Angela Harris

Remote sensing of vegetation cover dynamics and resilience across southern Africa

Southern Africa supports a significant portion of the worlds floral biodiversity but predicted changes in climate are likely to cause adverse impacts on the regions ecosystems and biodiversity. Knowledge regarding the resilience of vegetation cover is important for understanding the potential impact of anthropic or climatic change. The length of time vegetation cover takes to recover from disturbances can provide an indication of ecosystem resilience. We investigated spatial and temporal patterns in the persistence of vegetation cover across southern Africa (1982–2006) and used persistence probability plots to estimate decay times of NDVI trends as a means to characterise the potential resilience of key southern African biomes. Patterns of positive and negative NDVI trend persistence were spatially coherent, indicating collective dynamic behaviour of vegetation cover. Persistence probability plots indicated differences in resilience between biomes. Mean recovery times from negative NDVI trends were shorter than for positive trends in the Savanna and Nama Karoo, whereas the Succulent Karoo exhibited the shortest mean lifetime for positive NDVI trends and one of the longest mean lifetimes for negative trend survival, implying potentially slow recovery from environmental disturbance. The results show the potential of satellite-time series data for monitoring vegetation cover resilience in semi-arid regions.

Do peatland microforms move through time? Examining the developmental history of a patterned peatland using ground‐penetrating radar

Using ground-penetrating radar (GPR) to map subsurface patterns in peat physical properties, we investigated the developmental history of meso-scale surface patterning of microforms within a raised bog. Common offset GPR measurements were obtained along a 45-m transect, at frequencies ranging from 100 to 900 MHz. We found that low-frequency (central frequency = 240 MHz) showed a striking pattern of subsurface reflections that dip consistently in a northerly direction. The angle of these dipping reflectors is calculated using a semblance algorithm and was shown to average 3.9 degrees between a depth of 1.0 and 2.5 m. These dipping reflectors may indicate downslope migration of surface microforms during the development of the peatland. Based on the estimated angle and the rate of peat accumulation, the average rate of downslope propagation of these surface microforms is calculated at 9.8 mm per year. Further survey work is required to establish whether the downslope migration is common across the peatland.

A new approach for estimating northern peatland gross primary productivity using a satellite-sensor-derived chlorophyll index

Carbon flux models that are largely driven by remotely sensed data can be used to estimate gross primary productivity (GPP) over large areas, but despite the importance of peatland ecosystems in the global carbon cycle, relatively little attention has been given to determining their success in these ecosystems. This paper is the first to explore the potential of chlorophyll-based VI models for estimating peatland GPP from satellite data. Using several years of carbon flux data from contrasting peatlands, we explored the relationships between the MERIS Terrestrial Chlorophyll Index (MTCI) and GPP, and determined whether the inclusion of environmental variables such as PAR and temperature, thought to be important determinants of peatland carbon flux, improved upon direct relationships. To place our results in context, we compared the newly developed GPP models with the MODIS (Moderate Resolution Imaging Spectrometer) GPP product. Our results show that simple MTCI-based models can be used for estimates of inter- and intra annual variability in peatland GPP. The MTCI is a good indicator of GPP and compares favourably with more complex products derived from the MODIS sensor on a site specific basis. The incorporation of MTCI into a light use efficiency type model, by means of partitioning the fraction of photosynthetic material within a plant canopy, shows most promise for peatland GPP estimation, outperforming all other models. Our results show great promise and demonstrate that satellite data specifically related to vegetation chlorophyll content, may ultimately facilitate improved quantification of peatland carbon flux dynamics

Constitutive changes in pigment concentrations: implications for estimating isoprene emissions using the photochemical reflectance index

The photochemical reflectance index (PRI), through its relationship with light use efficiency (LUE) and xanthophyll cycle activity, has recently been shown to hold potential for tracking isoprene emissions from vegetation. However, both PRI and isoprene emissions can also be influenced by changes in carotenoid pigment concentrations. Xanthophyll cycle activity and changes in carotenoid concentrations operate over different timescales, but the importance of constitutive changes in pigment concentrations for accurately estimating isoprene emissions using PRI is unknown. To clarify the physiological mechanisms behind the PRI-isoprene relationship, the light environment of potted Salix viminalis (osier willow) trees was modified to induce acclimation in photosynthetic rates, phytopigments, isoprene emissions and PRI. Acclimation resulted in differences in pigment concentrations, isoprene emissions and PRI. Constitutive changes in carotenoid concentration were significantly correlated with both isoprene emissions and PRI, suggesting that the relationship between PRI and isoprene emissions is significantly influenced by constitutive pigment changes. Consequently knowledge regarding how isoprene emissions are affected by both longer term changes in total carotenoid concentrations and shorter term dynamic adjustments of LUE is required to facilitate interpretation of PRI for monitoring isoprene emissions.

Characterizing fractional vegetation cover and land surface temperature based on sub-pixel fractional impervious surfaces from Landsat TM/ETM+

Estimating the distribution of impervious surfaces and vegetation is important for analysing urban landscapes and their thermal environment. The application of a crisp classification of land-cover types to analyse urban landscape patterns and land surface temperature (LST) in detail presents a challenge, mainly due to the complex characteristics of urban landscapes. In this article, sub-pixel percentage impervious surface areas (ISAs) and fractional vegetation cover (FVC) were extracted from bitemporal Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data by linear spectral mixture analysis (LSMA). Their accuracy was assessed with proportional area estimates of the impervious surface and vegetation extracted from high-resolution data. A range approach was used to classify percentage ISA into different categories by setting thresholds of fractional values and these were compared for their LST patterns. For each ISA category, FVC, LST, and percentage ISA were used to quantify the urban thermal characteristics of different developed areas in the city of Fuzhou, China. Urban LST scenarios in different seasons and ISA categories were simulated to analyse the seasonal variations and the impact of urban landscape pattern changes on the thermal environment. The results show that FVC and LST based on percentage ISA can be used to quantitatively analyse the process of urban expansion and its impacts on the spatial–temporal distribution patterns of the urban thermal environment. This analysis can support urban planning by providing knowledge on the climate adaptation potential of specific urban spatial patterns.

The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method

ABSTRACT Green infrastructure (GI) mapping and monitoring is crucial in urban areas, and remote sensing is widely used to accomplish the task. Improved moderate resolution Sentinel-2A (10 m) and LandSat-8 (15 m) images, in place of commercial satellite images, enable GI mapping with little to no cost. Considering so, the objective of this paper is to evaluate the potential of GI feature extraction of Sentinel-2A (S2) and LandSat-8 (L8) (freely available images) using the Object Based Image Analysis (OBIA) method. The advantage of using OBIA over pixel-based analysis has been investigated primarily with very high resolution images. Using OBIA, bottom up (i.e. Multiresolution) and top down (i.e. Spectral Difference) segmentation were implemented using eCognition to obtain image objects for both S2 and L8 images. Then, rule-based classification was performed to extract GI areas from the objects. NDVI, NDWI, NIR/R ratios were utilized in rule set development, after several trial and error process. Both S2 and L8 provided acceptable extraction of GI for urban areas. However, with an overall accuracy of 71.24%, S2 was more effective when extracting GI areas. Shadows along roads and high rise buildings caused some inaccuracy in classification.