Low Normalized Difference Vegetation Index Values Research Articles

Sensitivity of Landsat NDVI to subpixel vegetation and topographic components in glacier forefields: assessment from high-resolution multispectral UAV imagery

Recently, deglaciated landscapes are ideal natural arenas to investigate ecological succession processes. However, ground data acquisition remains complicated as glacier forefields are often difficult to access and fieldwork possibilities remain limited. Remote sensing offers an opportunity to bypass this issue and increase spatial and temporal coverage of ecological parameters. The Landsat satellites (5 to 8) provide reflectance data for the past 40 years, which align with recent phenomena of glacier retreat and related ecological and geomorphological dynamics in glacier forefields. Difficulties remain as information retrieved from 30-m Landsat pixels are the result of a mixture of objects influencing reflectance signals. Here, we used a submeter multispectral unmanned aerial vehicle (UAV) image of the Glacier noir foreland, France, to assess the sensitivity of Landsat normalized difference vegetation index (NDVI) to subpixel vegetation and topographic components. We found a twofold linear relationship (a = 0.456) and high sensitivity between fractional vegetation cover (FVC) and Landsat NDVI with detection of low vegetation changes (FVC > 5 % ) at low NDVI values (<0.1) (F-score = 0.75). We also showed that vegetation height and subpixel topographic heterogeneity leads to misestimation of vegetation cover as quantified by Landsat NDVI. Overall, our comparative analysis using very-high resolution UAV imagery provides support for the use of widely available Landsat imagery for investigating vegetation dynamics in glacier forefields.

Article Title Variations in Growing Season NDVI and Its Sensitivity to Climate Change Responses to Green Development in Mountainous Areas

The Normalized Difference Vegetation Index (NDVI) is sensitive to changes in surface vegetation cover. Research into how climate change impacts surface vegetation cover is essential to manage ecological systems and promote green development. The Western Henan Mountains, located in the transitional zone between the northern subtropical and warm temperate zones of China, is an ideal location to study the impacts of climate change on surface vegetation cover. Combining a digital elevation model (DEM) with temperature and precipitation data; and MODIS-NDVI imagery (2000∼2017) for the Western Henan Mountains, this study explores variations in the growing season NDVI and its response to climate change. Results show that there are significant changes with fluctuations in NDVI values from 2000 to 2017. NDVI increased at a growth rate of 0.027 per decade (p &amp;lt; 0.05) overall, indicating vegetation conditions have gradually improved. Although the NDVI value showed an overall increasing trend, 19.12% of the areas showed a decreasing trend, interspersing and intersecting spatially, showing significant spatial differences. NDVI increased initially, but then decreased as a function of elevation, which was shown to be proportional to slope and independent of aspect. Variables including elevation and slope gradient are shown to provide high explanation of NDVI variability, whilst temperature is shown to have a more significant impact on NDVI than precipitation. However, vegetation responses to temperature and precipitation covaried with both slope and aspect. Positive NDVI trends were strongest at low elevations (i.e., &amp;lt;1,100 masl), which we attribute to vegetation restoration activities. Lower NDVI values characterized gentle slopes (&amp;lt;5°), whilst higher values were, in contrast, associated with steeper slopes (5∼10°). This study highlights the complex mechanisms and their relations governing vegetation response to climate change and should form an instructive basis for both future modeling studies investigating the response of vegetation to future global warming.

Novel sensor-based method (quick test) for the in-season rapid evaluation of herbicide efficacy under real field conditions in durum wheat

