Grassland Classes Research Articles

Syntaxonomic diversity of dry grassland and phrygana vegetation of the Southern Crimea: preliminary analysis

The aim of the work is to revise the prodrome and diagnostic species of the classes of natural dry grassland and phrygana vegetation of the Southern Crimea based on own field studies and modern literature data. Methods. The work is based on the Braun-Blanquet approach to classification of vegetation communities. Higher syntaxonomy units and lists of diagnostic species are given in accordance with “Vegetation of Europe ...” (EuroVegChecklist), nomenclature of syntaxon according to the requirements of the “International Code of Phytosociological Nomenclature”, nomenclature of taxon according to the Catalogue of Life and Euro + Med PlantBase international databases. Results. A preliminary version of the prodrome of natural dry grassland and phrygana vegetation of the Southern Crimea has been compiled. It was established that this vegetation type includes communities of 12 classes: Sedo-Scleranthetea , Trifolio-Geranietea sanguinei , Festuco-Brometea , Festuco-Puccinellietea , Kalidietea foliati , Ononido-Rosmarinetea , Cisto-Lavanduletea stoechadis , Helianthemetea guttati , Stipo-Trachynietea distachyae , Saginetea maritimae , Asplenietea trichomanis , Drypidetea spinosae. There are 14 orders, 14 alliances and 30 associations, three of which need to be validated. The classes of rock and scree vegetation ( Asplenietea trichomanis , Drypidetea spinosae ) are described in most detail. For most classes, a significant part of the classification units has not yet been established. Conclusion. The preliminary prodrome of the natural dry grassland and phrygana vegetation of the Southern Crimea includes 12 classes, 14 orders, 14 alliances and 30 associations. A great part of syntaxa has not yet been established. The composition of the diagnostic species indicates a significant floristic proximity of the classes Sedo-Scleranthetea - Helianthemetea guttati - Stipo-Trachynietea distachyae , Festuco-Puccinellietea - Kalidietea foliati , Ononido-Rosmarinetea - Cisto-Lavanduletea stoechadis .

Permanent grassland classifications predict agronomic and environmental characteristics well, but not ecological characteristics

Permanent grasslands produce highly diverse ecosystem goods and services, which need to be easily assessed by decision makers. Naturalists and agronomists classify grasslands in different ways to predict ecological, agronomic and environmental characteristics of the grasslands. However, few studies have compared the prediction abilities of these different classifications using the same botanical releves, and none has explored the utility of combining classifications. In this study, we attributed a grassland class from each of three classifications (phytosociological, agronomic and functional) to 250 permanent grasslands in north-eastern France to predict 16 characteristics: nine ecological, three agronomic and four environmental. We used statistical model selection to identify the classification or combination of classifications that best predicted each characteristic. Our results showed great prediction ability of agronomic classification, which created the best models for predicting agronomic (yield) and environmental (management, elevation) characteristics. We also identified a strong prediction ability of combining two or all three classifications to predict seven other grassland characteristics. However, grassland classifications did not predict most of the ecological characteristics well. We can assume that phytosociological classification, despite its mainstream use, predicts grassland characteristics less well than agronomic classification. We recommend combining grassland classifications to improve rapid prediction abilities. This study provides new knowledge useful for developing grassland classifications which meet the needs of agronomists and naturalists.

Detailed global modelling of soil organic carbon in cropland, grassland and forest soils.

Assessments of the global carbon (C) cycle typically rely on simplified models which consider large areas as homogeneous in terms of the response of soils to land use or consider very broad land classes. For example, “cropland” is typically modelled as an aggregation of distinct practices and individual crops over large regions. Here, we use the process-based Rothamsted soil Carbon Model (RothC model), which has a history of being successfully applied at a global scale, to calculate attainable SOC stocks and C mineralization rates for each of c. 17,000 regions (combination of soil type and texture, climate type, initial land use and country) in the World, under near-past climate conditions. We considered 28 individual crops and, for each, multiple production practices, plus 16 forest types and 1 grassland class (total of 80 classes). We find that conversion to cropland can result in SOC increases, particularly when the soil remains covered with crop residues (an average gain of 12 t C/ha) or using irrigation (4 t C/ha), which are mutually reinforcing effects. Attainable SOC stocks vary significantly depending on the land use class, particularly for cropland. Common aggregations in global modelling of a single agricultural class would be inaccurate representations of these results. Attainable SOC stocks obtained here were compared to long-term experiment data and are well aligned with the literature. Our results provide a regional and detailed understanding of C sequestration that will also enable better greenhouse gas reporting at national level as alternatives to IPCC tier 2 defaults.

