Farming systems in two less favoured areas in portugal: their development from 1989 to 2009 and the implications for sustainable land management
Jones, N.M. ; Graaff, J. de; Duarte, F. ; Rodrigo, I. ; Poortinga, A. - \ 2014
Land Degradation and Development 25 (2014)1. - ISSN 1085-3278 - p. 29 - 44.
agricultural soil conservation - policy measures - european-union - consequences - degradation - typology - spain - ndvi
Since the late 1980s, sustainable land management is one of the objectives of the European Commission in Less Favoured Areas. In this paper, we investigate the economic and environmental sustainability of farming systems in two less favoured areas in Centro and Alentejo areas of Portugal. The specific objectives were the following: (i) to characterise the farming systems; (ii) to analyse their development over a 20-year period (1989-2009); and (iii) to investigate to what extent these farming systems contribute to sustainable land management. The diversity of the farming systems was identified through a survey and cluster analysis and compared with the Farm Accountancy Data Network classification on types of farming. Indicators on the economic and environmental sustainability were estimated, namely, farm net income, return to labour and rotation management, on the basis of a survey, Farm Accountancy Data Network database and Landsat imagery, respectively. Results indicate an increased focus on livestock in the past 20years (1989-2009). In Centro, rotation management was not affected. The small ruminant farms have been able to retain a positive farm net income but that was only possible with a below average return to labour. In Alentejo, the increased focus on livestock, cattle in particular, led to an intensification of fodder production on certain plots. Mixed crop-livestock farms show a negative farm net income since 1995 and depend heavily on subsidies to remain viable. As other studies in southern Europe have shown, farm strategies have often been directed towards lowering labour inputs, lowering forage deficits through on-farm produced resources and acquiring subsidies. Copyright (c) 2013 John Wiley & Sons, Ltd.
A cost-effective approach for improving the quality of soil sealing change detection from Landsat imagery
Smiraglia, D. ; Rinaldo, S. ; Ceccarelli, T. ; Bajocco, S. ; Salvati, L. ; Ricotta, C. ; Perini, L. - \ 2014
European Journal of Remote Sensing 47 (2014). - ISSN 2279-7254 - p. 805 - 819.
urban - modis - tm - transformation - segmentation - phenology - sprawl - region - ndvi - area
The aim of this study is to develop a cost-effective approach for soil sealing change detection integrating radiometric analysis, multi-resolution segmentation and object-based classifiers in two study areas in Italy: Campania region and Veneto region. The integrated approach uses multi-temporal satellite images and CORINE Land Cover (CLC) maps. A good overall accuracy was obtained for the soil sealing maps produced. The results show an improvement in terms of size of the minimum mapping unit and of the changed object (1,44 ha in both cases) in respect to the CLC. The approach proves to be cost-effective given the data which are provided at low or no cost and as well as the level of automation achievable.
Trends in spring phenology of Western European deciduous forests
Hamunyela, E. ; Verbesselt, J. ; Roerink, G.J. ; Herold, M. - \ 2013
Remote Sensing 5 (2013)12. - ISSN 2072-4292 - p. 6159 - 6179.
image time-series - green-up date - climate-change - temporal resolution - plant phenology - vegetation - ndvi - responses - season - china
Plant phenology is changing because of recent global warming, and this change may precipitate changes in animal distribution (e.g., pests), alter the synchronization between species, and have feedback effects on the climate system through the alteration of biogeochemical and physical processes of vegetated land surface. Here, ground observations (leaf unfolding/first leaf separation of six deciduous tree species) and satellite-derived start-of-growing season (SOS) are used to assess how the timing of leafing/SOS in Western European deciduous forest responded to climate variability between 2001 and 2011 and evaluate the reliability of satellite SOS estimates in tracking the response of forest leafing to climate variability in this area. Satellite SOS estimates are derived from the Normalized Difference Vegetation Index (NDVI) time series of the Moderate Resolution Imaging Spectroradiometer (MODIS). Temporal trends in the SOS are quantified using linear regression, expressing SOS as a function of time. We demonstrated that the growing season was starting earlier between 2001 and 2011 for the majority of temperate deciduous forests in Western Europe, possibly influenced by regional spring warming effects experienced during the same period. A significant shift of up to 3 weeks to early leafing was found in both ground observations and satellite SOS estimates. We also show that the magnitude and trajectory of shifts in satellite SOS estimates are well comparable to that of in situ observations, hence highlighting the importance of satellite imagery in monitoring leaf phenology under a changing climate
Relationships between declining summer sea ice, increasing temperatures and changing vegetation in the Siberian Arctic tundra from MODIS time series (2000–11)
Dutrieux, L.P. ; Bartholomeus, H. ; Herold, M. ; Verbesselt, J. - \ 2012
Environmental Research Letters 7 (2012)4. - ISSN 1748-9326 - 12 p.