AbstractOptimum herbicide use is a key factor affecting the success of any integrated weed management strategy. The main objective of the current study was to implement a method based on spectrometer measurements for the in situ evaluation of herbicide efficacy and the detection of potentially herbicide-resistant weeds. Field trials were conducted in Greece between 2018 and 2020 in several durum wheat fields (Triticum durum Desf.). In all trials, the overall effect of herbicide application on the recorded Normalized Difference Vegetation Index (NDVI) values (at 1 and 2 wk after treatment [WAT]) was significant (P ≤ 0.001). For the majority of the surveyed fields, low NDVI values were recorded after 2,4-D application and a mixture of clopyralid + florasulam from 1 WAT, suggesting their increased efficacy. In several cases, the application of pyroxsulam + florasulam resulted in significant NDVI reductions at 2 WAT. As observed at the end of the growing seasons, the herbicides that reduced NDVI resulted in lower weed biomass. Strong correlations were observed between weed aboveground biomass and NDVI (2 WAT). In particular, R2 values were 0.8234 to 0.8649, 0.8453, 0.8595, 0.8149, and 0.8925 for the Aliartos, Thiva, Domokos, Larissa, and Orestiada fields, respectively. The overall effects of herbicide application on wheat grain yield were also significant (P ≤ 0.001). Pot experiments confirmed that the high NDVI values in some cases could be attributed to the presence of herbicide-resistant weeds. For instance, the resistance indices of two accessions of catchweed bedstraw (Galium aparine L.) to mesosulfuron-methyl + iodosulfuron-methyl-sodium ranged between 9.7 and 13.2, whereas one sterile oat [Avena sterilis L. ssp. ludoviciana (Durieu) Gillet &amp; Magne] accession was 8.8 times more resistant to fenoxaprop-p-ethyl than a susceptible one. The present study is targeted at making a significant contribution toward establishing cause–effect relationships and presenting a useful tool for developing more effective weed management practices in more arable crops and under different soil and climatic conditions.

Pairing soil sampling with very-high resolution UAV imagery: An examination of drivers of soil and nutrient movement and agricultural productivity in southern Ontario

Soil erosion from agricultural lands continues to be a global societal problem. The movement of soils is often accompanied by nitrogen and phosphorus that are crucial to crop growth, but their redistribution from farm fields to waterways can reduce crop yields and degrade water quality. While within-field sediment and nutrient movement has been quantified using small plots and edge-of-field monitoring, these approaches fail to capture their spatial distribution. The pairing of soil sampling with unmanned aerial vehicle (UAV) data offers a novel and low-cost approach to map the spatial distribution of soil characteristics and nutrient concentrations within a farm field. UAV data are used to generate a digital terrain model and subsequently map within-field topographic variation and erosional flow pathways. Topographic variation is discretized into landform elements (flat, shoulder, backslope, footslope) that capture within-field heterogeneity and have potential for scaling out soil sampling to larger spatial extents. Our results show the controlling factor of water content and organic matter on crop yield, as represented by normalized difference vegetation index (NDVI). Significant differences in water content and organic matter were found across landform elements with increases in both parameters downslope. Upslope landform elements contained more sand content (9–20%) and had lower NDVI values than downslope elements. Complementing these findings, significant differences in organic matter, soluble nitrogen, and soluble reactive phosphorus occurred along erosional flow pathways. Our within-field results have implications for farmers, as our analysis of soil characteristics indicated that NDVI was positively correlated with water content (0.05), organic matter (0.15), silt (0.36), and clay (0.17) content and negatively correlated with soluble nitrogen (−0.47) and phosphorus (−0.30) concentrations. In addition to discussing the challenges and opportunities for expanding upon the presented research, we use a simple proof-of-concept hydrological model to demonstrate the potential role of hydrological connectivity and variable source area as a driver of within-field nutrient movement. The combination of our empirical results showing water content and organic matter as controlling factors on agricultural yield, the role of hydrological connectivity, and climate predictions of increased storm intensity suggest that additional research into the generation of novel time-series soil sampling and UAV-generated erosion and flow data could advance our understanding of the variations in soil characteristics and nutrient concentrations within individual farm fields.

A methodological approach to assess canopy NDVI–based tomato dynamics under irrigation treatments

