Soil Adjusted Vegetation Index Values Research Articles

Soil Adjusted Vegetation Index, Normalize Difference Buildup Index, and Land Surface Temperature between 1987 and 2023 in Abuja Municipal Area Council, Nigeria

Abuja Municipal Area Council (AMAC) is experiencing rapid urban expansion, which is expected to impact land surface temperatures (LST). This paper evaluates the trends in soil adjusted vegetation index (SAVI), normalize difference buildup index (NDBI), and land surface temperature (LST) between 1987 and 2023 in AMAC using Landsat 4 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery, respectively. Results show that in 1987, SAVI ranged from -0.126 to 0.477, NDBI from -0.186 to 0.678, and LST from 27.18 to 46.4 oC. In 2023, SAVI ranged from -0.253 to 0.71, NDBI from -0.308 to 0.619, and LST from 23.89 to 46.57 oC. Analysis showed an increase in vegetation in 2023 compared to 1987. Built-up and bareland areas became more concentrated in the northeast in 2023 compared to 1987, and temperature reductions were observed in areas with increased vegetation, notably in the south and southwest. Correlation analysis indicated a strong negative relationship (-0.772) between SAVI and LST in 1987, weakening in 2023 (-0.389). NDBI and LST remained moderately positively correlated (0.645 in 1987, 0.621 in 2023). Significant differences (P<0.01) were observed between 1987 and 2023 SAVI, NDBI, and LST values. These findings have important implications for environmental monitoring, and urban planning in rapidly urbanizing areas such as AMAC

Satellite based estimation of land cover change and soil degradation in Urgamal, Durvoljin, Erdenekhairkhan soums of Zavkhan province

In this research, climate change over the last 22 years (2000-2022) in Durvoljin, Ugramal and Erdenekhairkhan Sum areas of Zavkhan province was determined and desertification status was assessed using satellite data. In this research, the soil-adjusted vegetation index (SAVI) was calculated among the vegetation indi-ces. When SAVI is calculated using data from the red and NIR channels of the MODIS satellite data, SAVI values vary between -0.4 and +0.5 as a result of im-age processing. The spatial heterogeneity of the land surface of the study area was mapped by SAVI values from -0.4 to +0.02 for water, +0.02 to +0.08 for gravel and rock, +0.08 to +0.11 for sand, +0.11 to +0.16 for sparse soil, +0.16 from - to +0.23 with sparse vegetation, and between +0.23 and +0.5 with good vegetation in 6 categories. As a result of the study, SAVI proved to be more suitable for determining soil types in desert and semi-desert areas.

Detection of Vegetation Cover Changes from 1988-2022 in the University of Baghdad Campus by Remote Sensing and GIS Techniques (Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI)

This study aims to investigate changes in vegetation cover of the University of Baghdad campus by Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) as a tool to determine the changes in vegetation cover on the University landscape were used for the period 1988 and 2022 for two seasons (Winter and Summer). Land cover was divided into four categories: Barren & buildings, Low, Medium, and Dense vegetation. The results showed an increase in NDVI values in winter (-0.2-0.5), and (-0.1-0.9) in 1988, and 2022 respectively, and recorded in summer (-0.3-0.5) and (-0.04-0.4) in 1988, and 2022 respectively. The highest value was recorded in winter of 2022. Barren & buildings recorded the highest value 1470630 Km2 with 43% in summer of 1988 while in 2022 recorded 1411156 Km2 with 40%, for both seasons. The Low vegetation recorded the highest value 130031 Km2 with 377% in winter 1988 and the lowest was recorded at 941671 Km2 with 28% in summer 2022. Medium vegetation was recorded the highest 787580 Km2 with 22% in winter 2022 and lowest 667730 Km2 with 19 %. Dense vegetation was the highest 413614 Km2 with 12 % in summer 2022 while the lowest was recorded 211763 Km2 with 6 % in winter 1988. SAVI values were recorded in winter (-0.3-0.7), (-0.1-0.4) in 1988 and 2022 respectively and in summer (-0.4 - 0.7)(-0.06 - 0.6) in 1988 and 2022 respectively. The results showed slight differences from NDVI values of vegetation cover (Dense). The integration of remote sensing and geographic information systems is an effective and sustainable tool for studying land cover changes in order to obtain reliable and accurate rapid and economical results.