climate-change - shrub expansion - high-latitudes - ndvi - responses - amplification - ecosystems - community - carbon - alaska
The concern about Arctic greening has grown recently as the phenomenon is thought to have significant influence on global climate via atmospheric carbon emissions. Earlier work on Arctic vegetation highlighted the role of summer sea ice decline in the enhanced warming and greening phenomena observed in the region, but did not contain enough details for spatially characterizing the interactions between sea ice, temperature and vegetation photosynthetic absorption. By using 1 km resolution data from the Moderate Resolution Imaging Spectrometer (MODIS) as a primary data source, this study presents detailed maps of vegetation and temperature trends for the Siberian Arctic region, using the time integrated normalized difference vegetation index (TI-NDVI) and summer warmth index (SWI) calculated for the period 2000-11 to represent vegetation greenness and temperature respectively. Spatio-temporal relationships between the two indices and summer sea ice conditions were investigated with transects at eight locations using sea ice concentration data from the Special Sensor Microwave/Imager (SSM/I). In addition, the derived vegetation and temperature trends were compared among major Arctic vegetation types and bioclimate subzones. The fine resolution trend map produced confirms the overall greening (+1% yr(-1)) and warming (+0.27% yr(-1)) of the region, reported in previous studies, but also reveals browning areas. The causes of such local decreases in vegetation, while surrounding areas are experiencing the opposite reaction to changing conditions, are still unclear. Overall correlations between sea ice concentration and SWI as well as TI-NDVI decreased in strength with increasing distance from the coast, with a particularly pronounced pattern in the case of SWI. SWI appears to be driving TI-NDVI in many cases, but not systematically, highlighting the presence of limiting factors other than temperature for plant growth in the region. Further unravelling those limiting factors constitutes a priority in future research. This study demonstrates the use of medium resolution remotely sensed data for studying the complexity of spatio-temporal vegetation dynamics in the Arctic.
Near real-time disturbance detection using satellite image time series
Verbesselt, J.P. ; Zeileis, A. ; Herold, M. - \ 2012
Remote Sensing of Environment 123 (2012). - ISSN 0034-4257 - p. 98 - 108.
land-surface phenology - monitoring structural-change - terrestrial ecosystems - vegetation indexes - ndvi - dynamics - patterns - exchange - models
Near real-time monitoring of ecosystem disturbances is critical for rapidly assessing and addressing impacts on carbon dynamics, biodiversity, and socio-ecological processes. Satellite remote sensing enables cost-effective and accurate monitoring at frequent time steps over large areas. Yet, generic methods to detect disturbances within newly captured satellite images are lacking. We propose a multi-purpose time-series-based disturbance detection approach that identifies and models stable historical variation to enable change detection within newly acquired data. Satellite image time series of vegetation greenness provide a global record of terrestrial vegetation productivity over the past decades. Here, we assess and demonstrate the method by applying it to (1) simulated time series of vegetation greenness data from satellite data, (2) real-world satellite greenness image time series between February 2000 and July 2011 covering Somalia to detect drought-related vegetation disturbances. First, simulation results illustrate that disturbances are successfully detected in near real-time while being robust to seasonality and noise. Second, major drought-related disturbance corresponding with most drought-stressed regions in Somalia are detected from mid-2010 onwards. The method can analyse in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds and does not require time series gap filling. While the data and methods used are appropriate for proof-of-concept development of global scale disturbance monitoring, specific applications (e.g., drought or deforestation monitoring) mandate integration within an operational monitoring framework
Linear trends in seasonal vegetation time series and the modifiable temporal unit problem
Jong, R. de; Bruin, S. de - \ 2012
Biogeosciences 9 (2012). - ISSN 1726-4170 - p. 71 - 77.
