Mean NDVI Values Research Articles

Early Detection of Potential Infestation by Capnodis tenebrionis (L.) (Coleoptera: Buprestidae), in Stone and Pome Fruit Orchards, Using Multispectral Data from a UAV

Wood-boring insect pests pose a significant threat to orchards, potentially leading to tree mortality. In the initial stages of infestation, no visible symptoms are apparent, but as infestations progress, rapid and widespread symptoms emerge, resulting in accelerated tree decline. Therefore, the timely detection of early wood-boring insect symptoms is critical for effective pest control, necessitating advanced methods such as remote sensing. In this study, remote sensing is utilized to identify the early symptoms of peach flatheaded root borer (PFRB) infestation in trees. A multispectral sensor attached to a UAV captures aerial imagery data from stone fruit and pome fruit orchards. These data undergo processing in photogrammetric and GIS programs, where NDVI, NDRE, and the tree crown area are computed. On-site observations confirm PFRB infestations. Various machine-learning models, including logistic regression (LR), artificial neural network (NN), random forest (RF), and extreme gradient boosting (XGBoost), are compared using mean NDVI values, mean NDRE values, crown area, mean temperature, and mean relative humidity. Mean NDVI values emerge as the most crucial factor for predicting PFRB infestation across all machine-learning models. The XGBoost model proves the most effective, achieving an accuracy of 0.85, with marginal variations from the other tested models.

Contemporary Dynamics in Vegetation of the Polar and Subpolar Regions of Western Siberia Using Remote Sensing Data

Abstract—Trends in the climatogenic dynamics of phytomass on the sites located along the borders of Western Siberia’s natural zones (the border of frozen and unfrozen bogs, the border of taiga and open boreal woodlands, and the northern boundary of the distribution of woody vegetation) were spotted based on the vegetation index NDVI derived using the sensors of the MODIS-Terra satellite for the period 2000–2020. The assessment of the vegetation dynamics and the factors that determine it was conducted with the use of the mean and maximum NDVI values and the relation of the index with basic climatic variables. A positive trend of NDVI caused by an increase in air temperature and precipitation was revealed in all the studied areas. The phytomass of open boreal larch woodlands grew the most strongly, and the greatest interannual variability was noted there. A correlation analysis conducted on the basis of the NDVI mean values displayed that improvements in vegetation growth conditions in tundra are mainly caused by the impact of the average summer temperature. The increase in phytomass in taiga is mostly connected with the presence of a tree stand that better retains snow cover, which contributes to less freezing of the ground during the cold season.

Spectral library of crops and discrimination of major vegetables grown in the eastern Himalayan ecosystem: A proximal hyperspectral remote sensing approach

Identifying, characterising and mapping, unique vegetable crops grown on small mixed lands in the eastern Himalayan mountain ecosystem (EHME) using traditional methods is a challenge. The exploration of high-resolution, multispectral optical satellite data for vegetable mapping is hindered by heavy rainfall, clouds, and year-round fog/snow cover. Hyperspectral imaging is restricted in the region that borders several international countries because of security concerns. In the global literature on spectroscopy, information on vegetable crops cultivated in the Himalayan ecosystem is marginal or never covered. Thus, we used a portable Field Spec Handheld2 (ASD) spectroradiometer for spectral characterization of 28 important vegetables, from 10 different families. During the summer and winter seasons of 2020–2022, they were grown without stress and in good health in the experimental field at Umiam, Meghalaya. The ground radiometer was able to achieve a spectral resolution of <3 nm in the 325 to 1075 nm wavelength range. At critical growth stages, data were acquired on cloudless days in 1100 and 1300 nm, with a height of 1 m above the crop and a field of view (FOV) <25°. Crop-specific sensitive wavebands were identified and estimated 12 vegetation indices (VI) that relate to biophysical and biochemical properties for crop discrimination. The near infrared region (NIR: 700–1070 nm) was effective for crop discrimination. Of the 10 families, the mean NDVI values varied in the following descending order: Umbelliferae(0.878) > Malvaceae(0.863) > Cucurbitaceae(0.802) > Lamiaceae(0.732) > Solanaceae(0.721) > Leguminosae(0.651) > Cruciferae (0.607) > Amarantha ceae(0.604) > Zingiberaceae(0.595) > Liliaceae(0.531). Among vegetables, the highest NDVI was reported for Fenugreek (0.922) and the lowest for Broccoli (0.421). The position on the red edge (700-750 nm) varied greatly. Similarly, crop families had varying VI values for chlorophyll, carotenoid, xanthophyll and anthocyanin pigments. The ranges of these indices were defined as healthy or stressed crops for the region. From the multi-season spectral signature, a spectral crop library (CSL) for all vegetables was developed under clearly defined conditions (e.g. growth stage, time and year, soil, cultivar type, and climate). The CSL generated from the study provides a reference spectral source for spatial mapping and quantitative spectral modelling to assess crop conditions and prescribe measures. This will be a crucial step in promoting precision agriculture in the eastern Himalayas and other similar ecosystems.