A tomato field experiment under deficit irrigation treatments was established to assess the feasibility of nonlinear mixed models to predict the change in the canopy Normalized Difference Vegetation Index (NDVI) along the growing season. The irrigation doses were 50, 70, 85, and 100 % of the actual evapotranspiration (ETc). The experiment was performed in the Peruvian Arid Coastal Region (13.10 S, 76.37 W). On-ground destructive plant measurements (7 times) allowed to obtain the leaf area index (LAI) and the canopy equivalent water thickness (EWTcanopy) to validate the experiment. Similarly, volumetric soil moisture was monitored at three hours interval using time-domain reflectometry. High-resolution multispectral imagery was acquired along the growing season (10 times) using an unmanned aerial vehicle. NDVI at plant level was obtained based on oversegmented imagery. The canopy NDVI growth curves were obtained in relation to the irrigation treatments and growing degree days (GDD) using a nonlinear mixed model.Deficit irrigation doses (50, 70 and 85 % ETc) induced lower biomass accumulation and greatly affected LAI and EWTcanopy, attaining lower values (p < 0.05) than the control (100 % ETc). In spite of reaching higher LAI and EWTcanopy values, 70 % ETc was not significantly different from 50 % ETc (p < 0.05) showing the difficulty to accurately discriminate treatments with high deficit irrigation; 85 % ETc produced important differences on EWTcanopy and LAI compared to the other deficit irrigation treatments (p < 0.05) due to a higher leaf area expansion. Deficit irrigation doses led to less vigorous plants resulting in lower NDVI compared to the control. Notwithstanding 50 % ETc obtained lower NDVI values than 70 % ETc, differences were not significant. Volumetric soil moisture was consistent with the irrigation treatments, resulting in a decreased trend with decreasing water application along the growing season. A three-parameter logistic mixed model was found to be well-suited to fit the tomato canopy NDVI growth curves in function of the irrigation treatments (i.e. fixed effects) considering the plant-specific variability (i.e. random effects). At maturity stage, significant differences of the deficit irrigation treatments from the control were obtained (p < 0.05) when cumulative effect of the treatments was well-defined and NDVI reached asymptotic behavior. During the vegetative stage, the inflection point marked the fastest NDVI growth rate for the 100 % ETc treatment whereas the lowest for the 50 % ETc (p < 0.05). The diagnosis plots and the low root mean squared error of the residuals indicated the suitability of using the three-parameter logistic growth curve estimated as a nonlinear mixed model to predict NDVI.

Pemetaan Kondisi Ekologi Perkotaan Skala Mikro Menggunakan Citra Landsat 8 di Kota Semarang

Semarang City, as a capital city of a province in Indonesia, has experienced intensive rural or suburban migration to urban robinareas. Consequently, the land surface temperature (LST) is getting warmer in urbanized areas and leading to microscale temperature variation from time to time. The objectives of this study are 1) to investigate the geographical distribution of LST and vegetation covers in Semarang, and 2) to map microscale urban ecological condition with regards to LST based on urban thermal field variance index (UTFVI). Landsat 8 data was utilized to derive LST as well as vegetation covers by means of normalized difference vegetation index (NDVI). Next, UTFVI was classified based LST. This study revealed that relatively low NDVI values which mainly consists of built up areas and grassland are predominantly concentrated in the northern and central parts of Semarang. High NDVI values representing vegetation covers are predominantly located in the southern portion of Semarang. The most ecologically depressed areas are mainly distributed in the central portions of the city toward the northern portions of coastal areas. Prioritized sub-districts to be ecologically restored are Ngaliyan, Semarang Utara and Semarang Barat. Upcoming studies should emphasize on finding suitable measures to restore ecologically stressed areas.

Vegetation Dynamics with Elevation in Southern European Russia

In this research work vegetation dynamics was examined for spring-summer (may–august) season based on Moderate Resolution Imaging Spectroadiometer (MODIS) satellite data generated Normalized Difference Vegetation Index (NDVI) with elevation from 2014 to 2018 in Southern part of European Russia. The resulting divergent groups with respect to decreasing, increasing or no trends presented significant differences in vegetation dynamics and correlation between elevation and NDVI values. Results indicate that in the year 2016, vegetation was highly degraded with low NDVI values from less than 0 to 0.20. In comparing of elevation maximum vegetation was present from 100 to 250 m elevation, below and above this range was very low vegetation. In high elevation from 100 to 250m, low NDVI values slightly shift in high NDVI values, which show healthy vegetation on high altitude. Field observation also conform high NDVI values to high elevation. The MODIS time series with 250m resolution is appropriate for vegetation dynamics study for a large, inaccessible area with low coast, within less time for long term changes in vegetation growth.

