High Normalized Difference Vegetation Index Research Articles

Dynamics of forage and land cover changes in Teltele district of Borana rangelands, southern Ethiopia: using geospatial and field survey data

BackgroundThe gradual conversion of rangelands into other land use types is one of the main challenges affecting the sustainable management of rangelands in Teltele. This study aimed to examine the changes, drivers, trends in land use and land cover (LULC), to determine the link between the Normalized Difference Vegetation Index (NDVI) and forage biomass and the associated impacts of forage biomass production dynamics on the Teltele rangelands in Southern Ethiopia. A Combination of remote sensing data, field interviews, discussion and observations data were used to examine the dynamics of LULC between 1992 and 2019 and forage biomass production.ResultsThe result indicate that there is a marked increase in farm land (35.3%), bare land (13.8%) and shrub land (4.8%), while the reduction found in grass land (54.5%), wet land (69.3%) and forest land (10.5%). The larger change in land observed in both grassland and wetland part was observed during the period from 1995–2000 and 2015–2019, this is due to climate change impact (El-Niño) happened in Teltele rangeland during the year 1999 and 2016 respectively. The quantity of forage in different land use/cover types, grass land had the highest average amount of forage biomass of 2092.3 kg/ha, followed by wetland with 1231 kg/ha, forest land with 1191.3 kg/ha, shrub land with 180 kg/ha, agricultural land with 139.5 kg/ha and bare land with 58.1 kg/ha.ConclusionsThe significant linkage observed between NDVI and LULC change types (when a high NDVI value, the LULC changes also shows positive value or an increasing trend). In addition, NDVI value directly related to the greenness status of vegetation occurred on each LULC change types and its value directly linkage forage biomass production pattern with grassland land use types. 64.8% (grass land), 43.3% (agricultural land), 75.1% (forest land), 50.6% (shrub land), 80.5% (bare land) and 75.5% (wet land) more or higher dry biomass production in the wet season compared to the dry season.

Spatiotemporal Variation and Climate Influence Factors of Vegetation Ecological Quality in the Sanjiangyuan National Park

The Sanjiangyuan National Park is the first Chinese national park system, and the ecological environment is inherently fragile and sensitive. Therefore, for environmental protection, it is imperative to understand the spatiotemporal variation characteristics of the ecological quality of vegetation and its climate influence factors. We used the MODIS normalized difference vegetation index (NDVI) dataset, meteorological dataset, and Carnegie–Ames–Stanford Approach (CASA) model to investigate the spatiotemporal patterns and change trends of the NDVI and the net primary productivity (NPP) of the vegetation in the Sanjiangyuan National Park from 2000 to 2016. A linear regression model was used to explore the influence of the ecological quality of vegetation and climatic factors. The results showed that (1) the NDVI and NPP were high in the southeast area and low in the northwest area. The Yangtze River headwater region had the lowest NDVI (0–0.3) and NPP (0–100 gC/m2). The Lancang River had the highest NDVI (0.4–0.8) and NPP (100–250 gC/m2). (2) From 2000–2016, approximately 23.46% of the area showed a significant positive trend of the NDVI that was mainly distributed in the prairie areas in the midlands and the north of the Yangtze River headwater region, and was scattered in the midlands and the north of Yellow River headwater region. Furthermore, 24.32% of the NPP was determined to have increased significantly, which was mainly distributed in the midlands and the north of the Yangtze River headwater region, as well as the midlands and the east of the Yellow River headwater region. (3) The vegetation growth in the Sanjiangyuan National Park was regulated by both water and heat conditions. The NDVI was significantly affected by precipitation during the growing season and by the annual precipitation. In addition, the NPP was significantly affected by temperature during the growing season and by the annual average temperature of the study area.