Can land surface phenology from Sentinel-2 time-series be used as an indicator of Macaronesian ecosystem dynamics?

Land surface phenology (LSP), the study of phenological patterns of vegetation using vegetation index (VI) time-series derived from multi-spectral satellite imagery, has helped to improve the understanding of the seasonal dynamics of terrestrial ecosystems from local to global scale. High spatial resolution satellite observations have emerged as a new possibility for monitoring the seasonal dynamics of heterogeneous ecosystems. Under-studied Macaronesian ecosystems have specific characteristics that make LSP estimation a complex task (e. g., frequent cloud cover, frequent presence of atmospheric aerosols, different degrees of vegetation density, diversity of plant species, landscape heterogeneity). Thus, this study aims to analyse the potential of LSP based on Sentinel-2 data to monitor the phenological dynamics of vegetation in the Macaronesian ecosystems of Canary Islands (Spain). NDVI time-series were generated using Sentinel-2 data from January 2018 to December 2021. NDVI time-series were smoothed using double logistic function. Three phenometrics, such as the start of the growing season (SOS), the end of the growing season (EOS) and the length of the growing season (LOS), were extracted using a threshold-based method (20%) only for pixels where the mean NDVI value of the smoothed four-year time-series was higher than 0.2. Growing season of the major of Macaronesian vegetation started between late summer and late autumn (start of the wet season) and ended between early spring and early summer (end of the wet season). The most representative Macaronesian tree species had a clear and marked seasonality, except for the laurel forest species. SOS of Juniperus phoenicea subsp. turbinata was slightly later than for Olea europaea var. sylvestris and Pinus canariensis, while the mean EOS was slightly later for Olea europaea var. sylvestris. The intra-specific variability of SOS and EOS in the laurel forests was very high. LSP derived from Sentinel-2 data contributed to understand the seasonal dynamics of the main Macaronesian ecosystems. However, the heterogeneity of laurel forests made it difficult the LSP estimation in laurel forests. Thus, complementary phenological approaches are suggested to improve the knowledge of the seasonal dynamics of this Macaronesian ecosystem.

Effect of vegetation and land surface temperature on NO2 concentration: A Google Earth Engine-based remote sensing approach