A linear approach for wheat yield prediction by using different spectral vegetation indices

Yield prediction before harvest is one of the important issues in terms of managing agricultural policies and making the right decisions for the future. Using remote sensing techniques in yield estimation studies is one of the important steps for many countries to reach their 21st-century agricultural targets. The aim of this study is to develop a wheat yield model using Landsat-8 and Sentinel-2 satellite data. In this study, the development stages of winter wheat were examined with the help of satellite images obtained between the years 2015-2018 of a selected region in Sanliurfa, Turkey, and it was aimed to predict the yields for other years by establishing a yield estimation model. The yield estimation model was established with the help of Normalized Difference Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI) and Modified Soil-adjusted Vegetation Index (MSAVI) obtained from remote sensing satellite images. Linear regression analysis was established between calculated NDVI, SAVI, GNDVI, MSAVI indices, and actual yield values on the pre-flowering, flowering stage, and post-flowering stage. As a result of the study, the highest correlation coefficient was found in the flowering stage between the vegetation indices values and the actual yield values. The values of NDVI, SAVI, GNDVI, and MSAVI and correlation coefficients are obtained in the flowering stage were 0.82, 0.80, 0.86, and 0.87, respectively. With the established model, yield values in 2019 were tried to be estimated for three different fields. The highest correlations were seen in the flowering stage for MSAVI and GNDVI, pre-flowering stage for NDVI and post-flowering stage for SAVI. This clearly shows that the satellite images can be used in yield estimation studies with a remarkable correlation between vegetation indices and actual yield values.

Determination of the Relationship between Rice Suitability Classes and Satellite Images with Different Time Series for Yeşil Küre Farm Lands

In this study, rice land designated for agricultural land suitability indices belonging to the enterprise Yeşil Küre Farm Land with different time series Sentinel-2A satellite images calculated utilizing spectral vegetation index, which are Normalized Difference Vegetation Index and Red Edge Optimized Soil Adjusted Vegetation Index values by statistical comparison of the relationship between rice for monitoring and estimation of potential productivity is presented a different perspective. Firstly, according to the rice suitability assessment for the study area, the area of 5488.9 ha was determined to be suitable for rice cultivation at the S1 and S2 levels, whereas the area of 588.9 ha was determined to be unsuitable. In this study, it was determined that the most successful results for each land conformity class were obtained using the NDVI. In particular, it was determined that August received the highest r2 value (NDVI; 0.8580 and RE-OSAVI; 0.8465) in both vegetation index models at the S1 level, and on the other hand, a higher r2 value was obtained with NDVI.

Crop Monitoring of Paddy Field Using Landsat 8 OLI

There are times where paddy farmers faced crop yield production that does not meet their expectations although the crop has been well-tended. Especially when the growing and harvest seasons, farmers will be able estimate on cutting some production costs while increasing crop yields with crop yield monitoring and mapping if they could measure the different vegetitation indices of crop field. Therefore, to test the efficiency of this study, the crop health was monitored using vegetation index values obtained from multispectral remote sensing images, such as the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). The objectives were to estimate the NDVI and SAVI values of a paddy field in Sabak Bernam, Selangor and create a crop health map using the calculated NDVI and SAVI during its resting period (January, February, July, August) and growing up period (April) in 2020. This study used Landsat 8 OLI images at a resolution of 30 meters. The data was then processed using Erdas Imagine and ArcGIS Pro to estimate the NDVI and SAVI values, as well as produce crop health maps. As a result, the estimated NDVI and SAVI crop health maps were displayed. In comparison to NDVI, SAVI estimates a greater vegetation index as the value of adjustment factor (L) applied in the SAVI can reduce the soil noise compared to the NDVI. Furthermore, the computation of planted areas revealed that some changes had occurred. The planted area decreased in a specific month, indicating the harvesting season. 

