Cyrus Samimi

Assessing spatio‐temporal variations in plant phenology using Fourier analysis on NDVI time series: results from a dry savannah environment in Namibia

Time series of Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) were used to capture plant phenology in Etosha National Park, a dry savannah environment in Namibia. Data from two consecutive growing periods with different precipitation conditions were included to study impacts of inter‐seasonal rainfall variations on a highly water‐limited ecosystem. Additionally, a contemporary reference map with four major vegetation units was used to compare phenology between plant formations. Phenological attributes were acquired for both seasons using Fourier analysis. Parameters were calculated for the entire study area and further stratified with respect to the mapping units of the reference. Vegetation growth was found to vary significantly between the two periods in accordance with available rainfall data. Additionally, separability of vegetation entities based on Fourier parameters was weak due to within‐class scattering and was commonly outranged by inter‐seasonal variations. Finally, discrimination of cover types was tested by combining selected Fourier parameters in a clustering procedure. Spatial class distribution was compared to the reference statistically and only a moderate correspondence was discovered. We conclude that Fourier‐based NDVI attributes are limited for cover‐type discrimination across space and time, as they only quantify certain aspects of plant phenology and seem to be largely altered by the actual rainfall situation.

Local Vegetation Trends in the Sahel of Mali and Senegal Using Long Time Series FAPAR Satellite Products and Field Measurement (1982–2010)

Local vegetation trends in the Sahel of Mali and Senegal from Geoland Version 1 (GEOV1) (5 km) and the third generation Global Inventory Modeling and Mapping Studies (GIMMS3g) (8 km) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series are studied over 29 years. For validation and interpretation of observed greenness trends, two methods are applied: (1) a qualitative approach using in-depth knowledge of the study areas and (2) a quantitative approach by time series of biomass observations and rainfall data. Significant greening trends from 1982 to 2010 are consistently observed in both GEOV1 and GIMMS3g FAPAR datasets. Annual rainfall increased significantly during the observed time period, explaining large parts of FAPAR variations at a regional scale. Locally, GEOV1 data reveals a heterogeneous pattern of vegetation change, which is confirmed by long-term ground data and site visits. The spatial variability in the observed vegetation trends in the Sahel area are mainly caused by varying tree- and land-cover, which are controlled by human impact, soil and drought resilience. A large proportion of the positive trends are caused by the increment in leaf biomass of woody species that has almost doubled since the 1980s due to a tree cover regeneration after a dry-period. This confirms the re-greening of the Sahel, however, degradation is also present and sometimes obscured by greening. GEOV1 as compared to GIMMS3g made it possible to better characterize the spatial pattern of trends and identify the degraded areas in the study region.

Biomass estimation using Landsat-TM and -ETM+. Towards a regional model for Southern Africa?

Grazing and fire are major factors influencing the savanna ecosystems of Southern Africa. In both grazing and conservation areas overgrazing is an important reason for degradation of vegetation and soil. Insufficient fire management can cause a change in the species composition and may influence the soil negatively. For adequate planning purposes the knowledge of available biomass is indispensable. High-resolution satellite systems can provide such knowledge on a large scale. Three study areas in Southern Africa contributed to a first survey. Gutu District is situated in Zimbabwe. In its Communal Lands a high population density leads to severe degradation of vegetation and soil. The South African test sites are located in Kruger National Park and Madikwe Game Reserve. Therefore a wide ecological range from highly degraded to slightly disturbed savanna ecosystems is included. Satellite images of both Landsat-5 (TM) and Landsat-7 (ETM+) were applied. After cross-calibration of the two different satellite systems, indices applied to radiance and reflectance showed significant correlations with ground truth data of grass and other foliage biomass. Including new data from Hluhluwe National Park (South Africa) into the regression models improved the results, indicating that a regional model for savanna ecosystems in Southern Africa could be found.

Woody vegetation and land cover changes in the Sahel of Mali (1967–2011)

Abstract In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmers fields. Mean woody cover decreased in the plains (−4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.

Human and climate impact on 15N natural abundance of plants and soils in high-mountain ecosystems: a short review and two examples from the Eastern Pamirs and Mt. Kilimanjaro

Population pressure increasingly endangers high-mountain ecosystems such as the pastures in the Eastern Pamirs and the mountain forests on Mt. Kilimanjaro. At the same time, these ecosystems constitute the economic basis for millions of people living there. In our study, we, therefore, aimed at characterising the land-use effects on soil degradation and N-cycling by determining the natural abundance of 15N. A short review displays that δ15N of plant–soil systems may often serve as an integrated indicator of N-cycles with more positive δ15N values pointing towards N-losses. Results for the high-mountain pastures in the Eastern Pamirs show that intensively grazed pastures are significantly enriched in 15N compared to the less-exploited pastures by 3.5 ‰, on average. This can be attributed to soil organic matter degradation, volatile nitrogen losses, nitrogen leaching and a general opening of the N-cycle. Similarly, the intensively degraded savanna soils, the cultivated soils and the soils under disturbed forests on the foothill of Mt. Kilimanjaro reveal very positive δ15N values around 6.5 ‰. In contrast, the undisturbed forest soils in the montane zone are more depleted in 15N, indicating that here the N-cycle is relatively closed. However, significantly higher δ15N values characterise the upper montane forest zone at the transition to the subalpine zone. We suggest that this reflects N-losses by the recently monitored and climate change and antropogenically induced increasing fire frequency pushing the upper montane rainforest boundary rapidly downhill. Overall, we conclude that the analysis of the 15N natural abundance in high-mountain ecosystems is a purposeful tool for detecting land-use- or climate change-induced soil degradation and N-cycle opening.