Threshold Vegetation Greenness under Water Balance in Different Desert Areas over the Silk Road Economic Belt

The sustainability of dryland vegetation growth over the Silk Road Economic Belt is under threat of water shortage, and the determination of water carrying capacity for vegetation is critically essential to balance water supply and water demand for the maintenance of existing ecosystems. To better understand how and why vegetation growth varies in different desert areas, this study first analyzed the spatiotemporal variation of the normalized difference vegetation index (NDVI). Then, we investigated the relationship between NDVI and climatic factors (precipitation, soil water content, air temperature, evapotranspiration), and estimated the threshold NDVI under water balance in different desert areas. Results showed that the higher NDVI was mainly distributed in Kazakhstan, Russia, and Azerbaijan, and it increased in approximately 53% of desert areas from 1982 to 2015 in the whole study region. The mean annual NDVI showed a simultaneous increasing trend in all desert areas from 1982 to 1994, and decreased significantly only in the cold arid desert area (p < 0.01, −0.0067 decade−1) or had no significant change in other desert areas after 1994 (p > 0.01). The climate condition generally appeared as a warming and drying trend in the past 34 years, with varied changing rates in different desert areas. NDVI presented a strong positive relationship with both precipitation and evapotranspiration in most desert areas. The threshold values of the mean annual NDVI under water balance between 1982 and 2015 were approximately 0.1041 (hot arid desert), 0.1337 (cold arid desert), 0.1346 (cold arid semi-desert), 0.0951 (hot arid desert semi-desert), 0.0776 (polar desert tundra), 0.1071 (hot arid desert shrub), 0.1377 (cold arid desert steppe), and 0.0701 (polar desert steppe), respectively. The responses of these threshold values to precipitation were all positive in different desert areas. These results provide an enhanced understanding of vegetation dynamics and ecological conservation, which are of great importance to implementing adaptation and mitigation measures for terrestrial ecosystems over the Silk Road Economic Belt.

Bangalore: Urban heating or urban cooling?

Bangalore, one of the Indian metropolitan cities, has witnessed rapid urbanization during the last decade with an overwhelming spatial growth of 696 sq. km to 2185 sq.km within a span of ten years (2006 to 2016). With this exponential rate of urban growth, the city is experiencing a shift in its micro- climatic condition. This paper aims to identify the surface urban heat island (SUHI) effect by comparing normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and the fraction of impervious surface area (ISA) as indicators of SUHI. Landsat 8 datasets were used to estimate the land surface temperature (LST) for two different seasons representing winter (January) and summer (April) using radiative transfer algorithm. A spatial map of ISA was generated using normalized spectral mixture analysis (NSMA) showing a standard error of 9.25%. Correlations between NDVI, NDBI and fraction of ISA with LST indicate a strong linear negative relationship between LST and ISA with a coefficient of correlation 0.85 and 0.94 s for January and April respectively and is contrary to the conventionally accepted positive SUHI phenomenon. The relationship between LST and NDVI was in particular negative at areas bearing high NDVI values. Results indicate that ISA is quantitatively a better metric indicating that the city experiences cool island effect indicative of high vegetative cover presence in the Bangalore Urban (BU) area. Also, NDVI is greatly affected by seasonal variation and NDBI shows mixing of soil characters and hence failed in providing conclusive results.

Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images

AbstractThe Aral Sea was one of the largest lakes in the world, but almost 60,000 km2 of the waterbody has dried up due to water withdrawal for irrigation. Afforestation on the desiccated seafloor could be important in preventing soil flation, dust storms, and negative impact on human health. In this study, we aimed to delineate potential vegetation establishment areas on the dried Aral Sea bed using remote‐sensed data in support of the decision‐making related to afforestation. Various indices such as normalized difference vegetation index (NDVI), topsoil grain size index (TGSI), soil salinity index (SSI), and normalized multiband drought index (NMDI) were calculated from the LANDSAT‐8 OLI satellite imagery. As an indicator of vegetation existence, NDVI was classified into three groups and set as a base for classifying other indices by performing statistical analyses. Based on the decision tree method, indices were combined and the potential vegetation establishment area was detected. Higher NDVI was identified in the southeast than the west of the study area. The results of statistical analyses showed that TGSI had a positive correlation with NDVI, while SSI and NMDI had a negative correlation. Overall, the potential vegetation area comprised 7,295.21 km2 (61.34%) of the 'unsuitable' area, 2,818.64 km2 (23.7%) of the 'intermediate' area, 1,612.15 km2 (13.56%) of the 'suitable' area, and 166.42 km2 (1.4%) of the 'very suitable' area. The developed map enables to identify dried seafloor area suitable for vegetation establishment thus contributing to planning the land rehabilitation efforts and preventing further land degradation.