With the increasing tension on the global sustainable environment in the urban areas, it is essential to monitor the airborne pollutants and understand the underlying factors that can trigger the situation in a worst-case scenario. Because of its cramped living conditions, excessive coal and fuel usage, and rapid deforestation, the southeast Asian region has historically had worse air quality than the rest of the world. The economic hubs of India and Bangladesh, in particular, have drawn so much attention away from rural regions that unrestrained urbanization is becoming controversial for planners, engineers, and stakeholders in sustainable development. This research combines the two main Asian capital regions, Delhi and Dhaka. It analyzes the change in nitrogen dioxide (NO2) concentration, land surface temperature (LST), and vegetation dynamics across three years (2019–2021) for summer and winter. The NO2 concentration data from Sentinel-5P has been extracted using Google Earth Engine (GEE), and Landsat-8 imagery was utilized for LST, Normalizer Vegetation Index (NDVI), and Enhance Vegetation Index (EVI). The statistical analysis has been carried out by dividing the research regions into one sq. km grid (1512 grids for Delhi and 1485 grids for Dhaka). According to descriptive research, Dhaka's condition is worse than Delhi's, with significant vegetation loss with LST and NO2 concentrations rising. In both research regions, the NO2 concentration is high throughout the winter. The Pearson correlation value demonstrates a negative association between total NO2 concentration and mean NDVI and EVI values and a positive relationship between total NO2 concentration and mean LST. The data have been further assessed using linear regression, which overlaps the correlation result with a maximum R-squared value of 0.2998 for NO2 and EVI in winter 2019.

Characterization of water hyacinth (Eichhornia crassipes (Mart.) Solms) biomass in Lake Tana, Ethiopia

This study aimed at assessing the characteristics of water hyacinth biomass in Lake Tana. Explanatory research design was used based on biomass samples and satellite images acquired along with field observation and literature reviews. The results showed that high amount of N and P nutrients, shallow nature of the Lake and suitable climatic conditions of the area are the causes for the weeds’ proliferation. The mean NDVI values were −0.542 to 0.828 and −0.628 to 0.877 in the dry and wet seasons, respectively. The spatial distribution of the weed was 2126.9 ha and 592.4 ha during the wet and dry seasons. The weed has recorded a mean leaf length of 9.3 cm, petiole length of 28.1 cm, root shoot ratio of 3.3, leaf blade area of 55.6 cm2 and leaf area index of 11343.9 cm2. In addition, a mean of 10.9 leaves per plant and 18.6 plants per 0.25m2 was found. Furthermore, the biomass analysis revealed 1,990,787.8 tons of wet and 554,523.8 tons of dry water hyacinth biomass can be harvested from the Lake per year. Stakeholders shall work on control of water hyacinth biomass as a short-term measure and reduction of nutrient inflow into the Lake as a long-term measure.

Spatiotemporal Variation and Influence Factors of Vegetation Cover in the Yellow River Basin (1982–2021) Based on GIMMS NDVI and MOD13A1

Depicting the spatiotemporal dynamics of vegetation cover in the Yellow River Basin (YRB) and delineating the influences of climate change and human activities on the dynamics have been of significant importance for understanding the surface earth systems in general and also for formulating ecological protection plans of the YRB in particular. This study uses the GIMMS NDVI dataset from 1982 to 2015 and the MOD13A1 NDVI dataset from 2000 to 2021 to explore the spatial and temporal characteristics of vegetation cover in the YRB for the period from 1982 to 2021 with an attempt to reveal the influencing factors. The spatial distribution and temporal variation characteristics of vegetation cover are analyzed by maximum value composite, Theil-Sen median trend analysis, and Mann–Kendall test. Combined with the mean annual temperature and annual precipitation in the same period, influencing factors of vegetation cover in the YRB are discussed by using binary linear regression analysis and residual analysis. Results show that: (1) the multi-year average NDVI values increase from the northwest to the southeast and that the annual mean values of the vegetation covers fluctuate relatively greatly along an increasing trend with a growth rate of 0.019/(10a). Understandably, the monthly mean NDVI values show a single-peak distribution pattern, with August being the peak time (0.4936). (2) 77.35% of the studied areas are characterized by exhibiting an increasing trend of vegetation cover during the study period (i.e., 1982–2021). (3) Vegetation cover of the YRB is affected by the combined effects of climate change and human activities, with human activities being more significant in the observed amelioration of vegetation cover.