Factors Influencing Households' Firewood Consumption in the Western Pamirs, Tajikistan

Abstract Firewood is a major energy source, especially in many high mountainous regions in developing countries where other energy sources are limited. In the mountainous regions of Tajikistan, current energy consumption is limited owing to geographic isolation and numerous challenges—including in the energy sector—that emerged after the collapse of the Soviet Union and Tajikistans independence. The sudden disruption of external supplies of energy forced people to rely on locally available but scarce biomass resources, such as firewood and animal dung. We conducted an empirical study to gain an understanding of current household energy consumption in the Western Pamirs of Tajikistan and the factors that influence firewood consumption. For this purpose, we interviewed members of 170 households in 8 villages. We found that, on average, households consumed 355 kg of firewood, 253 kWh of electricity, 760 kg of dung, and 6 kg of coal per month in the winter of 2011–2012. Elevation, size of a households private garden, and total hours of heating had a positive relationship with firewood consumption, and education level and access to a reliable supply of electricity showed a negative relationship.

Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote Sensing and Random Forests

In many arid mountains, dwarf shrubs represent the most important fodder and firewood resources; therefore, they are intensely used. For the Eastern Pamirs (Tajikistan), they are assumed to be overused. However, empirical evidence on this issue is lacking. We aim to provide a method capable of mapping vegetation in this mountain desert. We used random forest models based on remote sensing data (RapidEye, ASTER GDEM) and 359 plots to predictively map total vegetative cover and the distribution of the most important firewood plants, K. ceratoides and A. leucotricha. These species were mapped as present in 33.8% of the study area (accuracy 90.6%). The total cover of the dwarf shrub communities ranged from 0.5% to 51% (per pixel). Areas with very low cover were limited to the vicinity of roads and settlements. The model could explain 80.2% of the total variance. The most important predictor across the models was MSAVI2 (a spectral vegetation index particularly invented for low-cover areas). We conclude that the combination of statistical models and remote sensing data worked well to map vegetation in an arid mountainous environment. With this approach, we were able to provide tangible data on dwarf shrub resources in the Eastern Pamirs and to relativize previous reports about their extensive depletion.

Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

Remote sensing-based woody biomass quantification in sparsely-vegetated areas is often limited when using only common broadband vegetation indices as input data for correlation with ground-based measured biomass information. Red edge indices and texture attributes are often suggested as a means to overcome this issue. However, clear recommendations on the suitability of specific proxies to provide accurate biomass information in semi-arid to arid environments are still lacking. This study contributes to the understanding of using multispectral high-resolution satellite data (RapidEye), specifically red edge and texture attributes, to estimate wood volume in semi-arid ecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and Selection Operator) and random forest were used as predictive models relating in situ-measured aboveground standing wood volume to satellite data. Model performance was evaluated based on cross-validation bias, standard deviation and Root Mean Square Error (RMSE) at the logarithmic and non-logarithmic scales. Both models achieved rather limited performances in wood volume prediction. Nonetheless, model performance increased with red edge indices and texture attributes, which shows that they play an important role in semi-arid regions with sparse vegetation.

Comparing the dry season in-situ leaf area index (lai) derived from high-resolution rapideye imagery with MODIS LAI in a Namibian savanna

Abstract: The Leaf Area Index (LAI) is one of the most frequently applied measures to characterize vegetation and its dynamics and functions with remote sensing. Satellite missions, such as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) operationally produce global datasets of LAI. Due to their role as an input to large-scale modeling activities, evaluation and verification of such datasets are of high importance. In this context, savannas appear to be underrepresented with regards to their heterogeneous appearance (e.g., tree/grass-ratio, seasonality). Here, we aim to examine the LAI in a heterogeneous savanna ecosystem located in Namibia’s Owamboland during the dry season. Ground measurements of LAI are used to derive a high-resolution LAI model with RapidEye satellite data. This model is related to the corresponding MODIS LAI/FPAR (Fraction of Absorbed Photosynthetically Active Radiation) scene (MOD15A2) in order to evaluate its performance at the intended annual minimum during the dry season. Based on a field survey we first assessed vegetation patterns from species composition and elevation for 109 sites. Secondly, we measured

Disturbance feedbacks on the height of woody vegetation in a savannah : a multi-plot assessment using an unmanned aerial vehicle (UAV)

ABSTRACT Disturbances affect the woody, i.e. trees and shrubs, and herbaceous vegetation in savannah ecosystems worldwide. In Northern Namibia, livestock grazing and fires depict two prominent agents of disturbance. These affect the structural parameters of vegetation such as the height of woody species. Remote sensing is a tool to quantify such structural parameters. In particular, Image-Based Point Clouds (IBPCs) obtained from unmanned aerial vehicles (UAVs) are nowadays increasingly used for three-dimensional (3D) remote-sensing applications. Here we aim at deriving the height of woody stands through a multi-plot UAV campaign (n = 19) carried out at the end of the dry season. We use direct georeferencing from the navigation-grade instruments on board the UAV in a Structure-from-Motion (SfM) approach. Watershed segmentation is applied to derive plot-scale height metrics (maximum, mean, and median) based on delineated individuals. Fire and grazing – both individually and synergistically – are then investigated for their impacts on UAV-derived height metrics. The results indicate good agreement between the UAV-derived and in situ-measured height metrics on the plot scale (coefficient of determination (R2) approximately 0.7, root mean square error (RMSE) <1.9 m). Underestimations of height are apparent with large, leafless trees. Clumping of equally sized individuals complicated their correct delineation. Grazing was found to be significant for all height metrics as well as in combination with fire for the plots’ maxima. We conclude that the approach applied here is able to reproduce the plot-scale heights of woody vegetation with acceptable accuracy. We attribute the observed height reductions with the simultaneous presence of disturbances to legacy effects.