Long-term NDVI and recent vegetation cover profiles of major offshore island nesting sites of sea turtles in Saudi waters of the northern Arabian Gulf

Abstract Vegetation is an important ecological component of offshore islands in the Arabian Gulf (AG), which maintains long-term resilience of these islands. This is achieved by influencing sediment retention and moisture acquisition via condensation during periods of high humidity and by providing a variety of microhabitats for island fauna. The resilience of offshore islands’ ecosystems in the Saudi waters is important because they host the largest number of nesting hawksbill and green turtles in the AG. This study defines the characteristics and the long-term trends in vegetation cover of the offshore islands used by sea turtles as nesting grounds in the northern AG. To establish a ground-validated baseline for vegetation profiles, a 50 m × 50 m grid system is developed on Karan and Jana islands (Is.) with photo-quadrats taken at each grid intersection. The 1,317 and 444 photo-quadrats, for Karan and Jana Is., respectively, were analyzed for maximum plant height and percent cover of living (green) plants, dead plants, and bare sand. Landsat 7 and 8 satellite top-of-atmosphere reflectance images were used to calculate the Normalized Difference Vegetation Index (NDVI) from 1999 through 2018 to analyze the long-term vegetation profiles of the islands. Monthly rainfall data from five meteorological stations along the Eastern Province of Saudi Arabia and Oceanic Nino Index (ONI) are presented to provide a context of the long-term NDVI time series variability. The ground-validated vegetation profiles provided baseline data during the onset of summer in 2017 and revealed differences in maximum plant height and the extent of living, dead vegetation and sand cover on Jana Is. (28.3 cm, 19.9%, 63.3%, and 16.8%) and Karan Is. (21.7 cm, 20.6%, 48.7%, and 30.7%), respectively. The NDVI data for both islands are grouped into three periods, namely: 2001–2007 - high winter, low summer; 2008–2013 – low winter, low summer; 2014–2018 – irregular high/low winter, low summer. The long-term trend showed a slightly decreasing NDVI when compared in the context of the high NDVI measured for the two islands during the early 2000 s, particularly during the winter time. An extended reduction in winter NDVI was recorded for six years from 2008 to 2013, which coincided with reduced rainfall in the region and prolonged La Nina. Five extreme dips in winter NDVI values coincided with strong (2000, 2008, and 2011) and moderate (2012 and 2018) La Nina events. Long-term vegetation profiles of the offshore islands seemed to be tightly coupled with long-term rainfall patterns.

Unpiloted Aerial System (UAS)-Supported Biogeomorphic Analysis of Restored Sierra Nevada Montane Meadows

The benefits of meadow restoration can be assessed by understanding the connections among geomorphology, hydrology, and vegetation; and multispectral imagery captured from unpiloted aerial systems (UASs) can provide the best method in terms of cost, resolution, and support for vegetation indices. Our field studies were conducted on northern Sierra montane meadows (with ≤70 km2 watershed area). The meadows exist in various stages of ecological restoration. Field survey methods included GPS + laser-leveling channel survey, cross-sections, LiDAR, vegetation sampling, soil measurements, and UAS imaging. A sensor captured calibrated blue (465–485 nm), green (550–570 nm), red (663–673 nm), near infrared (NIR) (820–860 nm), and red-edge (712–722 nm) bands at 5.5 cm resolution (as well as thermal at 81 cm resolution) and provided multispectral images and derivative vegetation indices such as the normalized difference vegetation index (NDVI) and red-edge chlorophyll index (Clre). This fine-scale imagery extended our morphometric assessment of post-restoration channel bedform patterns and sinuosity related to Carex-influenced soil properties and Salix influence, and also documented groundwater-related effects via Carex patterns evident from spring snowmelt images, as well as NDVI and Clre (derived from spring and summer images) in growing to senescent phenological stages. Carex was significantly associated with low bulk density and high soil moisture, NDVI, and Clre in low-lying areas, and channel sinuosity was significantly associated with willow influence. Our methods can be applied by restoration managers to assess where projects are threatened by renewed incision and to document levels of carbon sequestration significant to addressing climate change.