LAND COVER MONITORING OF LAGUNA LAKE WATERSHED USING MODIS NDVI DATA

Abstract. Land use and land cover monitoring is an important component in the management of Laguna Lake watershed due to its impacts on the lake’s water quality. Due to limitations caused by cloud cover, satellite systems with limited revisit capability fail to provide sufficient data to more effectively monitor the land surface. Normalized difference vegetation index (NDVI) derived from MODIS image data were used to generate land cover maps for the years 2001, 2005, 2009, 2013, and 2017. These were produced by classifying ISODATA classes using annual NDVI profiles, which resulted in land cover classes, namely, agricultural land, built-up, forest, rangeland, water, and wetland. The resulting maps were post-processed using multi-variate alteration detection (MAD), resulting in multi-temporal land cover maps with improved overall accuracies and kappa coefficients that indicate moderate agreement with ground truth data. Spatiotemporal hot spot analysis was also performed using NDVI data from 2001 to 2017 to identify vegetation hot spot areas, where clustering of low NDVI values were observed over the years. Results showed an increasing trend in built-up areas accompanied by decreasing trends in water and wetland areas, indicating impacts caused by land reclamation and expansion of residential subdivisions near the lakeshore. The decrease in total vegetation area from 2001 to 2017 could be attributed to conversion of land to built-up surface. Vegetated areas in identified hot spots decreased from 41% in 2001 to 19% in 2017. This suggests that vegetation cover in these hot spots was converted to non-vegetated surface during the time period studied.

Risk of Low Productivity is Dependent on Farm Characteristics: How to Turn Poor Performance into an Advantage

The principle role of agriculture is to produce food for the increasing human population. However, the environmental footprint needs to be simultaneously reduced. Data from the Finnish Food Authority (181,108 parcels in the south-western crop production region of Finland) were used to identify the farming system, farm type, farm size, field parcel scale, physical parcel characteristics, cultivated crops, crop rotations and cultivars. Sentinel-2 derived Normalized Difference Vegetation Index (NDVI) values were used to identify fields with very low productivity. Thereby, the impacts of farm and field characteristics on risks of low NDVI values and their associated means of coping by the farmer were studied. High variations in field parcel characteristics and growth capacity were typical in the studied area. Although it is challenging for farmers, high variation can provide many opportunities for the development of multifunctional and resource-smart production systems, e.g., by optimizing land use: allocating high-quality fields for food production, and poorly performing fields for extensification, i.e., the production of environmental benefits. Many usable policy instruments are available to support such a transition, but more focus should be put onto the most efficient means to enable progress towards environmentally, economically and socially sustainable high-latitude agricultural systems.

Long-term enrichment with the camedor palm (Chamaedorea elegans Mart.) improved forest cover in an anthropogenic tropical landscape

The use of non-timber forest products forms part of the strategy contributing to the well-being of populations near forest areas. Chamaedorea is one of the most important palm genera that is globally commercialized profitably for its attractive foliage used in floral decorations. In our study area located in the central region of Veracruz, Mexico we identified changes in tree cover between 1973 and 2017 using the normalized difference vegetation index (NDVI). We conducted plant surveys to identify tree species richness associated with the cultivation of C. elegans. We conducted semi-structured interviews with local palm producers to understand their perceptions regarding changes in tree cover in the study area and its association with the presence of the camedor palm. Soil cover patterns and vegetation index signatures indicated an increase in the forested area from 1973 to 2017 (after more than 43 years), with a 69% gain in secondary vegetation coverage with low NDVI values. This increase is associated with the cultivation of palm, since arboreal cover had previously been dominated by species traditionally used as shade for camedor cultivation. Tree diameters at breast height (DBH) were low, indicating young secondary forests with low recruitment and diversity in the understory suggestive of simplified species assemblages; most of the associated species were long-living pioneers and typical of the secondary forest favored by the palm producers. Producers mentioned they did not remove coffee because it is their main means of subsistence, and in combination with the palm, there is greater economic benefit. They also confirmed an increase in forest cover owing to the introduction of palm cultivars in the areas north and south of the study area. This was confirmed with the NDVI results, which detected the same spatial pattern. These changes resulted in the interruption of the processes of succession without the transition of secondary to old-growth forests. However, the canopy of the study area provides better environmental services than did the previously existing vegetation cover. This study showed the need for the assessment of non-timber forest products as key indicators of land use change and alterations in associated plant cover.

Exploring improvements to the design of an operational seasonal forage scarcity index from NDVI time series for livestock insurance in East Africa