proxy global assessment - land degradation - sahel - environment - america - modis - ndvi
Time series of vegetation indices (VI) derived from satellite imagery provide a consistent monitoring system for terrestrial plant productivity. They enable detection and quantification of gradual changes within the time frame covered, which are of crucial importance in global change studies, for example. However, VI time series typically contain a strong seasonal signal which complicates change detection. Commonly, trends are quantified using linear regression methods, while the effect of serial autocorrelation is remediated by temporal aggregation over bins having a fixed width. Aggregating the data in this way produces temporal units which are modifiable. Analogous to the well-known Modifiable Area Unit Problem (MAUP), the way in which these temporal units are defined may influence the fitted model parameters and therefore the amount of change detected. This paper illustrates the effect of this Modifiable Temporal Unit Problem (MTUP) on a synthetic data set and a real VI data set. Large variation in detected changes was found for aggregation over bins that mismatched full lengths of vegetative cycles, which demonstrates that aperiodicity in the data may influence model results. Using 26 yr of VI data and aggregation over full-length periods, deviations in VI gains of less than 1% were found for annual periods (with respect to seasonally adjusted data), while deviations increased up to 24% for aggregation windows of 5 yr. This demonstrates that temporal aggregation needs to be carried out with care in order to avoid spurious model results.
Managing climatic risks for enhanced food security: Key information capabilities
Balaghi, R. ; Badjeck, M.C. ; Bakari, D. ; Pauw, E.D. de; Wit, A.J.W. de; Defourny, P. ; Donato, S. ; Gommes, R. ; Jlibene, M. ; Ravelo, A.C. ; Sivakumar, M.V.K. ; Telahigue, N. ; Tychon, B. - \ 2010
Procedia Environmental Sciences 1 (2010). - ISSN 1878-0296 - p. 313 - 323.
prediction - agriculture - ndvi - fisheries - impacts - models
Food security is expected to face increasing challenges from climatic risks that are more and more exacerbated by climate change, especially in the developing world. This document lists some of the main capabilities that have been recently developed, especially in the area of operational agroclimatology, for an efficient use of natural resources and a better management of climatic risks. Many countries, including the developing world, now benefit from well-trained staff in the use of climate data, physical and biological information and knowledge to reduce negative climate impacts. A significant volume of data and knowledge about climate–agriculture relationships is now available and used by students, scientists, technicians, agronomists, decision-makers and farmers alike, particularly in the areas of climate characterization, land suitability and agroecological zoning, seasonal climate forecasts, drought early warning systems and operational crop forecasting systems. Climate variability has been extensively modelled, capturing important features of the climate through applied statistical procedures, agroclimatic indices derived from raw climatic data and from remote sensing. Predictions of climate at seasonal to interannual timescales are helping decision-makers in the agricultural sector to deal more effectively with the effects of climate variability. Land suitability and agroclimatic zoning have been used in many countries for agricultural planning, thanks to the availability of new and comprehensive methodologies; developments in climate, soil and remote sensing data collection and analysis; and improved applications in geographic information systems (GIS). Drought early warning systems are available worldwide at both national and international levels. These systems are helping decision-makers and farmers to take appropriate decisions to adapt to short-term climatic risks. Also, operational crop forecasting systems are now becoming available at the regional and national levels. In some developed countries, several efficient and well tested tools are now available for optimizing on-farm decisions based on the combination of crop simulation models and seasonal forecasts. However, in developing countries few tools have been developed to efficiently manage crops at the farm level to cope with climate variability and climate risks. Climate change impacts on agriculture and food security have been assessed in international studies using specific and efficient methodologies and tools. Adaptation to climate change and variability can also be facilitated through effective planning and implementation of strategies at the political level. The role of technological progress, risk transfer mechanisms and financial instruments and their easy accessibility to rural people are critical elements of climate risk management.