The dynamics of vegetation and implications for ecosystem services in the context of urbanisation: An example from Huangyan-Taizhou, China

Urban sprawl and associated land use changes have been referred as primary drivers of environmental change. Yet it is unclear in detail how land use changes impact vegetative structures or ecosystem services and what the specific drivers of change are, especially in urban-rural interfaces in medium-sized Chinese cities. Our future contribution is intended to highlight the importance of urban-rural interfaces for sustainable land use and the development of ecosystem services. To this end, we mapped the dynamics of land cover and the condition of vegetation as well as ecosystem services based on remote sensing data for the period of 1992–2020, to quantify these changes in Huangyan district, Taizhou, China. The results show a dramatic increase in urban area over the 28-year timeframe, i.e. 265% growth in Huangyan district. This rise was particularly evident in the period of 2015–2020. The huge expansion in urban area came at the cost of arable land. To compensate the resulting loss of farmland, large-scale natural ecosystems such as forests, grasslands and wetlands were continuously transformed into arable (as well as urbanized) land. Despite the dramatic reduction in green space as a result of urbanisation, we found a slight increase in the overall mean NDVI value for Huangyan, mainly due to the improved condition and density of remaining forest area in the western countryside. Further, we evaluated the provision of ecosystem services (ESS) by adapting an existing assessment methodology elaborated by Burkhard et al. (2012). The results show that ESS supply continually fell in Huangyan since 1992, reflecting a reduction of green space. The highest ecosystem capacity is seen in recreation and biodiversity due to the large proportion of forested area. Our findings serve as an important basis for further investigations in the region of Huangyan by framing the general issue of green space dynamics and highlighting specific developments of ecosystem distribution and change as well as ESS supply.

Multiple Effects of Topographic Factors on Spatio-Temporal Variations of Vegetation Patterns in the Three Parallel Rivers Region, Southeast Qinghai-Tibet Plateau

Topographic factors are critical for influencing vegetation distribution patterns, and studying the interactions between them can enhance our understanding of future vegetation dynamics. We used the Moderate-resolution Imaging Spectroradiometer Normalized Differential Vegetation Index (MODIS NDVI) image dataset (2000–2019), combined with the Digital Elevation Model (DEM), and vegetation type data for trend analysis, and explored NDVI variation and its relationship with topographic factors through an integrated geographically-weighted model in the Three Parallel Rivers Region (TPRR) of southeastern Qinghai-Tibet Plateau (QTP) in the past 20 years. Our results indicated that there was no significant increase of NDVI in the entire basin between 2000–2019, except for the Lancang River basin. In the year 2004, abrupt changes in NDVI were observed across the entire basin and each sub-basin. During 2000–2019, the mean NDVI value of the whole basin increased initially and then decreased with the increasing elevation. However, it changed marginally with variations in slope and aspect. We observed a distinct spatial heterogeneity in vegetation patterns with elevation, with higher NDVI in the southern regions NDVI than those in the north as a whole. Most of the vegetation cover was concentrated in the slope range of 8~35°, with no significant difference in distribution except flat land. Furthermore, from 2000 to 2019, the vegetation cover in the TPRR showed an improving trend with the changes of various topographic factors, with the largest improvement area (36.10%) in the slightly improved category. The improved region was mainly distributed in the source area of the Jinsha River basin and the southern part of the whole basin. Geographically weighted regression (GWR) analysis showed that elevation was negatively correlated with NDVI trends in most areas, especially in the middle reaches of Nujiang River basin and Jinsha River basin, where the influence of slope and aspect on NDVI change was considerably much smaller than elevation. Our results confirmed the importance of topographic factors on vegetation growth processes and have implications for understanding the sustainable development of mountain ecosystems.