VEGETATION INDICES FOR IRRIGATED CORN MONITORING

ABSTRACT Monitoring of large agricultural lands is often hampered by data collection logistics at field level. To solve such a problem, remote sensing techniques have been used to estimate vegetation indices, which can subsidize crop management decision-making. Therefore, this study aimed to select vegetation indices to detect variability in irrigated corn crops. Data were collected in Sao Desiderio, Bahia State (Brazil), using an OLI sensor (Operational Land Imager) embedded to a Landsat-8 satellite platform. Five corn growing plots under central pivot irrigation were assessed. The following vegetation indices were tested: NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio), NDWI (Normalized Difference Water Index), and MSI (Moisture Stress Index). Among the tested indices, SR was more sensitive to high corn biomass, while GNDVI, NDVI, EVI, and SAVI were more sensitive to low values. Overall, all indices were found to be concordant with each other, with high correlations among them. Despite this, the use of a set of these indices is advisable since some respond better to certain peculiarities than others.

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.

Identification of Groundwater Potential Zones Using Analytical Hierarchy Process (AHP) and GIS-Remote Sensing Integration, the Case of Golina River Basin, Northern Ethiopia

Groundwater is the main source of water both for domestic and irrigation purposes in the study area, Golina basin. The Golina basin is located in the North Wollo Administration, Amhara regional state, Northern Ethiopia. The study area has a surface area of 916.77 km 2 and is a semi-arid environment with an annual mean precipitation of 913 mm/year. Because of its presumed groundwater potential, the area has recently received significant attention from the regional and federal governments as well as investors. As a result, 81wells have been drilled for groundwater extraction mainly for irrigation purposes. About 182 springs and hand-dug wells have been developed for domestic purposes. However, little has been done to know the overall groundwater potential, the groundwater potential zonation and the sustainability of the current and planned groundwater extraction in the basin. Thus, the main objective of this work is to delineate groundwater potential zones of the area using an integrated method of AHP, GIS, and Remote sensing. A total of 10 thematic layers are used for the groundwater potential zone assessment. Based on the AHP method, the priority of the thematic layers with their weight are as follows: geology (0.22), geomorphologic landform (0.22), lineament density (0.17), slope (0.12), drainage density (0.09), soil (0.07), land use land cover (0.04), precipitation (0.04), normalized difference vegetation index (NDVI) (0.02) and topographic wetness index (TWI) (0.02). The groundwater potential zones of the area are computed using raster calculator in spatial analyst tool in GIS software and divided into five classes as very poor, poor, moderate, good and very good with area coverage of 93 km 2 (10%), 265km 2 (29%), 175 km 2 (19%), 221 km 2 (24%) and 162 km 2 (18%) of the total area, respectively. The result is validated using the existing spring, hand-dug wells and borehole yields. Accordingly, the high yield boreholes are drilled and concentrated in very good groundwater potential zones whereas the low yield springs are developed in the very poor. Out of 263 water points 74, 72, 48, 59 and 10 water points are located in very good, good, moderated, poor and very poor groundwater potential zones, respectively. Mainly the very good groundwater potential fall in the alluvial deposit, plain and valley landforms, very low slope (flat), high lineament density, low drainage density, coarse soil textures, and water body and cultivated lands with high precipitation, NDVI and TWI. The result of this study can be used for the planning of new groundwater-based projects and the expansion of the existing irrigation project in the basin. DOI: https://doi.org/10.23953/cloud.ijarsg.460

Environmental Influences on Dall's Sheep Survival

ABSTRACTUnderstanding how species respond to environmental conditions can assist with conservation strategies and harvest management, especially in arctic and boreal regions that are experiencing rapid climate change. Although climatic influences on species distributions have been studied, broad‐scale effects of climate on survival are less well known. We examined the interactive effects of meteorological and remotely sensed environmental variables on survival of Dall's sheep (Ovis dalli dalli) lambs and adults by synthesizing radio‐telemetry data across their range. We used data from 9 studies of adult sheep and 2 studies of lambs that were conducted between 1997 and 2012 at sites spanning the species' range in Alaska, USA, and northwestern Canada. We obtained environmental variables throughout the range of Dall's sheep, including the normalized difference vegetation index (NDVI) from optical remote sensing, freeze‐thaw frequency (FTF) from passive microwave remote sensing, and gridded climate variables such as snow water equivalent, temperature, and precipitation. We used Cox proportional hazard regression to investigate the effects of environmental variables recorded during summer, winter, and the previous winter on annual survival rates of Dall's sheep lambs and adults. Summer NDVI was the most influential environmental factor affecting lamb survival, with improved lamb survival occurring in years with a high maximum NDVI. Also, lamb predation by coyotes (Canis latrans) and golden eagles (Aquila chrysaetos) decreased substantially with increasing NDVI. The previous winter FTF had the strongest effect on adult survival, with decreased survival occurring after winters with high FTF. In addition, these remotely sensed environmental factors interacted with meteorological factors to affect survival, such that effects of winter temperature depended on summer NDVI and winter FTF. Warm winters increased lamb survival only when preceded by summers with high NDVI, and warm winters increased adult survival only when winter FTF was low. Thus, potential benefits of climate warming may be counteracted if wintertime freeze‐thaw events markedly increase. Correlations among environmental variables across sites were low, and regional climate cycles such as the Pacific Decadal Oscillation (PDO) had weak effects, indicating substantial local variability in climatic conditions experienced by Dall's sheep across their range. These findings can help managers anticipate how Dall's sheep populations will respond to changes in local environmental conditions. Our results also highlight the utility of multiple remotely sensed environmental conditions for ungulate management, especially passive microwave products that provide valuable information on winter icing events. © 2020 The Wildlife Society.

Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI

One of the obstacles in monitoring agricultural crops is the difficulty in understanding and mapping rapid changes of these crops. With the purpose of addressing this issue, this study aimed to model and fuse the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) using Landsat-like images to achieve daily high spatial resolution NDVI. The study was performed for the period of 2017 on a commercial farm of irrigated maize-soybean rotation in the western region of the state of Bahia, Brazil. To achieve the objective, the following procedures were performed: (i) Landsat-like images were upscaled to match the Landsat-8 spatial resolution (30 m); (ii) the reflectance of Landsat-like images was intercalibrated using the Landsat-8 as a reference; (iii) Landsat-like reflectance images were upscaled to match the MODIS sensor spatial resolution (250 m); (iv) regression models were trained daily to model MODIS NDVI using the upscaled Landsat-like reflectance images (250 m) of the closest day as the input; and (v) the intercalibrated version of the Landsat-like images (30 m) used in the previous step was used as the input for the trained model, resulting in a downscaled MODIS NDVI (30 m). To determine the best fitting model, we used the following statistical metrics: coefficient of determination (r2), root mean square error (RMSE), Nash–Sutcliffe efficiency index (NSE), mean bias error (MBE), and mean absolute error (MAE). Among the assessed regression models, the Cubist algorithm was sensitive to changes in agriculture and performed best in modeling of the Landsat-like MODIS NDVI. The results obtained in the present research are promising and can enable the monitoring of dynamic phenomena with images available free of charge, changing the way in which decisions are made using satellite images.

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.

Changes in stay-green and nitrogen use efficiency traits in historical set of winter barley cultivars

The objective of this study was to compare grain yield, nitrogen use efficiency (NUE) and stay-green traits in winter barley under low and high nitrogen (N) fertilization treatments. Field trials were conducted during two growing seasons in a southern Pannonian location (Novi Sad, Serbia) with fifteen six-rowed barley cultivars released between 1972 and 2013. The rate of genetic gain in grain yield was higher under high N level, indicating that modern barley cultivars use the applied N fertilizer more efficiently than older cultivars. The improvement in NUE and N uptake efficiency were positively related with year of cultivar release under both fertilization levels, while association between N utilization efficiency and year of cultivar release was significant at high N fertilization. Modern cultivars had higher normalized difference vegetation index (NDVI) values at the anthesis, start and mid senescence. Moreover, time to mid senescence was prolonged in modern cultivars, indicating slower N remobilization during early grain filling period. Values of NDVI at different senescence stages were more related to N uptake efficiency, while timing of senescence stages were in association with N utilization. Close association between NDVI values at different senescence stages and N uptake indicate that application of NDVI sensors could be used for identification of cultivars with improved N productivity.