Comparing direct image and wavelet transform-based approaches to analysing remote sensing imagery for predicting wildlife distribution
Murwira, A. ; Skidmore, A.K. - \ 2010
International Journal of Remote Sensing 31 (2010)24. - ISSN 0143-1161 - p. 6425 - 6440.
spatial heterogeneity - species richness - patterns - vegetation - elephants - landscape - movements - ndvi
In this study we tested the ability to predict the probability of elephant (Loxodonta africana) presence in an agricultural landscape of Zimbabwe based on three methods of measuring the spatial heterogeneity in vegetation cover, where vegetation cover was measured using the Landsat Thematic Mapper (TM)-derived normalized difference vegetation index (NDVI). The three methods of measuring spatial heterogeneity were: one wavelet-derived spatial heterogeneity measure; and two direct image measures. The wavelet-derived spatial heterogeneity measure consists of the intensity, which measures the maximum contrast in the vegetation cover, and the dominant scale, which determines the scale at which this intensity occurs. The two direct image measures use the NDVI average and the NDVI coefficient of variation (NDVIcv). The results show that the wavelet-derived spatial heterogeneity significantly explains 80% of the variance in elephant presence compared with 60% and 48% variance explained by the NDVI average and NDVIcv, respectively. We conclude that the wavelet transform-based approach predicts elephant distribution better than the direct image measures of spatial heterogeneity
Detecting trend and seasonal changes in satellite image time series
Verbesselt, J. ; Hyndman, R. ; Newnham, G. ; Culvenor, D. - \ 2010
Remote Sensing of Environment 114 (2010)1. - ISSN 0034-4257 - p. 106 - 115.
forest disturbance detection - structural-change models - land-cover change - temporal-resolution - spatial-resolution - vegetation indexes - tasseled cap - modis - ndvi - ecosystems
A wealth of remotely sensed image time series covering large areas is now available to the earth science community. Change detection methods are often not capable of detecting land cover changes within time series that are heavily influenced by seasonal climatic variations. Detecting change within the trend and seasonal components of time series enables the classification of different types of changes. Changes occurring in the trend component often indicate disturbances (e.g. fires, insect attacks), while changes occurring in the seasonal component indicate phenological changes (e.g. change in land cover type). A generic change detection approach is proposed for time series by detecting and characterizing Breaks For Additive Seasonal and Trend (BFAST). BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within time series. BFAST iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. We tested BFAST by simulating 16-day Normalized Difference Vegetation Index (NDVI) time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes. This revealed that BFAST can robustly detect change with different magnitudes (> 0.1 NDVI) within time series with different noise levels (0.01–0.07 s) and seasonal amplitudes (0.1–0.5 NDVI). Additionally, BFAST was applied to 16-day NDVI Moderate Resolution Imaging Spectroradiometer (MODIS) composites for a forested study area in south eastern Australia. This showed that BFAST is able to detect and characterize spatial and temporal changes in a forested landscape. BFAST is not specific to a particular data type and can be applied to time series without the need to normalize for land cover types, select a reference period, or change trajectory. The method can be integrated within monitoring frameworks and used as an alarm system to flag when and where changes occur
Effects of plant phenology and solar radiation on seasonal movement of golden takin in the Qinling Mountains, China
Zeng, Z.G. ; Beck, P.S.A. ; Wang, T. ; Skidmore, A.K. ; Song, Y.L. ; Gong, H.S. ; Prins, H.H.T. - \ 2010
Journal of Mammalogy 91 (2010)1. - ISSN 0022-2372 - p. 92 - 100.
satellite sensor data - white-tailed deer - time-series - vegetation dynamics - body-weight - migration - habitat - range - ndvi - fennoscandia
The golden takin (Budorcas taxicolor bedfordi) is a large, forest-dwelling ungulate endemic to the Qinling Mountains, China. A recent study showed that golden takin move to different elevations depending on the season, remaining at high elevations in summer, intermediate elevations in winter, and at low elevations for short periods in spring and autumn. We proposed the following hypotheses: seasonal movement of golden takin is a response to a shift in vegetation phenology, which affects forage quality; and uphill movement of golden takin in winter is an adaptation to select areas with higher thermal energy. To test the 1st hypothesis we used relative phenological development derived from the normalized difference vegetation index time series to link seasonal shifts in vegetation phenology to movement patterns of golden takin. Golden takin descended to a low elevation with the greening of vegetation in early spring, ascended to a high elevation in late spring, and descended again in autumn as vegetation senesced. To test the 2nd hypothesis we compared thermal energy in the winter habitat with that in other areas of the home range, using the amount of solar radiation calculated by a solar radiation model. In winter, preference of the golden takin for exposed southern slopes at intermediate elevations correlated closely with areas of higher solar radiation. Our results indicate that solar radiation and vegetation phenology are critical factors in driving seasonal movement of golden takin.