A landscape assessment of frost damage in the northmost Neotropical dry forest

Extreme climatic and weather events are inducing widespread tree mortality in many forest ecosystems worldwide. A severe frost related to the historic cold wave that affected much of North America in early February 2011 triggered extensive tree mortality in the lowland tropical dry forest in northwestern Mexico. In this landscape, we assessed frost-induced vegetation damage using anomalies of the widely used NDVI spectral index calculated with a 12-year pre-disturbance reference period. In addition, NDVI anomalies were related to ground-based measures of frost-disturbance severity (density of frost-killed trees) and forest recovery (post-frost recruits and resprouting of surviving trees) obtained in 48 1-ha study plots distributed across the landscape. We found negative NDVI anomalies in 84% of our study area, indicating widespread frost-induced vegetation damage. The relationship between NDVI anomalies and density of frost-killed trees confirmed the level of frost damage across the lowland vegetation, suggesting that even small deviations from long-term mean NDVI values may hint to severe disturbance on forest structure and function. Higher frost-disturbance severity stimulated more new recruitment and less tree resprouting. Our results demonstrate the reliability of spectral indices for rapid assessment of climate-induced vegetation change, the high vulnerability of tropical dry forests to extreme frost and the predominant mechanism of vegetation recovery after severe frost disturbance.

Assessment of properties of vegetation phenology in fire-affected areas from 2000 to 2015in the Peloponnese, Greece

Fire events are among the natural disasters that affect both ecosystem functioning but also human lives. The present work deals with the assessment of the properties of vegetation phenology in areas affected by fire events during a 10-year period in the Peloponnese, Greece. The main objective is to identify special patterns in NDVI time series of fire-affected areas, that can be used either as precursory indicators or as a tool for assessment of regeneration patterns. Therefore, we constructed and analyzed time series of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI at 250 m spatial resolution with a 16-day time step for affected and non-affected areas surrounding the burnt sites, where the majority of the area refers to the same land cover type. Analysis of changes of NDVI time series of both burnt and unburnt areas was conducted and results indicated that all fire events in the burnt sites caused breaks in the NDVI time series, demonstrated either as NDVI shifts to the lower and/or as changes in the trend and seasonality of the time series. Trends of the NDVI time series of the affected sites were almost doubled in the post fire period compared to the period before the event, indicating the evolution of the regenerating process. The basic statistics of the NDVI time series of the affected and non-affected areas correspond to greater minimum NDVI values of the affected areas throughout the study period, except of the two years after the fire event. Mean NDVI values were almost equal in burnt and unburnt areas before the events and reached the same values approximately seven years after the fire. Maximum NDVI values of the affected and non-affected sites were almost equal before the event but were abruptly reduced in the affected areas after the events and revert to their original state approximately ten years after. An alarming finding is the considerably lower standard deviation of the fire affected areas in comparison to non-fire-affected ones, during all years of the study period, except of a short period immediately after the fire event. It seems thus that areas covered by homogeneous vegetation with no fire breaks or other landforms to act as barriers to fire are the most vulnerable and most severely affected.

Possibility of using multiscale normalized difference vegetation index data for the assessment of total suspended solids (TSS) concentrations in surface water: A specific case of scale issues in remote sensing

The degradation of watersheds creates immense pressure on water quality, especially in arid and semiarid regions. Total suspended solids (TSS) provide essential information to water environmental quality assessments. However, the calibration of direct retrieval models requires complicated preparations and further increases uncertainties. Here, we hypothesized that a common remote sensing index (NDVI, normalized difference vegetation index) could be used to estimate TSS concentrations in water due to the effects of canopy cover. To address this hypothesis, we collected 65 water samples from the Ebinur Lake Watershed, northwest China, to investigate the potential relationships between TSS concentrations and Sentinel-2-based NDVI at various scales (100, 200, 300, 400, and 500 m). Subsequently, we established a classical measurement error (CME) model for the estimation of TSS concentrations. The results showed that TSS concentration is negatively related to the NDVI value at all buffer distances. The 300 m scale mean NDVI value showed the most effective explanation of the variations in TSS concentrations (R2 = 0.83, P-value < 0.001), which indicated that the TSS concentration can be assessed by NDVI. The CME model showed that NDVI values played an important role in the assessment of TSS concentrations in surface water. Furthermore, the results of both leave-one-out cross-validation and the accuracy measure suggested that this specific method is satisfactory. Compared with previous statistical and field monitoring results, the proposed method is promising for cost-effective monitoring of TSS concentrations in water, especially in data-poor watersheds. This specific method may provide the basis for the conservation and management of nonpoint source pollution in arid regions.