Is Habitat Constraining Bighorn Sheep Restoration? A Case Study

ABSTRACTRocky Mountain bighorn sheep (Ovis canadensis) restoration continues to be a challenge throughout western North America despite nearly a century of efforts dedicated to the species' recovery. Though bighorn sheep restoration may be constrained by several environmental factors and behavioral tendencies, areas with unrealized restoration potential may exist if novel restoration strategies are considered. We used global positioning system (GPS) location data from 27 female bighorn sheep within the southern portion of the Madison Range in southwest Montana, USA, 2015–2017, to develop and validate winter and summer habitat models, which we extrapolated throughout the entire Madison Range to identify potential seasonal habitat. We estimated potential bighorn sheep minimum population estimates within the extrapolation area by linking our top‐ranked winter habitat model to population count data. During summer, female bighorn sheep selected areas characterized by rugged and steep terrain, reduced canopy cover, southwestern aspects, and ridgelines. During winter, female bighorn sheep selected areas characterized by low elevations, southwestern aspects, steep slopes, reduced canopy cover, ridgelines, high normalized difference vegetation index amplitude, and areas close to steep terrain. Predicted summer habitat was concentrated along the high‐elevation ridgelines associated with steep slopes and reduced canopy cover. Predicted winter habitat occurred in a non‐contiguous distribution primarily along the low‐elevation, southwest‐facing aspects along the western slopes of the Madison Range. Our results suggest that the Madison Range may be capable of supporting 780–1,730 animals, which is 2–4 times the number of bighorn sheep currently observed within the range. Further, our findings provide managers with a quantification of female bighorn sheep habitat and suggests that a strategy focused on establishing a metapopulation through a series of within‐range translocations may enhance bighorn sheep restoration. We suggest that similar restoration opportunities may be common in other unoccupied areas of bighorn sheep historical range. © 2020 The Wildlife Society.

A Simple Method to Identify Potential Groundwater-Dependent Vegetation Using NDVI MODIS

The potential groundwater-dependent vegetation (pGDV) in the Iberian Peninsula (IP) was mapped, with a simple method, hereafter referred to as SRS-pGDV, that uses only Normalized Difference Vegetation Index (NDVI) time series retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra V6 product, covering the period February 2000 to April 2018. NDVI was standardized, to minimize the effect of the different land cover types. The extreme drought event of 2004/2005 was used to perform the classification. Considering the water scarcity that affected vegetation in the IP during this event, it was postulated that vegetation showing a high standardized NDVI should be classified as pGDV. Irrigated vegetation and areas with sparse vegetation were eliminated. A cluster analysis was performed, in order to classify the pixels as more/less likely to be pGDV. The results obtained were compared with modeled water table depth, and a propensity of pixels identified as pGDV in areas with low water table depth was clearly observed. However, based on CORINE Land Cover types, some areas identified as pGDV are likely irrigated, such as fruit-tree plantations; this inference is in line with the postulated criterion of vegetation access to sources of water other than precipitation. SRS-pGDV could also be applied to regional studies, using NDVI with a higher spatial resolution.

Consequences of climate-induced vegetation changes exceed those of human disturbance for wild impala in the Serengeti ecosystem.

In East Africa, climate change is predicted to reduce vegetation quality, and pervasive human disturbance has already resulted in significant declines in biodiversity. We studied the combined effects of reduced forage quality and human disturbance on faecal glucocorticoid metabolite (FGM) concentrations. We predicted that decreasing nutritional quality and increasing human disturbance would have an additive positive effect on FGM levels in wild impala (Aepyceros melampus). Employing a space-for-time approach, we used normalized difference vegetation index (NDVI) as a measure of forage quality, combined with spatially explicit proxies of human disturbance across areas of different protection management strategies in the Serengeti ecosystem. We collected 639 faecal samples, spread over 4years, including both wet and dry seasons. Impala FGM levels increased significantly with declining NDVI and, to a lesser extent, with increasing proxies for human disturbance. However, we found no interaction between the two, such that impala had elevated FGM levels with low NDVI and low FGM levels with high NDVI regardless of human disturbance levels. This implies that impala will have high FGM levels if forage quality is poor, even with significant protection and reduced human disturbance. Understanding how animals respond to and cope with changes in forage quality and human land use across different protected areas is important for conservationists and managers to better protect species at risk and predict population viability.