Regional estimation of daily to annual regional evapotranspiration with MODIS data in the Yellow River Delta wetland
Jia, L. ; Xi, G. ; Liu, S. ; Huang, C. ; Yan, Y. ; Liu, G. - \ 2009
Hydrology and Earth System Sciences 13 (2009)10. - ISSN 1027-5606 - p. 1775 - 1787.
surface-energy balance - time-series - vegetation - soil - algorithm - index - ndvi - temperature - evaporation - roughness
Evapotranspiration (ET) from the wetland of the Yellow River Delta (YRD) is one of the important components in the water cycle, which represents the water consumption by the plants and evaporation from the water and the non-vegetated surfaces. Reliable estimates of the total evapotranspiration from the wetland is useful information both for understanding the hydrological process and for water management to protect this natural environment. Due to the heterogeneity of the vegetation types and canopy density and of soil water content over the wetland (specifically over the natural reserve areas), it is difficult to estimate the regional evapotranspiration extrapolating measurements or calculations usually done locally for a specific land cover type. Remote sensing can provide observations of land surface conditions with high spatial and temporal resolution and coverage. In this study, a model based on the Energy Balance method was used to calculate daily evapotranspiration (ET) using instantaneous observations of land surface reflectance and temperature from MODIS when the data were available on clouds-free days. A time series analysis algorithm was then applied to generate a time series of daily ET over a year period by filling the gaps in the observation series due to clouds. A detailed vegetation classification map was used to help identifying areas of various wetland vegetation types in the YRD wetland. Such information was also used to improve the parameterizations in the energy balance model to improve the accuracy of ET estimates. This study showed that spatial variation of ET was significant over the same vegetation class at a given time and over different vegetation types in different seasons in the YRD wetland
Displaying remotely sensed vegetation dynamics along natural gradients for ecological studies
Beck, P.S.A. ; Wang, T.J. ; Skidmore, A.K. ; Liu, X.H. - \ 2008
International Journal of Remote Sensing 29 (2008)14. - ISSN 0143-1161 - p. 4277 - 4283.
plant phenology - noaa-avhrr - ndvi - climate - fennoscandia - variability - dataset - series - season - cover
Normalized difference vegetation index (NDVI) datasets are growing in popularity to represent vegetation dynamics in ecological studies. Because of its multidimensional nature, it is difficult to visualise the spatial and temporal components of NDVI datasets simultaneously. This letter presents a method to display vegetation dynamics as captured by the NDVI along natural gradients and to visualise and test correlations between vegetation phenology and animal movement.
Upscaling leaf area index in an Arctic landscape through multiscale observations
Williams, M. ; Bell, R. ; Spadavecchia, L. ; Street, L.E. ; Wijk, M.T. van - \ 2008
Global Change Biology 14 (2008)7. - ISSN 1354-1013 - p. 1517 - 1530.