POST-FIRE FORESTRY RECOVERY MONITORING USING HIGH-RESOLUTION MULTISPECTRAL IMAGERY FROM UNMANNED AERIAL VEHICLES

Abstract. In recent years unmanned aerial vehicles (UAVs) have been used in several applications and research studies related to environmental monitoring. The works performed have demonstrated the suitability of UAVs to be employed in different scenarios, taking advantage of its capacity to acquire high-resolution data from different sensing payloads, in a timely and flexible manner. In forestry ecosystems, UAVs can be used with accuracies comparable with traditional methods to retrieve different forest properties, to monitor forest disturbances and to support disaster monitoring in fire and post-fire scenarios. In this study an area recently affected by a wildfire was surveyed using two UAVs to acquire multi-spectral data and RGB imagery at different resolutions. By analysing the surveyed area, it was possible to detect trees, that were able to survive to the fire. By comparing the ground-truth data and the measurements estimated from the UAV-imagery, it was found a positive correlation between burned height and a high correlation for tree height. The mean NDVI value was extracted used to create a three classes map. Higher NDVI values were mostly located in trees that survived that were not/barely affected by the fire. The results achieved by this study reiterate the effectiveness of UAVs to be used as a timely, efficient and cost-effective data acquisition tool, helping for forestry management planning and for monitoring forest rehabilitation in post-fire scenarios.

Analysis of Relationship between Vegetation Indices and Land Surface Temperature using GIS and Python in Middle Ganga Plain, India

Global vegetation dynamics is a significant phenomenon being monitored from space. This study attempts to establish relationship among vegetation changes and land surface temperature using the data derived from satellite products in the Middle Ganga Plain, India using python programming. Ten years of MODIS Land Surface Temperature (LST) on board Terra, Normalized Difference Vegetation Index/Enhanced Vegetation Index (NDVI/EVI) (1km spatial and 8-days composite temporal resolution for LST and 250m spatial and 16-days composite for NDVI/EVI) has been used in this study. The average LST for the month of January was 23.0°C which fell to 15.7°C for the same month in 2015; whereas in March it was recorded to be 35.3°C and reduced to 32.3°C in 2015. Mean NDVI value has been recorded to be 0.44 in January 2000 which has slightly increased to reach 0.50 for the same month in 2015. For the month of September, it was recorded at 0.49 in 2000 and 0.52 for the same month in 2015. This paper attempts to analyze the spatio-temporal distribution and empirical relationship of vegetation cover and LST using Python.

Groundnut spectral indices and characterization in relation to crop nutrient status

The spectral indices such as NDVI and RVI were calculated by using spectral signatures obtained through spectro-photo-sensor. The mean NDVI and RVI values were 0.71 and 6.46, respectively. Wide variation in the NDVI and RVI values were observed across the farmers’ fields revealing that crop was exposed to varied levels of nutrient deficiency. The SPAD (Indicator of chlorophyll activity) readings ranged between 25.8 and 46.8. The SPAD and the index leaf N, P, K, Ca and Fe content had positive and strong relation with both NDVI and RVI. However, NDVI registered stronger relation than RVI. Co-efficient of determination (R2) value of the multiple regressions between NDVI and index leaf nutrient content was 0.52. The mean N, P, K, Ca and S content in the index leaf were 3.9, 0.43, 3.45, 1.77 and 0.43 per cent, respectively. Similarly, Zn, Fe, Mn and Cu content in the crop index leaf tissue were 21.9, 40.6, 53.2 and 19.3 mg kg-1.

