources in high mountain semi-arid zones is essential to be able to manage and plan the use of these resources downstream where they are used. However, it is not easy to manage an unknown resource, a situation that is common in the vast majority of high mountain hydrological basins. In the present work, the discharge flow in an ungauged basin is estimated using the hydrological parameters of an HBV (Hydrologiska Byråns Vattenbalansavdelning) model calibrated in a “neighboring gauged basin”. The results of the hydrological simulation obtained in terms of average annual discharge are validated using the VI-ETo model. This model relates a simple hydrological balance to the discharge of the basin with the evaporation of the vegetal cover of the soil, and this to the SAVI index, which is obtained remotely by means of satellite images. The results of the modeling for both basins underscore the role of the underground discharge in the total discharge of the hydrological system. This is the result of the deglaciation process suffered by the high mountain areas of the Mediterranean arc. This process increases the infiltration capacity of the terrain, the recharge and therefore the discharge of the aquifers that make up the glacial and periglacial sediments that remain exposed on the surface as witnesses of what was the last glaciation.

Correlations between spectral and biophysical data obtained in canola canopy cultivated in the subtropical region of Brazil

Abstract: The objective of this work was to identify the spectral bands, vegetation indices, and periods of the canola crop season in which the correlation between spectral data and biophysical indicators (total shoot dry matter and grain yield) is most significant. The experiment was carried out during the 2013 and 2014 crop seasons at Embrapa Trigo, in the state of Rio Grande do Sul, Brazil. A randomized complete block design was used, with four replicates, and the treatments consisted of five doses of nitrogen topdressing. Plant dry matter, grain yield, and phenology were measured. The canola spectral response was evaluated by measuring the canola canopy reflectance using a spectroradiometer, and, with this data, the SR, NDVI, EVI, SAVI, and GNDVI vegetation indices were determined. Pearson’s correlations between the spectral and biophysical variables of canola showed that the red (620 to 670 nm) and near-infrared (841 to 876 nm) bands were the best to estimate the dry matter. The vegetative period is the most indicated to obtain the most significant correlations for canola. All the used vegetation indices are adequate for estimating the dry matter and grain yield of canola.

ANÁLISE ESPAÇO-TEMPORAL DA COBERTURA VEGETAL NA ÁREA DE PROTEÇÃO AMBIENTAL DO CARIRI, PARAÍBA, BRASIL - DOI 10.5216/bgg.v36i3.44558

A Caatinga está inserida na região semiárida do Nordeste e caracteriza-se por floras adaptadas que vivem com a semiaridez e o ritmo sazonário do clima e da hidrologia. Apresenta uma longa história de uso e ocupação que tem transformado consideravelmente as coberturas de vegetação natural que ocorrem neste bioma. Na atualidade, com a disponibilidade das tecnologias de sensoriamento remoto é possível avaliar as mudanças espaço-temporais nas diversas paisagens por meio da modelagem de vários parâmetros biofísicos da vegetação. O presente estudo teve como objetivo analisar as mudanças espaço-temporais da cobertura vegetal na Área de Proteção Ambiental (APA) do Cariri por meio dos índices NDVI e SAVI a partir do processamento de imagens do sensor TM Landsat 5 dos anos 1989, 2005 e 2010. Seis classes de cobertura foram encontradas na APA, sendo quatro delas correspondentes com as fitofisionomias de Caatingas com maior e menor nível de antropização. Os resultados mostram um declínio progressivo das Caatingas mais densas no período analisado. O NDVI teve uma resposta mais próxima às condições reais da vegetação da área de estudo. Os índices de vegetação analisados representam um indicador da qualidade ambiental da área para um futuro planejamento ambiental.Palavras-chave: Caatinga, sensoriamento remoto, índices de vegetação.