Spatiotemporal characterization of land cover and degradation in the agreste region of Pernambuco, Brazil, using cloud geoprocessing on Google Earth Engine

The characterization of land use and land cover (LULC), as well as the identification of its degradation factors, are necessary for the preservation of the agroecosystem and help in decision making. Therefore, the objective of this research was to evaluate the spatial-temporal dynamics of vegetation cover and soil degradation in the Agreste mesoregion of the state of Pernambuco, through the leaf area index (LAI), soil-adjusted vegetation index (SAVI), and pasture vegetation cover index (PVC) from Sentinel-2/MSI images from the Multispectral Instrument (MSI) sensor and, develop software for automatic cloud processing on the Google Earth Engine (GEE) platform. Were used 8 Sentinel-2/MSI satellite images (2017–2020), processed in GEE, to obtain the SAVI, LAI, and PVC. A pluviometric survey was carried out for the month before the registration of each image, from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The images were subjected to descriptive statistics and principal component analysis (PCA). The months that comprise the highest rainfall exceeded the accumulated 100 mm. The SAVI values ranged from −0.2 to 0.80, of the LAI between 0 and 3 m2 m−2 (with small regions greater than 3). Regions of the low spectral response of LAI and SAVI, correlated with areas of low rainfall in the study region. The total variance showed 100% representativeness and significance of the data for the accumulated in PC2, this denotes how significant the correlations between SAVI, LAI, and PVC are. Because of the new climatic and environmental scenarios, this study presented a relevant contribution to characterizing the potential areas in soil degradation processes, mainly in the Caatinga biome. Between the years 2017–2020, there was the occurrence of soil degradation processes in the Agreste region of Pernambuco, where the state's dairy basin is located, caused by poor management, use of arable land, and long periods of drought.

Monitoring Onion Crop “Cipolla Rossa di Tropea Calabria IGP” Growth and Yield Response to Varying Nitrogen Fertilizer Application Rates Using UAV Imagery

Remote sensing (RS) platforms such as unmanned aerial vehicles (UAVs) represent an essential source of information in precision agriculture (PA) as they are able to provide images on a daily basis and at a very high resolution. In this framework, this study aims to identify the optimal level of nitrogen (N)-based nutrients for improved productivity in an onion field of “Cipolla Rossa di Tropea” (Tropea red onion). Following an experiment that involved the arrangement of nine plots in the onion field in a randomized complete block design (RCBD), with three replications, three different levels of N fertilization were compared: N150 (150 kg N ha−1), N180 (180 kg N ha−1), and e N210 (210 kg N ha−1). The crop cycle was monitored using multispectral (MS) UAV imagery, producing vigor maps and taking into account the yield of data. The soil-adjusted vegetation index (SAVI) was used to monitor the vigor of the crop. In addition, the coverage’s class onion was spatially identified using geographical object-based image classification (GEOBIA), observing differences in SAVI values obtained in plots subjected to differentiated N fertilizer treatment. The information retrieved from the analysis of soil properties (electrical conductivity, ammonium and nitrate nitrogen), yield performance and mean SAVI index data from each field plot showed significant relationships between the different indicators investigated. A higher onion yield was evident in plot N180, in which SAVI values were higher based on the production data.

PEMANFAATAN INDEKS VEGETASI UNTUK ESTIMASI KANDUNGAN KALIUM PADA TANAMAN NANAS (Ananas comosus) MENGGUNAKAN UAV (Unmanned Aerial Vehicle) DI PT. GREAT GIANT PINEAPPLE, LAMPUNG

PT Great Giant Pineapple (PT. GGP) is the largest pineapple production company in Indonesia. One of the nutrients that pineapple plants really need is potassium (K). K plays a key role in carbohydrate metabolism and transport of photosynthates from source to sink. Remote sensing technology has been developed to estimate nutrient status, one of which is using an Unmanned Aerial Vehicle (UAV). This study aims to estimate the K nutrient content in pineapple plants using vegetation indexes in the form of NDVI (Normalyzed Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and OSAVI (Optimized of Soil Adjusted Vegetation Index). The research was carried out by taking aerial photographs and samples of pineapple plants in the 5 months phase before forcing up to 2 months after forcing (F-5 to F + 2), laboratory analysis, statistical analysis, and making distribution maps. The results showed that the relationship between the vegetation index value and K plant was the strongest and most significant is in 1 month before forcing phase (F-1) with the same r value for the three indices vegetation (r=0.867). The results of the regression analysis between the NDVI, SAVI and OSAVI values with K plant were 75.17%, 75.18% and 75.17%, respectively. The calculation of the K estimate using three methods yields no different values. The validation results using paired t test (t count -0.63; t table 2.31; p-value 0.544) where the K content in the measured plants and the estimation results showed no significant difference with the measurement results.