Habitat choices of African buffalo (Syncerus caffer) and plains zebra (Equus quagga) in a heterogeneous protected area

Abstract ContextAn understanding of large herbivore habitat choices in heterogeneous African protected areas is important for the better management of these key ecosystems. AimsTo determine habitat use of African buffalo (Syncerus caffer) and plains zebra (Equus quagga) in a heterogeneous protected area. MethodsZambezi National Park (ZNP), Zimbabwe, was divided into five vegetation types using an unsupervised classification on a Landsat satellite image that was classified into five land cover classes, using the K-means classification algorithm. African buffalo and plains zebra densities were then determined in each vegetation type using road transect surveys monthly between January 2013 and December 2015. Normalised difference vegetation index (NDVI), grass biomass, grass height and grass quality (nitrogen, calcium, phosphorus and acid detergent fibre content) were determined in each vegetation type during the wet (November to April) and dry (August to October) seasons to establish their quality as habitats for African buffalo and plains zebra. Key resultsBoth African buffalo and plains zebra mostly foraged in mixed and grassland areas, and avoided Zambezi teak vegetation type. Zambezi teak vegetation type had high NDVI due to the dense tree cover. Both African buffalo and plains zebra preferred vegetation types with intermediate grass biomass (approximately 300gm−2) and grass height (approximately 16cm). Grass nutritive value (in terms of nitrogen, phosphorus and acid detergent fibre) declined from wet to dry season in all vegetation types. ConclusionsAfrican buffalo and plains zebra in the ZNP confined their habitat use mostly to two vegetation types (mixed and grassland), which together covered 25% of the protected area. ImplicationsTeak (Baikiaea plurijuga) vegetation, which accounted for about 60% of the ZNP, was avoided by both African buffalo and plains zebra, suggesting that a significant part of the protected area was not used by the two herbivores.

MAPPING OF SARGASSUM DISTRIBUTION IN THE EASTERN COAST OF SOUTHERN LEYTE USING SENTINEL 2 SATELLITE IMAGERY

Abstract. Sargassum is a brown seaweed distributed in the Philippines and recognized as an additional source of income for fishing communities. Due to uncontrolled harvesting of the seaweed, the Department of Agriculture regulated its collection and harvesting by imposing seasonal restrictions. Hence, the need to identify the locations and cover of healthy Sargassum is vital to address the demand in the market while maintaining ecological balance in the marine ecosystem. Two Sentinel-2 satellite imagery (10 m resolution) acquired on December 08, 2017 (peak growth) and May 27, 2018 (senescence stage) were used to map the presence of Sargassum in the eastern coast of Southern Leyte. Supervised classification using maximum likelihood algorithm and accuracy assessment were conducted before generating the map. Three classes were considered namely Sargassum, clouds and land. Furthermore, Anselin Local Moran’s I (cluster and outlier analysis) was conducted to determine which areas have significant clustering of “healthy” Sargassum using the normalized difference vegetation index (NDVI). For both image dates, high classification accuracies of Sargassum were obtained in the islands. However, there are misclassifications of Sargassum in Silago (UA = 78.72%) and Hinunangan (PA = 82.35%) using the May image. Furthermore, misclassification of Sargassum were obtained in Silago (PA = 93.6%) and Hinundayan (PA = 96.23%) using the December image. Clusters of high NDVI values are more evident in December. Healthy Sargassum are apparent in the coast of Silago and mostly found near shore and in rocky substrates.

Increasing interannual variability of global vegetation greenness

Despite the long-term greening trend in global vegetation identified in previous investigations, changes in the interannual variability (IAV) of vegetation greenness over time is still poorly understood. Using Global Inventory Modeling and Mapping Studies normalized difference vegetation index (NDVI) third generation data and corresponding meteorological data from 1982 to 2015, we studied the changes and drivers of the IAV of vegetation greenness as indicated by the coefficient of variation of vegetation greenness at a global scale. Dry and high-latitude areas exhibited high NDVI variability whereas humid areas exhibited relatively low NDVI variability. We detected an increase in the global IAV of vegetation greenness over time using a 15 year moving window. Spatially, we observed significant increases in the IAV of vegetation greenness in greater than 45% of vegetated areas globally and decreases in 21%. Our comparison of ecological models suggests good performance in terms of simulating spatial differences in vegetation variability, but relatively poor performance in terms of capturing changes in the IAV of vegetation greenness. Furthermore, the dominant climate variables controlling changes in the IAV of vegetation greenness were determined spatially using principal component regression and partial least squares regression. The two methods yielded similar patterns, revealing that temperature exerted the biggest influence on changes in the IAV of vegetation greenness, followed by solar radiation and precipitation. This study provides insights into global vegetation variability which should contribute to an understanding of vegetation dynamics in the context of climate change.