wet sedge - vegetation - tundra - model - fertilization - ecosystems - dynamics - exchange - scale - ndvi
Monitoring and understanding global change requires a detailed focus on upscaling, the process for extrapolating from the site-specific scale to the smallest scale resolved in regional or global models or earth observing systems. Leaf area index (LAI) is one of the most sensitive determinants of plant production and can vary by an order of magnitude over short distances. The landscape distribution of LAI is generally determined by remote sensing of surface reflectance (e.g. normalized difference vegetation index, NDVI) but the mismatch in scales between ground and satellite measurements complicates LAI upscaling. Here, we describe a series of measurements to quantify the spatial distribution of LAI in a sub-Arctic landscape and then describe the upscaling process and its associated errors. Working from a fine-scale harvest LAI¿NDVI relationship, we collected NDVI data over a 500 m × 500 m catchment in the Swedish Arctic, at resolutions from 0.2 to 9.0 m in a nested sampling design. NDVI scaled linearly, so that NDVI at any scale was a simple average of multiple NDVI measurements taken at finer scales. The LAI¿NDVI relationship was scale invariant from 1.5 to 9.0 m resolution. Thus, a single exponential LAI¿NDVI relationship was valid at all these scales, with similar prediction errors. Vegetation patches were of a scale of 0.5 m and at measurement scales coarser than this, there was a sharp drop in LAI variance. Landsat NDVI data for the study catchment correlated significantly, but poorly, with ground-based measurements. A variety of techniques were used to construct LAI maps, including interpolation by inverse distance weighting, ordinary Kriging, External Drift Kriging using Landsat data, and direct estimation from a Landsat NDVI¿LAI calibration. All methods produced similar LAI estimates and overall errors. However, Kriging approaches also generated maps of LAI estimation error based on semivariograms. The spatial variability of this Arctic landscape was such that local measurements assimilated by Kriging approaches had a limited spatial influence. Over scales >50 m, interpolation error was of similar magnitude to the error in the Landsat NDVI calibration. The characterisation of LAI spatial error in this study is a key step towards developing spatio-temporal data assimilation systems for assessing C cycling in terrestrial ecosystems by combining models with field and remotely sensed data.
Monitoring change in the spatial heterogeneity of vegetation cover in an African savanna
Murwira, A. ; Skidmore, A.K. - \ 2006
International Journal of Remote Sensing 27 (2006)11. - ISSN 0143-1161 - p. 2255 - 2269.
ndvi - ecology - pattern
The extent to which a new intensity-dominant scale approach to characterizing spatial heterogeneity from remote sensing imagery can be used to monitor two-dimensional changes (i.e. variability and patch size) in the spatial heterogeneity of vegetation cover (estimated from a Landsat Thematic Mapper (TM)-derived Normalized Difference Vegetation Index (NDVI)) was tested in the Sebungwe region in north-western Zimbabwe between 1984 and 1992. Intensity of spatial heterogeneity (i.e. the maximum variance obtained when a spatially distributed landscape property is measured with a successively increasing window size) was used to measure variability in vegetation cover. Dominant scale of spatial heterogeneity (i.e. the window size at which the maximum variance in the landscape property is measured) was used to measure the dominant patch dimension of vegetation cover. This approach was validated by testing whether the observed change in the dominant scale and intensity of spatial heterogeneity of vegetation cover between 1984 and 1992 was related to changes in the proportion of arable fields. The results also indicated that there was a significant relationship (p <0.05) between changes in the proportion of agricultural fields and changes in the intensity and the product of intensity and dominant scale of spatial heterogeneity (intensity x dominant scale), suggesting that the new approach captures observable changes in the landscape, and is not an artefact of the data. The results imply that the intensity-dominant scale approach to quantifying spatial heterogeneity in remote sensing imagery can be used for a comprehensive characterization and monitoring of changes in landscape condition.
Remotely sensed habitat indicators for predicting distribution of impala Aepyceros melampus in the Okavango Delta, Botswana
Bommel, F.P.J. van; Heitkönig, I.M.A. ; Epema, G.F. ; Ringrose, S. ; Veenendaal, E.M. - \ 2006
Journal of Tropical Ecology 22 (2006)1. - ISSN 0266-4674 - p. 101 - 110.
availability data - species richness - ecosystem - bird - ndvi
We studied the spatial and temporal habitat use of impala in Botswana's Okavango Delta at landscape level with the aid of satellite imagery, with minimal fieldwork. We related remotely sensed vegetation to impala habitat preferences, by first distinguishing three vegetation types through a multi-temporal classification, and dividing these into subclasses on the basis of their Normalized Difference Vegetation Index (NDVI). This indicator for abundance and greenness of biomass was assessed for wet and dry season separately. Similarly, habitat use was assessed for both seasons by allocating vegetation classes to bimonthly impala observations. Impala distribution patterns coincided with NDVI-based subclasses of the landscape, nested within broad vegetation types, to which impala did not show a marked seasonal response. We suggest that this methodology, using limited field data, offers a functional habitat classification for sedentary herbivores, which appears particularly valuable for application in extensive areas with high spatial variability, but with restricted access.