Recurrent drought represents a major threat in arid and semi-arid regions of East Africa where pastoralists depend on their livestock for subsistence. In Kenya and southern Ethiopia, an existing satellite-based index insurance scheme aims to protect pastoralists against the adverse effects of drought. Under that scheme, payouts are made based on an area-aggregated seasonal forage scarcity index derived from remotely-sensed Normalized Difference Vegetation Index (NDVI). NDVI values are directly averaged per unit areas of insurance (UAI), which are based on administrative borders but take into limited account the ecological variability within the unit. The choice of administrative boundaries at the onset of the analysis may negatively impact the performance of the product. Our study explores an alternative index design based on an ecological stratification of the study area. First, we performed an unsupervised classification of NDVI time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) to group pixels with similar temporal NDVI trajectories. Next, we used average NDVI profiles and ancillary data to discard areas deemed insignificant for forage production. We then transformed NDVI values into z-scores to assess how each pixel relates to the multi-year distribution of NDVI values per class and season. In the final step, we calculated the alternative forage scarcity index, which is the percentage of pixels with anomalously low NDVI values per season and UAI (i.e. z-score ≤ −1.0). To evaluate its performance, we compared unit-level results for both the original and alternative designs against spatially-aggregated monthly household survey data on livestock mortality from 16 sample sites corresponding to eight administrative units within the study area. Besides performing better in predicting livestock mortality (i.e. increases of 53% and 39% in correlation coefficients as measured by Pearson’s r and Spearman’s ρ, respectively) and strengthening the ecological significance of the index, the proposed design has a number of other advantages: 1) the index is calculated using a much larger statistical basis, 2) it allows for analysis of forage conditions at sub-unit level, and 3) it offers a more flexible structure for payout calculation. These advantages could be of particular relevance for expanding the index-insurance scheme to agro-pastoral regions characterized by more heterogeneous landscapes. Finally, we propose to consider using this approach to better account for the ecological variability of rangelands in other NDVI-based early warning and monitoring systems.

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

Quantitative analysis of the factors influencing spatial distribution of soil heavy metals based on geographical detector

With the rapid development of modern industry, heavy metals in the soil introduce the risk of serious pollution. To reduce this pollution risk, the following four research questions needed to be addressed: What are the main influencing factors of soil pollution? What is the degree of influence? Do factors operate independently or are they interconnected? Which regions have high pollution risk and should be paid more attention? The study area was in Huanjiang County, with 273 km2, and geographical detector proved to be a useful tool to solve these four problems. We found that mine activity and pH value were the primary influencing factors for total and water-soluble heavy metals. The interaction effects of mine activity and soil type, pH values, and normalized difference vegetation index (NDVI) for total heavy metals, as well as pH value and mine activity for water-soluble heavy metals, were greater than the sum effect of two factors. Zones with a high concentration of heavy metals were closer to the road and farther from the mine area, which had low NDVI, large slope, high terrain, and large pH values. Concentrations of total heavy metals were higher in calcareous soils and in dryland and forests. Zones with a higher concentration of water-soluble heavy metals were closer to the mine and river, which had lower DEM and pH values. The uncertainty of geographical detector was also analyzed on the basis of their interpolation accuracy and the stratification number of influencing factors, and we found that the existing sample numbers and the stratification number of influencing factors met the needs of geographical detector calculation. This study's conclusions are useful for soil pollution control and restoration.

A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China

AbstractSince 2000, the gross domestic product (GDP) of the Pearl River Delta (PRD) has increased by more than eight‐fold. Experiences throughout the world indicate that the impacts of economic development on vegetation are particularly important. This study analyzed the variations in vegetation coverage in the PRD from 2000 to 2016. Moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data were employed, whereas climate data, GDP, and population were also used as influencing factors. Topographic factors were introduced to explain the spatial vegetation patterns, and the impacts of human activities on vegetation were explored. The results revealed (a) the overall vegetation coverage in the PRD showed an increasing trend, and the overall average NDVI increased by 0.0033/a. (b) The spatial pattern did not change greatly, but the high and low NDVI values moved to low elevations and shallow topographic slopes where human activities were prevalent. This trend resulted in increased NDVI values in 38% of the area of the PRD (p &lt; 0.01). (c) There was a positive correlation between NDVI and economic growth (GDP, population), with correlation coefficients greater than 0.8 (p &lt; 0.01). NDVI is influenced by climate change and human activities (such as afforestation, resident lifestyles, and economic development policies). This study reveals that with good policies, both rapid economic development and vegetation coverage improvements can be simultaneously achieved in a region. The findings of this study are crucial for the sustainable development of the mega‐urban agglomerations of the PRD and Guangdong‐Hong Kong‐Macau Grand Bay.