HOW MUCH CARBON IS STORED IN DESERTS? AN APPROACH FOR THE CHILEAN ATACAMA DESERT USING LANDSAT-8 PRODUCTS

Abstract. The Atacama Desert in northern Chile is known as the driest place on Earth, with an average rainfall of about 15 mm per year. Despite these conditions, it contains a rich variety of flora with hundreds of species characterised by their extraordinary ability to adapt to this extreme environment. These biotic components have a direct link to important ecosystem services, especially those related to carbon storage and sequestration. No quantitative assessment is currently available for these services and the role of the desert in this matter remains unclear. We propose an approach to estimate above-ground biomass (AGB) using Landsat-8 data, which we tested in the Taparacá region, located in the northern section of the desert. To calibrate and validate the models, we used field data from 86 plots and several spectral indexes (NDVI, EVI and SAVI) obtained from the provisional Landsat-8 Surface-reflectance products. We applied randomised branch sampling and allometry principles (non-destructive methods) to collect biomass samples for all plant biological types: wetlands, steppes, shrubs and trees. All samples were dried in an oven until they reached constant weight and the final values were used to extrapolate dry matter content (AGB) to each plot in terms of kg m-2. We used all available scenes from September 2014 to August 2015 to calculate the maximum, minimum and average value for each index in each pixel within this period. For modeling, we used the method based on classification and regression trees called random forest (RF), available in the statistical software R-Project. The explained variance obtained by the RF algorithm was around 80-85%, and it improved when a wetland vector layer was used as the predictive factor in the model to reach the range 85-90%. The mean error was 1.45 kg m-2 of dry matter. The best model was obtained using the maximum and mean values of SAVI and EVI indexes. We were able to estimate total biomass storage of around 8 million tons (~ 4 million tons of C) for the whole region.

HOW MUCH CARBON IS STORED IN DESERTS? AN APPROACH FOR THE CHILEAN ATACAMA DESERT USING LANDSAT-8 PRODUCTS

The Atacama Desert in northern Chile is known as the driest place on Earth, with an average rainfall of about 15 mm per year. Despite these conditions, it contains a rich variety of flora with hundreds of species characterised by their extraordinary ability to adapt to this extreme environment. These biotic components have a direct link to important ecosystem services, especially those related to carbon storage and sequestration. No quantitative assessment is currently available for these services and the role of the desert in this matter remains unclear. We propose an approach to estimate above-ground biomass (AGB) using Landsat-8 data, which we tested in the Taparacá region, located in the northern section of the desert. To calibrate and validate the models, we used field data from 86 plots and several spectral indexes (NDVI, EVI and SAVI) obtained from the provisional Landsat-8 Surface-reflectance products. We applied randomised branch sampling and allometry principles (non-destructive methods) to collect biomass samples for all plant biological types: wetlands, steppes, shrubs and trees. All samples were dried in an oven until they reached constant weight and the final values were used to extrapolate dry matter content (AGB) to each plot in terms of kg m-2. We used all available scenes from September 2014 to August 2015 to calculate the maximum, minimum and average value for each index in each pixel within this period. For modeling, we used the method based on classification and regression trees called random forest (RF), available in the statistical software R-Project. The explained variance obtained by the RF algorithm was around 80-85%, and it improved when a wetland vector layer was used as the predictive factor in the model to reach the range 85-90%. The mean error was 1.45 kg m-2 of dry matter. The best model was obtained using the maximum and mean values of SAVI and EVI indexes. We were able to estimate total biomass storage of around 8 million tons (~ 4 million tons of C) for the whole region.

THE STUDY OF TEMPORAL CHANGES ON LAND COVER/LAND USE PREVAILING IN BAGHDAD GOVERNORATE USING RS & GIS

This study was carried out in order to identify and mapping the temporal changes for land covers in Baghdad province using Remote Sensing and GIS. Three images were used of land sate taken in 1976, 2000 and 2014 the study area. Suppervised classification and SAVI Index were used to identify land cover classes dominated in the study area. The results of supper classification indicated the presence of five land cover classes including water bodies, bare land, urban, low dense vegetation, and dense vegetation classes. There were four classes of land cover when was used SAVI index: water, no vegetation (bare land and urban), low dense vegetation, and dense vegetation. The results showed that Remote Sensing is a very active and useful tool that can be used to detect land core types. The results showed a decline in class of water bodies from 2.8% to 1.5% for 1976 to 1990 while in 2014 increased to 2.1%. class Urban areas increased continuously with time and accounted for 17.6% , 23.5% and 28.2 % for years of study, indicating the existence of the phenomenon of urban encroachment. Bare land areas accounted for 29.3% , 26.8% and 33.5% of stady years, respectively. The class of low dense vegetation decreased from 44.8% to 31.7% and 29.4% for 1976, 1990 and 2014 respectively, while the class-Dense vegetation increased at 1976-1990 from 5.5% to 16.4% and decreased in 2014 to 6.8%. The SAVI had a role in the detection of agriculture and gave results same to the results of super classification. Results indicated that urban land and salinization process can be consider as the most phenomenon which negatively affected on agriculture area.