Estudio multiespectral del cultivo de tuna para determinar los índices NDVI , CWSI y SAVI , a partir de imágenes SENTINEL 2A, en el cantón Guano, provincia de Chimborazo, Ecuador

En este trabajo se evalúan: el Índice de Vegetación Diferencial Normalizado (NDVI, por sus siglas en inglés), el Índice de Vegetación Ajustado al Suelo (SAVI, por sus siglas en inglés) y el Índice de estrés hídrico (CWSI, por sus siglas en inglés) en los cultivos de tuna, ubicados en el cantón Guano de la provincia de Chimborazo, Ecuador. El área de interés (AOI, por sus siglas en inglés) son las parroquias San Andrés y Guano, en las cuales se determinó la textura del suelo con los datos existentes en el Sistema Nacional de Información (SNI) del Ecuador, para ello se utilizó una imagen satelital de Sentinel 2A del 16 de junio de 2018; y mediante operaciones de funciones raster se obtuvo una media aproximada y referencial de NDVI, la reflectancia recibida tanto del suelo como del cultivo de tuna (SAVI) y la transpiración que corresponde al estrés hídrico (CSWI) de las parcelas de tuna. Finalmente, se obtuvieron datos aproximados y referenciales de NDVI con un valor de 0.31, valores de SAVI que reflejan datos del suelo y del cultivo y un CWSI muy bajo que determinan que la transpiración es mínima, por lo que se puede concluir que una de las posibles técnicas más adecuadas para incrementar la producción de la tuna es la de riego por goteo.

Field phenotyping using multispectral imaging in pea (Pisum sativum L) and chickpea (Cicer arietinum L)

Pea (Pisum sativum L) and chickpea (Cicer arietinum L) are important grain legumes grown in the Palouse region of the Pacific Northwest United States. The USDA-ARS grain legume breeding program in this region focuses on developing pea and chickpea varieties with high yield potential, resistance to biotic and abiotic stresses, and superior agronomic characteristics. In this study, aerial high resolution multispectral imaging was evaluated to phenotype yield potential differences among genotypes in green pea, yellow pea and chickpea. Five experiments (three field pea and two chickpea) with 10–25 varieties grown at two locations (Pullman, Washington; Genesee, Idaho) were assessed. Images were acquired approximately 60, 70 and 90 days after planting (DAP) at 110 m above ground level. Normalized difference vegetation index (NDVI), green normalized difference vegetation index, soil adjusted vegetation index (SAVI) and simple ratio (SR) image based features (SUM, MIN, MAX, MEAN) were extracted. In most cases, the MEAN NDVI data was found to be consistently correlated with dry seed yield (p < 0.05), with green pea genotypes showing strongest relationship (r = 0.64–0.93 at about 70 DAP, both during “plot-by-plot” and “by genotype” comparisons). The MEAN SAVI and SR values were also strongly correlated with yield at 61–72 DAP in most of the pea experiments. The data collected during flowering and early pod development phenological growth stages was found to be useful in yield estimation. The developed methods can be used for early generation evaluation in breeding programs, where yield cannot be estimated due to limited seed availability.

Satellite image fusion to detect changing surface permeability and emerging urban heat islands in a fast-growing city.