A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter

High-quality Normalized Difference Vegetation Index (NDVI) time-series data are important for many regional and global ecological and environmental applications. Unfortunately, residual noise in current NDVI time-series products greatly hinders their further applications. Several noise-reduction methods have been proposed during the past two decades, but two important issues remain to be resolved. First, the methods usually perform poorly for cases of continuous missing data in the NDVI time series. Second, they generally assume negatively biased noise in the NDVI time series and thus erroneously raise some local low NDVI values in certain cases (e.g., the harvest period for multi-season crops).We therefore developed a new noise-reduction algorithm called the Spatial-Temporal Savitzky-Golay (STSG) method. The new method assumes discontinuous clouds in space and employs neighboring pixels to assist in the noise reduction of the target pixel in a particular year. The relationship between the NDVI of neighboring pixels and that of the target pixel was obtained from multi-year NDVI time series thanks to the accumulation of NDVI data over many years, which would have been impossible a decade ago. We tested STSG on 16-day composite MODIS NDVI time-series data from 2001 to 2016 in regions of mainland China and 11 phenology camera sites in North American. The results showed that STSG performed significantly better compared with four previous widely used methods (i.e., the Asymmetric Gaussian, Double Logistic, Fourier-based, and Savitzky-Golay filter methods). One obvious advantage was that STSG was able to address the problem of temporally continuous NDVI gaps. STSG effectively increased local low NDVI values and simultaneously avoided overcorrecting low NDVI values during the crop harvest period. In addition, implementing STSG required only raw MODIS NDVI time-series products without any additional burden of data requirements. All of these advantages make STSG a promising noise-reduction method for generating high-quality NDVI time-series data.

Returning home after fire: how fire may help us manage the persistence of scrub-steppe specialist bird populations

Semi-natural open habitats have drastically changed in the last few decades due to agricultural intensification and rural depopulation. Steppe-birds, and especially those adapted to primary stages of vegetation succession, are threatened by an increase in scrub cover, and management actions are being applied to reverse scrub encroachment and restore habitat suitability in semi-natural open habitats. In this paper we evaluated for the first time, the long-term effects of a wildfire on habitat structure, vegetation productivity, and the associated response of an endangered scrub-steppe specialist bird, the Dupont’s Lark Chersophilus duponti. Wildfire occurred in a Mediterranean steppe of central Spain dominated by permanent community of dwarf cushions scrubs. Bird abundance was evaluated by line transects in the burnt and unburnt areas 3 years prior to the fire and 4 and 7 years after the fire. We quantified changes in habitat structure at fine scale level through vegetation sampling points and in vegetation productivity by estimating the Normalized Difference Vegetation Index (NDVI). Fire had strong effects for at least up to 4 years after the fire, when lower NDVI values, less scrub cover and fewer, but not significant, number of males were detected in the burnt area with respect to the pre-fire conditions. Seven years after fire most vegetation variables measured did not differ between areas, number of males detected within the burnt area was recovered and NDVI values in burnt area were slightly recovered but were significantly lower than in control area. Slow regeneration of the scrub cover after fire explained the unsuccessful occupation of the burnt area by the Dupont’s Lark up to several years after fire. The more dispersed and shorter habitat created by fire 7 years after the fire seems to be more suitable for the species than that in control areas. The large number of males around the burnt area may have played a role in the recolonization process. In sum, vegetation recovery and the presence of a low scrub-steppe specialist, as the Dupont’s lark, suggests that fire management could be integrated into conservation plans to effectively manage scrub encroachment processes in Mediterranean scrub-steppes.

TREE AGE AS ADJUSTMENT FACTOR TO NDVI

ABSTRACT This study aimed to increase satellite-derived Normalized Difference Vegetation Index (NDVI) sensitivity to biophysical parameters changes with aid of a forest age-based adjustment factor. This factor is defined as a ratio between stand age and age of rotation, which value multiplied by Landsat-5/TM-derived NDVI generated the so-called adjusted index NDVI_a. Soil Adjusted Vegetation Index (SAVI) was also calculated. The relationship between these vegetation indices (VI) with Eucalyptus and Pinus stands’ wood volume was investigated. The adjustment factor caused an increase in NDVI dynamic range values, since older stands tended to be assigned with highest NDVI values, while younger ones tended to be forced to assume lower NDVI values. As a result, direct and significant relationship between NDVI_a and wood volume could be maintained for wider ranges of wood volume. However, it was observed that NDVI_a was only statistically superior to NDVI and SAVI when a detailed age dataset is available. It is conclude that, the stand age has potential to improve NDVI sensitivity to biophysical parameters allowing that quantitative estimates could be made since young to adult stands.