Analysis of Climate and Topography Impacts on the Spatial Distribution of Vegetation in the Virunga Volcanoes Massif of East-Central Africa

This paper aimed to investigate the influence of climatic and topographic factors on the distribution of vegetation in the Virunga Volcanoes Massif using GIS and remote sensing techniques. The climatic variables considered were precipitation, Land Surface Temperature (LST), and evapotranspiration (ET), whereas the topographic factors considered were elevation and aspect. The dataset consisted of MODIS NDVI data, satellite-delivered precipitation, ET, and the LST. A 2014 Landsat 8 OLI image was used to produce a vegetation map of the study area, while DEM was used to derive the elevation attributes and to calculate the aspect angles. Moran’s I and Geographically Weighted Regression (GWR) Model was used to analyze the relationships between the climatic factors and NDVI changes over elevation and aspect. The results indicated that among the nine vegetation types inventoried in the area, the Mean NDVI varied from 0.33 to 0.59 and the optimal vegetation growth was found at an elevation between 2000 and 3900 m, with mean NDVI values larger than 0.50. The peak mean NDVI value of 0.59 was found at the elevation from 2100 to 2800 m. Vegetation growth was found to be more sensitive to elevation, as NDVI values were more varied at a lower elevation (&lt;4000 m) than at a higher elevation (&gt;4000 m). Considering the aspect, the greater vegetation growth was found in SE (132°, 148°), SW (182°, 186°), and NW (309.5°–337.5°), with mean NDVI values larger than 0.56. This indicated that vegetation was susceptible to better growth conditions in the lower elevation ranges and in shady areas. The vegetation NDVI in this study area was mostly uncorrelated with precipitation (R2 = 0.34), but was strongly correlated with LST (R2 = 0.99) and ET (R2 = 98). LST (≥18 °C) and ET (1286 mm/year−1) were found to provide optimal conditions for vegetation growth in the Virunga Volcanoes Massif. Empirically, the results concluded that elevation, aspect, LST, and ET are the main factors controlling the spatial distribution and vegetation growth in this area. This information is significantly helpful for biodiversity conservation and constitutes a valuable input to environmental and ecological research.

Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies

The development of cost-effective, reliable and easy to implement crop condition monitoring methods is urgently required for perennial tree crops such as coffee (Coffea arabica), as they are grown over large areas and represent long term and higher levels of investment. These monitoring methods are useful in identifying farm areas that experience poor crop growth, pest infestation, diseases outbreaks and/or to monitor response to management interventions. This study compares field level coffee mean NDVI and LSWI anomalies and age-adjusted coffee mean NDVI and LSWI anomalies in identifying and mapping incongruous patches across perennial coffee plantations. To achieve this objective, we first derived deviation of coffee pixels from the global coffee mean NDVI and LSWI values of nine sequential Landsat 8 OLI image scenes. We then evaluated the influence of coffee age class (young, mature and old) on Landsat-scale NDVI and LSWI values using a one-way ANOVA and since results showed significant differences, we adjusted NDVI and LSWI anomalies for age-class. We then used the cumulative inverse distribution function (α≤0.05) to identify fields and within field areas with excessive deviation of NDVI and LSWI from the global and the age-expected mean for each of the Landsat 8 OLI scene dates spanning three seasons. Results from accuracy assessment indicated that it was possible to separate incongruous and healthy patches using these anomalies and that using NDVI performed better than using LSWI for both global and age-adjusted mean anomalies. Using the age-adjusted anomalies performed better in separating incongruous and healthy patches than using the global mean for both NDVI (Overall accuracy=80.9% and 68.1% respectively) and for LSWI (Overall accuracy=68.1% and 48.9% respectively). When applied to other Landsat 8 OLI scenes, the results showed that the proportions of coffee fields that were modelled incongruent decreased with time for the young age category and while it increased for the mature and old age classes with time. We concluded that the method could be useful for the identification of anomalous patches using Landsat scale time series data to monitor large coffee plantations and provide an indication of areas requiring particular field attention.