The response of elephants to the spatial heterogeneity of vegetation in a Southern African agricultural landscape
Murwira, A. ; Skidmore, A.K. - \ 2005
Landscape Ecology 20 (2005)2. - ISSN 0921-2973 - p. 217 - 234.
ecology - pattern - habitat - zimbabwe - forest - scale - ndvi - classification - movements - ecosystem
Based on the agricultural landscape of the Sebungwe in Zimbabwe, we investigated whether and how the spatial distribution of the African elephant (Loxodonta africana) responded to spatial heterogeneity of vegetation cover based on data of the early 1980s and early 1990s. We also investigated whether and how elephant distribution responded to changes in spatial heterogeneity between the early 1980s and early 1990s. Vegetation cover was estimated from a normalised difference vegetation index (NDVI). Spatial heterogeneity was estimated from a new approach based on the intensity (i.e., the maximum variance exhibited when a spatially distributed landscape property such as vegetation cover is measured with a successively increasing window size or scale) and dominant scale (i.e., the scale or window size at which the intensity is displayed). We used a variogram to quantify the dominant scale (i.e., range) and intensity (i.e., sill) of NDVI based congruent windows (i.e., 3.84 km × 3.84 km in a 61 km × 61 km landscape). The results indicated that elephants consistently responded to the dominant scale of spatial heterogeneity in a unimodal fashion with the peak elephant presence occurring in environments with dominant scales of spatial heterogeneity of around 457-734 m. Both the intensity and dominant scale of spatial heterogeneity predicted 65 and 68% of the variance in elephant presence in the early 1980s and in the early 1990s respectively. Also, changes in the intensity and dominant scale of spatial heterogeneity predicted 61% of the variance in the change in elephant distribution. The results imply that management decisions must take into consideration the influence of the levels of spatial heterogeneity on elephants in order to ensure elephant persistence in agricultural landscapes
Optical instruments for measuring leaf area index in low vegetation: application in arctic ecosystems
Wijk, M.T. van; Williams, M. - \ 2005
Ecological Applications 15 (2005)4. - ISSN 1051-0761 - p. 1462 - 1470.
tussock tundra - carbon-dioxide - exchange - productivity - responses - light - ndvi - lai
Leaf area index (LAI) is a powerful diagnostic of plant productivity. Despite the fact that many methods have been developed to quantify LAI, both directly and indirectly, leaf area index remains difficult to quantify accurately, owing to large spatial and temporal variability. The gap-fraction technique is widely used to estimate the LAI indirectly. However, for low-stature vegetation, the gap-fraction sensor either cannot get totally underneath the plant canopy, thereby missing part of the leaf area present, or is too close to the individual leaves of the canopy, which leads to a large distortion of the LAI estimate. We set out to develop a methodology for easy and accurate nondestructive assessment of the variability of LAI in low-stature vegetation. We developed and tested the methodology in an arctic landscape close to Abisko, Sweden. The LAI of arctic vegetation could be estimated accurately and rapidly by combining field measurements of canopy reflectance (NDVI) and light penetration through the canopy (gap-fraction analysis using a LI-COR LAI-2000). By combining the two methodologies, the limitations of each could be circumvented, and a significantly increased accuracy of the LAI estimates was obtained. The combination of an NDVI sensor for sparser vegetation and a LAI-2000 for denser vegetation could explain 81% of the variance of LAI measured by destructive harvest. We used the method to quantify the spatial variability and the associated uncertainty of leaf area index in a small catchment area
Narrow band vegetation indices overcome the saturation problem in biomass estimation
Mutanga, O. ; Skidmore, A.K. - \ 2004
International Journal of Remote Sensing 25 (2004)19. - ISSN 0143-1161 - p. 3999 - 4014.