Use of Logistic Regression in Land-Cover Classification with Moderate-Resolution Multispectral Data

The current study highlights the use of binary logistic regression for land-use land-cover (LULC) classification. The moderate-resolution Sentinel-2 multispectral data was used for LULC map generation for the post-monsoon season. The main focus of this study is to present a simple and precise approach for image classification using binary logistic regression (BLR) technique. The study was carried out in cropland, fallow land, forest and water body dominated subtropical region of India located in the eastern coastal region. The cropland and fallow lands are mostly dependent on the monsoon and reciprocal land covers. A large number of training and testing data points were collected viewing the image in a standard false-color composite. ArcGIS, Microsoft Office Excel and R software were used for classification. In addition to BLR, the training and testing data points were also used to perform the classification with ‘random forest’ classifier in R. We observed higher classification accuracy for spectrally pure classes and pixels and lower for closely associated mix-pixels. Lower user’s and producer’s accuracies (< 90%) were observed for fallow land, water body and grassland class during training and model building and for fallow land and forest during accuracy assessment, whereas the accuracies were more than 90% for the rest of classes during both training and testing. Misclassifications were mostly observed between forest, fallow land, grassland and water body during training, which were forest and fallow land in testing, due to their lower spectral difference with reference to classified classes. However, the overall accuracy and kappa value during training and testing were more than 94% and 0.98, respectively. Similar accuracies and misclassification were also obtained with the results of random forest model, validating the adopted methodology. Regardless of the seasonal variations in cropland and fallow land, the field observations (52 locations) also corroborated the estimated classification accuracy. The easy implementation and comparatively higher classification accuracy with the binary logistic technique are believed to increase its intense use in land-cover classification.

Investigative Spatial Distribution and Modelling of Existing and Future Urban Land Changes and Its Impact on Urbanization and Economy

Land use and land cover (LULC) change analysis is a critical instrument for studying urban growth across the world. Our objectives were to produce historical LULC maps during the 1988–2016 period for spatial and temporal analysis, forecast future LULC until 2040 by using the Markov model, and identify the impact of LULC on urbanization. Two scenes of Landsat-5 TM for 1988 and 2001 and one scene of Landsat-8 OLI for 2016 were processed and used. The Normalized Difference Vegetation Index (NDVI) model with precise class value ranges was applied to produce land cover maps with six classes of water, built-up, barren land, shrub and grassland, sparse vegetation, and dense vegetation. LULC maps for the years of 1988 and 2001 were used to develop an LULC transformation matrix. It was used to drive an LULC transformation probability matrix using a Markov model for future forecasting of LULC in 2014, 2027, and 2040. The accuracy of 2016 LULC classes was estimated by comparing it against Markov modeled classes. It was found that the areas for: (i) water decreased from 1.43% to 0.51%; (ii) built-up increased from 9.58% to 20.80%; (iii) barren land decreased from 29.50% to 13.40%; (iv) shrub and grass land decreased from 30.57% to 21.10%; (v) sparse vegetation increased from 18% to 20.10%; and (vi) dense vegetation increased from 10.57% to 24.10%. The variations in LULC classes could be noticed by 2040 as compared to 1988. This LULC variation revealed that the water could decrease to 5.32 km2 from 25.37 km2; the built-up could increase to 625.16 km2 from 168.29 km2; the barren land could decrease to 137.53 km2 from 514.13 km2; the shrub and grassland could decrease to 297.68 km2 from 539.46 km2; the sparse vegetation could decrease to 297.68 km2 from 539.46 km2; and the dense vegetation could increase to 409.65 km2 from 191.51 km2. The LULC classification accuracy was 90.27% and 95.11% for 1988 and 2001, respectively. The co-efficient of determination (R2) was found to be 0.90 for 2016 LULC classes obtained from Landsat-8 and derived from a Markov model. For District Lahore, the LULC changes could be related to increasing population and intense migration trends, which had progressive impact on infrastructure development, industrial and economic growth, and detrimental effects on water resources.