Yield estimation comparison of oil palm based on plant density coefficient variation index using spot-6 imagery in part of Riau

Oil palm plantations consist of diverse plant density level that influence the appearance of soil surface or commonly in remote sensing terms called as soil background. Choosing the right density coefficient of vegetation transformation can decrease the noise of soil background for estimating oil palm yield. This research aims 1) to examine the accuracy of SPOT-6 to identify the oil palm l plant growth level and to estimate their yield 2) to know the variation of oil palm yield based on SAVI index vegetation using different density coefficient; and 3) to determine the best density coefficient to estimate the yield of oil palm.This research was held in part of Air Molek, Indragiri Hulu Regency, Riau, one of the largest oil palm plantations in Indonesia. This research method utilises SAVI transformation with density coefficient L-0, L-0.5, and L-1, and regression statistics analysis. The land-cover primary data is derived from SPOT-6 imagery archived in 13rd June 2013. The field survey was conducted in the same month of image's acquisition time and 120 sample areas were taken during that time. Two steps of regression analyses were applied to see the correlation between, first, vegetation index value and oil palm plant; and second, oil palm plant, vegetation index values, and oil palm yield from field observation. These steps produced a model to estimate the oil palm yield based on the index values of yield and vegetation, and the productivity estimation. The result shows that SPOT-6 imagery has 96% accuracy level which is considered high for identifying the oil palm variation. The R value for L-0 density coefficient is 0.8, for L-5 is 0.81 whereas for L-1 is 0.82. The best plant's density coefficient for estimating oil palm yield/yield is L-0 with yield estimation accuracy of 83.33%.

Mapping of soil sealing by vegetation indexes and built-up index: A case study in Madrid (Spain)

This study analyzes the most suitable methods for detecting and measuring the soil sealing change caused by urban growth in the city of Madrid, Spain, using remote sensing techniques and GIS. Some irreversible, negative effects of soil sealing are impermeable ground and modified ecosystems (bio-geochemical cycle, water cycles, loss of biodiversity, etc.), which alter matter and energy flows and the ecological functions of the soil. Urban development has often been more concerned with viability and financial profit, to the detriment of the environment. Multi-temporal studies on the processes are assisted by several types of technologies providing geographical information, together with large cartography, photogrammetric and satellite databases. The best soil sealing detection is carried out based on principal component analysis of the images and the SAVI index. Supervised classifications show that from 1984 to 2013 soil sealing in the municipality of Madrid and its surroundings has risen from almost 15% to 24%, an increase of 9%.

AGRICULTURAL DROUGHT ASSESSMENT OF POST MONSOON SEASON OF VAIJAPUR TALUKA USING LANDSAT 8

Drought is least understood natural phenomena and it has a direct impact on livelihood. The government bodies spend time and money for drought survey, but this process is a time consuming and challenging. Advantage of Geospatial Technology is that, it is helpful to understand the drought prone area and its severity level through satellite images. This research study was focused on Vaijapur taluka which is located at latitude of 19°40’ to 20°15’ north and longitude of 74°35’ to 75°00’ which comes under the scanty rainfall region. The annual rainfall of the region is 502.6mm which is below than an average of 750mm, so that it leads to the agriculture drought. In this study, Landsat 8 images of the year 2013 and 2014 were used for drought assessment in the post monsoon season. Landsat 8 has Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) on board which provides 11 band data of the earth surface. The post monsoon season starts from October to December in India so that we have compared drought indices of two years. To do so OLI and TIRS images of Landsat 8 for the year 2013 and 2014 and ancillary data such as rainfall, temperature, sown area statistics have been used. The analysis of vegetation cover using NDVI, VCI, and SAVI indices demonstrate the impact of the post monsoon rainfall on agriculture field. According to the research study year 2013 was affected by agricultural drought.