Preliminary determination of geothermal working area based on Thermal Infrared and Synthetic Aperture Radar (SAR) remote sensing

Remote sensing is one of the methods for geothermal exploration. This method can be used to map the geological structures, manifestations, and predict the geothermal potential area. The results from remote sensing were used as guidance for the next step exploration. Analysis of target in remote sensing is an efficient method to delineate geothermal surface manifestation without direct contact to the object. The study took a place in District Merangin, Jambi Province, Indonesia. The area was selected due to existing of Merangin volcanic complex composed by Mounts Sumbing and Hulunilo with surface geothermal manifestations presented by hot springs and hot pools. The location of surface manifestations could be related with local and regional structures of Great Sumatra Fault. The methods used in this study were included identification of volcanic products, lineament extraction, and lineament density quantification. The objective of this study is to delineate the potential zones for sitting the geothermal working site based on Thermal Infrared and Synthetic Aperture Radar (SAR) sensors. The lineament-related to geological structures, was aimed for high lineament density, is using ALOS - PALSAR (Advanced Land Observing Satellite - The Phased Array type L-band Synthetic Aperture Radar) level 1.1. The Normalized Difference Vegetation Index (NDVI) analysis was used to predict the vegetation condition using Landsat 8 OLI-TIRS (The Operational Land Imager – Thermal Infrared Sensor). The brightness temperature was extracted from TIR band to estimate the surface temperature. Geothermal working area identified based on index overlay method from extracted parameter of remote sensing data was located at the western part of study area (Graho Nyabu area). This location was identified because of the existence of high surface temperature about 30°C, high lineament density about 4 - 4.5 km/km2 and low NDVI values less than 0.3.

Geotechnology as support for the management of conservation units in Brazil's Pantanal

The aim of this paper is to study temporal variations in vegetation indices as a tool for monitoring the integral protection conservation units (parks, reserves, and ecological stations) in the Brazilian Pantanal, giving support to environmental management actions in these areas. The study was carried out in five conservation units. Evaluated data, beside the Normalized Difference Vegetation Index (NDVI), included information on wildfires and deforestation. Analyses showed that the Taiamã Ecological Station, the Pantanal Matogrossense National Park, and the Encontro das Águas State Park presented high vegetation indices, showing their effectiveness as areas of environmental protection. Although this latter state park presented one fire hotspot in 2015, there was significant improvement in this park. The Guirá State Park and the Pantanal of Rio Negro State Park presented relatively low NDVI values when compared with other analysed areas. The aggravating factor was that in the Rio Negro park 36 fire hotspots occurred in 2007, demonstrating the need for more vigorous monitoring and management of this protected area.

Long and short term population dynamics of acacia trees via remote sensing and spatial analysis: Case study in the southern Negev Desert

Monitoring vegetation dynamics in hyper-arid zones is important because any decrease in the already sparse vegetation cover in these areas could considerably affect the entire ecosystem. The new generation of high spatial resolution satellite (HSR) sensors is suitable for monitoring trees in arid regions because of the distinct and separate objects that trees represent in these environments. The main limitation of modern HSR sensors is the lack of a historical archive that would otherwise enable meaningful landscape change detection, especially in arid regions where tree population dynamics are naturally very slow. This study uses spatial analysis to gain information regarding the long- and short-term dynamic processes affecting the acacia tree population in Wadi Ktora, in the southern Arava Valley, Israel. The data is extracted from a single HSR aerial photograph composed of three spectral bands in the visible and infrared spectrum (green, red, and near infrared).A map of individual acacia trees that is extracted from a colour infrared aerial photograph of Wadi Ktora from 2010 enables the examination of spatial distribution patterns for both tree size and foliage health. Tree size distribution is used as an indicator of long-term (decades) hydrologic spatial processes affecting the acacia population. The tree health distribution is used as an indicator for short-term (months to a few years) hydrologic spatial processes, such as the paths of recent flash floods events. Comparing the distributions of tree size and normalized difference vegetation index (NDVI) enables differentiation between the long-term and short-term processes that brought the population to its present state.Using spatial statistic grouping, the distribution of the trees in the wadi (ephemeral stream) is divided into three distinct categories: (1) large trees with high NDVI values, (2) large trees with low NDVI values, and (3) small trees with medium NDVI values. Using the resulting classification, we divided the wadi into three sections, each representing a unique combination of long- and short-term hydrologic processes affecting the acacia trees. We suggest that the lack of spatial correlation between tree size and health status is a result of spatio-temporal changes in the water supply.