Rapid and extensive urbanization has adversely impacted humans and ecological entities in the recent decades through a decrease in surface permeability and the emergence of Urban Heat Islands (UHI). While detailed and continuous assessments of surface permeability and UHI are crucial for urban planning and management of landuse zones, they mostly involve time consuming and expensive field studies and single sensor derived large scale aerial and satellite imageries. We demonstrated the advantage of fusing imageries from multiple sensors for landuse and landcover (LULC) change assessments as well as for assessing surface permeability and temperature and UHI emergence in a fast growing city, i.e. Tirunelveli, Tamilnadu, India. IRS-LISSIII and Landsat-7 ETM+ imageries were fused for 2007 and 2017, and classified using a Rotation Forest (RF) algorithm. Surface permeability and temperature were then quantified using Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST) index, respectively. Finally, we assessed the relationship between SAVI and LST for entire Tirunelveli as well as for each LULC zone, and also detected UHI emergence hot spots using a SAVI-LST combined metric. Our fused images exhibited higher classification accuracies, i.e. overall kappa coefficient values, than non-fused images. We observed an overall increase in the coverage of urban (dry, real estate plots and built-up) areas, while a decrease for vegetated (cropland and forest) areas in Tirunelveli between 2007 and 2017. The SAVI values indicated an extensive decrease in surface permeability for Tirunelveli overall and also for almost all LULC zones. The LST values showed an overall increase of surface temperature in Tirunelveli with the highest increase for urban built-up areas between 2007 and 2017. LST also exhibited a strong negative association with SAVI. Southeastern built-up areas in Tirunelveli were depicted as a potential UHI hotspot, with a caution for the Western riparian zone for UHI emergence in 2017. Our results provide important metrics for surface permeability, temperature and UHI monitoring, and inform urban and zonal planning authorities about the advantages of satellite image fusion.

Effects of Different Grazing Systems on Aboveground Biomass and Plant Species Dominance in Typical Chinese and Mongolian Steppes

This study investigated the effects of different grazing systems on plant communities, and examined the causes of Mongolian grassland desertification. The typical steppes near the Chinese-Mongolian border were studied using quadrat sampling and remote sensing methods. Aboveground biomass in the steppe areas differed significantly among the three grazing systems (p &lt; 0.05): Biomass in the grazing-prohibited areas (455.9 g) was greater than that in the rotational-grazing areas (268.4 g) and the continuous grazing areas (122.2 g). Aboveground biomass was well correlated with the Soil Adjusted Vegetation Index (SAVI; y = 5600x2 + 260x + 110; R2 = 0.67; p &lt; 0.05). The relative mean deviation between the aboveground biomass was calculated using this regression and the measured biomass was 29.1%. The Soil Adjusted Vegetation Index (SAVI) values for nomadic-grazing areas were greater than those for continuous-grazing areas in 1989, 2005, 2011, and 2016, and were significantly greater in 2011 and 2016. The SAVI values for the continuous-grazing areas were slightly, but not significantly greater, than those for the nomadic-grazing areas in 1993. Plant species that dominated in moderately degraded areas were most dominant in nomadic-grazing areas, followed by continuous-grazing areas and grazing-prohibited areas. Plant species that dominated in lightly and heavily degraded areas were most dominant in continuous-grazing areas, followed by nomadic-grazing areas and grazing-prohibited areas. Generally, continuous grazing caused more serious grassland degradation than did nomadic grazing, and nomadic-grazing areas tolerated more intense grazing than did continuous-grazing areas.

Assessment of environmental impact of solid waste dumpsites using remote sensing

This study presents a remote sensing approach of using freely available Landsat 8 satellite Indicators ( Land Surface Temperature ( LST ), Soil Adjusted Vegetation Index ( SAVI )) and Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) geospatial data to assess the impact of dumpsites on the environment in Benin City, Nigeria. The finding reveals that the average derived LST at the dumpsites were higher than the immediate surrounding, and the average SAVI values were lower than the immediate surrounding. The high values of LST at the dumpsites depict the effect of gases released because of decomposition activities, while low values of SAVI indicate vegetation response to soil and ground water contamination due to leachate infiltration. The average elevation within the dumpsite area derived from SRTM DEM was also applied as a proxy to estimate disposed waste quantity, and related closely with LST that depict biodegradation activities. The result presented here shows that bacterial and fungal counts correlate strongly with the LST and SAVI values at each of the dumpsites R 2 : LST vs Bacteria Count = 0.982, LST vs Fungi Count = 0.951; SAVI vs Bacteria Count = 0.745, SAVI vs Fungi Count = 0.664, thereby suggesting remote sensing can be applied to aid longterm dumpsite monitoring and management. http://dx.doi.org/10.4314/njt.v37i1.36