hyperspectral brdf data - red edge position - leaf-area index - spectral reflectance - chlorophyll content - classification - canopies - grass - ndvi
Remotely sensed vegetation indices such as NDVI, computed using the red and near infrared bands have been used to estimate pasture biomass. These indices are of limited value since they saturate in dense vegetation. In this study, we evaluated the potential of narrow band vegetation indices for characterizing the biomass of Cenchrus ciliaris grass measured at high canopy density. Three indices were tested: Modified Normalized Difference Vegetation Index (MNDVI), Simple Ratio (SR) and Transformed Vegetation Index (TVI) involving all possible two band combinations between 350 nm and 2500 nm. In addition, we evaluated the potential of the red edge position in estimating biomass at full canopy cover. Results indicated that the standard NDVI involving a strong chlorophyll absorption band in the red region and a near infrared band performed poorly in estimating biomass ( R(2) = 0.26). The MNDVIs involving a combination of narrow bands in the shorter wavelengths of the red edge ( 700 - 750 nm) and longer wavelengths of the red edge ( 750 - 780 nm), yielded higher correlations with biomass ( mean R(2) = 0.77 for the highest 20 narrow band NDVIs). When the three vegetation indices were compared, SR yielded the highest correlation coefficients with biomass as compared to narrow band NDVI and TVI ( average R(2) = 0.80, 0.77 and 0.77 for the first 20 ranked SR, NDVI and TVI respectively). The red edge position yielded comparable results to the narrow band vegetation indices involving the red edge bands. These results indicate that at high canopy density, pasture biomass may be more accurately estimated by vegetation indices based on wavelengths located in the red edge than the standard NDVI.
An analytical algorithm for the determination of vegetation leaf area index from TRMM/TMI data
Wen, J. ; Su, Z. - \ 2004
International Journal of Remote Sensing 25 (2004)6. - ISSN 0143-1161 - p. 1223 - 1234.
soil-moisture retrieval - surface-temperature - microwave emission - tibetan plateau - polarization - field - ndvi - ghz
In this paper, an analytical algorithm for the determination of land surface vegetation Leaf Area Index (LAI) with the passive microwave remote sensing data is developed. With the developed algorithm and the Tropical Rainfall Measuring Mission/Microwave Imager (TRMM/TMI) remote sensing data collected during the Global Energy and Water Experiment (GEWEX) Asian Monsoon Experiment in Tibet (GAME/Tibet) Intensive Observation Period (IOP'98), the regional and temporal distributions of the land surface vegetation LAI have been evaluated. To validate the developed algorithm and the retrieval results, the maximum-composite Normalized Difference Vegetation Index (NDVI) data over the same study area and period are used in this study; the cloud contaminated NDVI values have been replaced by the cloud-free values reconstructed by the Harmonic ANnalysis of Time Series (HANTS) technique. The results show that the retrieved LAI is in good agreement with the cloud-free NDVI in regional and temporal distributions and in their statistical characteristics; the vegetation characteristics can be clearly assessed from the regional distribution of the retrieved LAI. As lower frequency microwave radiation can penetrate atmosphere and thin cloud layer, with the application of the passive microwave remote sensing data, the developed algorithm can be used to monitor the land surface vegetation condition more effectively.
Remote sensing parameterization of land surface heat fluxes over arid and semi-arid areas
Ma, Y.M. ; Wang, J.M. ; Huang, R.H. ; Wei, G. ; Menenti, M. ; Su, Z. ; Hu, Z.Y. ; Gao, F. ; Jiang, W. - \ 2003
Advances in atmospheric sciences 20 (2003)4. - ISSN 0256-1530 - p. 530 - 539.
heterogeneous landscape - oasis - heife - index - desert - field - ndvi
Dealing with the regional land surfaces heat fluxes over inhomogeneous land surfaces in arid and semi-arid areas is an important but not an easy issue. In this study, one parameterization method based on satellite remote sensing and field observations is proposed and tested for deriving the regional land surface heat fluxes over inhomogeneous landscapes. As a case study, the method is applied to the Dunhuang experimental area and the HEIFE (Heihe River Field Experiment, 1988¿1994) area. The Dunhuang area is selected as a basic experimental area for the Chinese National Key Programme for Developing Basic Sciences: Research on the Formation Mechanism and Prediction Theory of Severe Climate Disaster in China (G1998040900, 1999¿2003). The four scenes of Landsat TM data used in this study are 3 June 2000, 22 August 2000, and 29 January 2001 for the Dunhuang area and 9 July 1991 for the HEIFE area. The regional distributions of land surface variables, vegetation variables, and heat fluxes over inhomogeneous landscapes in arid and semi-arid areas are obtained in this study.