Flood hazard assessment of 2014 floods in Sonawari sub-district of Bandipore district (Jammu&Kashmir): An application of geoinformatics

Massive flooding in the Kashmir valley was induced by heavy rainfall due to combined effects of western disturbances and Indian summer monsoon during September 04–10, 2014. The populous Sonawari sub-district was severely affected by flood waves that caused complete damage to standing crops and disrupted man-made linear structures (embankment, canal, and road network). High value land use classes (cropped land, built-up and plantation), which encroached on swamps and wetlands between 1972 and 2014, were badly damaged. The main aim of this study is to assess flood hazard at village level in the sub-district. Village-wise standardised flood hazard indicators were flood affected areas, mean turbidity, mean flood depth and duration. These indicators were derived from the Landsat 7 ETM+, Landsat 8 OLI-TIRS images and SRTM (30m) DEM. Linear combination of these standardised indicators in GIS environment formulated Flood Hazard Index (FHI). Furthermore, the computed FHI values were divided into four flood hazard zones. Low, moderate, high and very high flood hazard zones occupied 16, 32, 34 and 19% of the total area, respectively. Further, these zones accounted for 21, 23, 33 and 23% of the total number of villages and towns (43), respectively. FHI was validated with overall changes in NDVI values with the help of linear regression model that showed a fair association between village-wise FHI and changes in mean NDVI values as the coefficient of determination (R2) was 0.75 with a least standard error of 0.05. Village-wise flood hazard assessment has immense importance to plan future land use and to implement flood management strategies during pre and post- floods in the sub-district.

Natural vegetation covers as indicators of the soil water content in a semiarid mountainous watershed

This paper investigates the use of the vegetative state of natural covers as an indicator of soil moisture conditions at the end of the dry season in order to evaluate the cumulative effect of the hydrological regime. To achieve this, the three major vegetation covers in a mountainous semiarid environment in southern Spain were selected. Temporal and spatial trends of NDVI from Landsat-TM images were computed and related to the different hydrological patterns of variables in the study site, which were obtained with the hydrological WiMMed model. The heterogeneity in the hydrological behavior during the study period (914.5mm of annual rainfall in the wettest year (2009–2010) and 284.4mm in the driest year (2004–2005)) was reflected in the annual differences in NDVI values with steady mean NDVI values in coniferous vegetation (0.5–0.6) and more variable values in scrub cover. Both Correlation Analysis and Principal Component Analysis showed correlations among the different states of the vegetation cover, the variables involved in the soil water balance and those related to the snow dynamics of the antecedent year. Exponential fits were obtained between the mean annual soil water content and NDVI values with Pearson r2 coefficients of over 0.7 in scrub cover. In certain years, the best fits were also found in scrub cover with r2 values of up to 0.9. These results demonstrate the relationship between soil water content, the vigor of the natural vegetation and the hydrological characteristics of the antecedent year. The expressions obtained may serve to adjust the soil water content at the beginning of a hydrological year and to use the scrub cover as an indicator of the soil water balance in the area for a given year.

Monitoring of damage from cedar shoot moth Dichelia cedricola Diakonoff (Lep.: Tortricidae) by multi-temporal Landsat imagery

Abstract: In this study defoliation damage in Taurus cedar (Cedrus libani A. Rich) stands in Turkey (Isparta region) caused by cedar shoot moth (Dichelia cedricola Diakonoff - Lep.: Tortricidae) was examined using multi-temporal Landsat data. Undamaged, low-damaged and heavily-damaged areas were located by assessing the variation of the Normalized Difference Vegetation Index derived from satellite imagery. Threshold boundaries for different damage levels were defined using mean NDVI values obtained from sub-areas spanning over the whole range of NDVI values. The reliability of the classification based on damages was statistically tested by comparing mean annual ring widths measured on increment cores extracted from sample trees exposed to different damage levels. Significant differences were found in mean annual ring widths among different areas previously classified based on NDVI data. Mean annual ring width in 2001 (an outbreak year) was 1.64 mm for undamaged area, 1.04 mm for low-damaged area, and 0.54 mm for heavily-damaged area. These findings indicate that damage mapping and monitoring mass damage caused by insect defoliation in Taurus cedar stands can be performed remotely by using NDVI values and Landsat TM data.