Reflectance-based crop coefficients REDUX: For operational evapotranspiration estimates in the age of high producing hybrid varieties

Methodologies based on earth observation remote sensing allow for a precise estimation of actual water requirements for irrigated crops across large areas. In spite of the many number of experiments using or analyzing the relationship between the basal crop coefficient (Kcb) and the soil adjusted vegetation index (SAVI) for maize, the development of new maize hybrid varieties with modifications related to canopy architecture suggest a possible change of the leaf area index (LAI) for maximum Kcb and its relationship with the SAVI or other vegetation indices. In addition, we noted a lack of analysis of these relationships for cultivated soybean. The objective of this paper is to analyze the Kcb, SAVI and LAI relationships in maize and soybean based on the non-linear relationships proposed by Choudhury et al. (1994). In addition, we propose a modification of the Choudhury et al. (1994) approach based on field-based experimental evidence of a minimum Kcb greater than 0. For sites with limited field data, we also analyze the utility of a simple linear regression based on the Kcb and SAVI values for bare soil and maximum Kcb values. The resulting Kcb-SAVI relationships are assimilated into a remote sensing based soil water balance model. The results of the model are analyzed in terms of irrigation requirements and crop evapotranspiration (ETa) for 11 growing seasons in two fields cultivated with irrigated and rain-fed maize and soybean in eastern Nebraska. Comparisons of measured and modelled ETa values indicate a good agreement, with RMSE lower than 0.7mmd−1 for weekly averaged values. The comparison of actual irrigation applied and irrigation requirements indicate the central pivot systems could not supply adequate water in some growing seasons with higher demands.

ASSESSMENT OF FIRE SEVERITY AND POST-FIRE REGENERATION BASED ON TOPOGRAPHICAL FEATURES USING MULTITEMPORAL LANDSAT IMAGERY: A CASE STUDY in MERSIN, TURKEY

Satellite based remote sensing technologies and Geographical Information Systems (GIS) present operable and cost-effective solutions for mapping fires and observing post-fire regeneration. Mersin-Gülnar wildfire, which occurred in August 2008 in Turkey, selected as study site. The fire was devastating and continued 55 days. According to Turkish General Directorate of Forestry reports, it caused two deaths and left hundreds of people homeless. The aim of this study is to determine the fire severity and monitor vegetation recovery with using multitemporal spectral indices together with topographical factors. Pre-fire and post-fire Landsat ETM+ images were obtained to assess the related fire severity with using the widely-used differenced Normalized Burn Ratio (dNBR) algorithm. Also, the Normalized Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to determine vegetation regeneration dynamics for a period of six consecutive years. In addition, aspect image derived from Aster Global Digital Elevation Model (GDEM) were used to determine vegetation regeneration regime of the study area. Results showed that 5388 ha of area burned with moderate to high severity damage. As expected, NDVI and SAVI values distinctly declined post-fire and then began to increase in the coming years. Mean NDVI value of burned area changed from 0.48 to 0.17 due to wildfire, whilst mean SAVI value changed from 0.61 to 0.26. Re-growth rates calculated for NDVI and SAVI 57% and 63% respectively, six years after the fire. Moreover, NDVI and SAVI were estimated six consecutive year period by taking into consideration east, south, north and west facing slopes. Analysis showed that north-facing and east-facing slopes have higher regeneration rates in compared to other aspects. This study serves as a window to an understanding of the process of fire severity and vegetation regeneration that is vital in wildfire management systems.

ASSESSMENT OF FIRE SEVERITY AND POST-FIRE REGENERATION BASED ON TOPOGRAPHICAL FEATURES USING MULTITEMPORAL LANDSAT IMAGERY: A CASE STUDY in MERSIN, TURKEY

Abstract. Satellite based remote sensing technologies and Geographical Information Systems (GIS) present operable and cost-effective solutions for mapping fires and observing post-fire regeneration. Mersin-Gülnar wildfire, which occurred in August 2008 in Turkey, selected as study site. The fire was devastating and continued 55 days. According to Turkish General Directorate of Forestry reports, it caused two deaths and left hundreds of people homeless. The aim of this study is to determine the fire severity and monitor vegetation recovery with using multitemporal spectral indices together with topographical factors. Pre-fire and post-fire Landsat ETM+ images were obtained to assess the related fire severity with using the widely-used differenced Normalized Burn Ratio (dNBR) algorithm. Also, the Normalized Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to determine vegetation regeneration dynamics for a period of six consecutive years. In addition, aspect image derived from Aster Global Digital Elevation Model (GDEM) were used to determine vegetation regeneration regime of the study area. Results showed that 5388 ha of area burned with moderate to high severity damage. As expected, NDVI and SAVI values distinctly declined post-fire and then began to increase in the coming years. Mean NDVI value of burned area changed from 0.48 to 0.17 due to wildfire, whilst mean SAVI value changed from 0.61 to 0.26. Re-growth rates calculated for NDVI and SAVI 57% and 63% respectively, six years after the fire. Moreover, NDVI and SAVI were estimated six consecutive year period by taking into consideration east, south, north and west facing slopes. Analysis showed that north-facing and east-facing slopes have higher regeneration rates in compared to other aspects. This study serves as a window to an understanding of the process of fire severity and vegetation regeneration that is vital in wildfire management systems.

Resilience of a novel ecosystem after the loss of a keystone species: plague epizootics and urban prairie dog management

In a complex urban‐impacted landscape, native black‐tailed prairie dogs (Cynomys ludovicianus) amplify the trajectory at which grassland plant communities deviate from historical configurations. Prairie dog removal has been proposed as an intervention method based upon the premise that removing a major directional driver of change will initiate the recovery of historically common plant communities. However, in a heavily anthropogenically influenced landscape with a matrix containing only small fragmented areas of native vegetation, the recolonization speed and success of native plant species may not match those observed in less anthropogenically influenced landscapes. This study examined the effect of urban prairie dog removal by using remote sensing to observe the response of plant communities near Boulder, Colorado, USA to plague epizootics. We used Mann‐Whitney U tests to compare the soil adjusted vegetation index (SAVI) values from colonies recently extirpated by plague to both areas unoccupied by prairie dogs and to plague‐absent colonies. Analysis of 67 Landsat images in three growing season subsets suggested that prairie dog removal alone does not return colony plant communities to compositions representative of grassland areas unoccupied by prairie dogs. The absence of SAVI value changes in the mid‐ and late‐growing seasons suggested that novel vegetation communities on urban prairie dog colonies were highly resilient systems, and prairie dog removal alone was insufficient for restoration. Furthermore, increased early season SAVI values on extirpated colonies could indicate a proliferation of introduced winter active species and exotic forbs, not the desired reemergence of native species, but rather species expected given current climatic changes. Intensive management efforts appear necessary for overcoming the thresholds required to restore urban prairie dog colonies to their historical compositions, an effort made increasingly more difficult with the ongoing effects of other global change drivers.

Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

Assessment of spatio-temporal dynamics of soil erosional severity through geoinformatics

The present study makes an attempt to determine the effects of the change in the landuse and landcovers during 14 years (1996–2010) on the magnitude of the soil erosional severity on microwatershed basis in an agricultural watershed located around a rapidly urbanizing city Ranchi, the capital of Jharkhand state, India. The tasks comprise three steps: categorization of the I order microwatersheds into five zones of erosional severity based on the Universal Soil Loss Equation derived soil erosion for the respective years, determination of the temporal change in thenumber and area of the microwatersheds and of the temporal change in thelanduse and landcovers falling in the respective severity zones. The crop management factor (C) was estimated using the Transformed Soil-Adjusted Vegetation Index values derived from the respective years' satellite data. The investigation revealed significant change in the spatio-temporal pattern and magnitude of the erosional severity zones vis-à-vis the change in the landuse and landcovers.