Mapping semi-natural grassland communities using multi-temporal RapidEye remote sensing data

ABSTRACTMapping semi-natural grassland has become increasingly important with regard to climate variability, invasive species, and the intensification of land use. At the same time, adequate field data collection is of pivotal importance for national and international reporting obligations, such as the European Habitats Directive. We present a remote-sensing-based monitoring framework for a Natura 2000 site with a heterogeneous composition of different grassland communities, using the Random Forest algorithm. Automated training data selection was successfully implemented based on the Random Forest proximity measure (Overall Accuracy ranging from 77.5–86.5%). RapidEye acquisitions originating from the onset of vegetation (prespring and first spring) and senescence (late summer and first autumn) were identified as important phenological phases for mapping semi-natural grassland communities. The derived probability maps of occurrences for each grassland class captured transitions between grassland communities and are therefore a better approximation of real-world conditions compared to classical, discrete maps.

Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia

This paper presents a random forest classification approach for identifying and mapping three types of lowland native grassland communities found in the Tasmanian Midlands region. Due to the high conservation priority assigned to these communities, there has been an increasing need to identify appropriate datasets that can be used to derive accurate and frequently updateable maps of community extent. Therefore, this paper proposes a method employing repeat classification and statistical significance testing as a means of identifying the most appropriate dataset for mapping these communities. Two datasets were acquired and analysed; a Landsat ETM+ scene, and a WorldView-2 scene, both from 2010. Training and validation data were randomly subset using a k-fold (k=50) approach from a pre-existing field dataset. Poa labillardierei, Themeda triandra and lowland native grassland complex communities were identified in addition to dry woodland and agriculture. For each subset of randomly allocated points, a random forest model was trained based on each dataset, and then used to classify the corresponding imagery. Validation was performed using the reciprocal points from the independent subset that had not been used to train the model. Final training and classification accuracies were reported as per class means for each satellite dataset. Analysis of Variance (ANOVA) was undertaken to determine whether classification accuracy differed between the two datasets, as well as between classifications. Results showed mean class accuracies between 54% and 87%. Class accuracy only differed significantly between datasets for the dry woodland and Themeda grassland classes, with the WorldView-2 dataset showing higher mean classification accuracies. The results of this study indicate that remote sensing is a viable method for the identification of lowland native grassland communities in the Tasmanian Midlands, and that repeat classification and statistical significant testing can be used to identify optimal datasets for vegetation community mapping.

Evaluating land cover changes in Eastern and Southern Africa from 2000 to 2010 using validated Landsat and MODIS data

In this study, we assessed land cover land use (LCLU) changes and their potential environmental drivers (i.e., precipitation, temperature) in five countries in Eastern & Southern (E&S) Africa (Rwanda, Botswana, Tanzania, Malawi and Namibia) between 2000 and 2010. Landsat-derived LCLU products developed by the Regional Centre for Mapping of Resources for Development (RCMRD) through the SERVIR (Spanish for “to serve”) program, a joint initiative of NASA and USAID, and NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to evaluate and quantify the LCLU changes in these five countries. Given that the original development of the MODIS land cover type standard products included limited training sites in Africa, we performed a two-level verification/validation of the MODIS land cover product in these five countries. Precipitation data from CHIRPS dataset were used to evaluate and quantify the precipitation changes in these countries and see if it was a significant driver behind some of these LCLU changes. MODIS Land Surface Temperature (LST) data were also used to see if temperature was a main driver too.Our validation analysis revealed that the overall accuracies of the regional MODIS LCLU product for this African region alone were lower than that of the global MODIS LCLU product overall accuracy (63–66% vs. 75%). However, for countries with uniform or homogenous land cover, the overall accuracy was much higher than the global accuracy and as high as 87% and 78% for Botswana and Namibia, respectively. In addition, the wetland and grassland classes had the highest user’s accuracies in most of the countries (89%–99%), which are the ones with the highest number of MODIS land cover classification algorithm training sites.Our LCLU change analysis revealed that Botswana’s most significant changes were the net reforestation, net grass loss and net wetland expansion. For Rwanda, although there have been significant forest, grass and crop expansions in some areas, there also have been significant forest, grass and crop loss in other areas that resulted in very minimal net changes. As for Tanzania, its most significant changes were the net deforestation and net crop expansion. Malawi’s most significant changes were the net deforestation, net crop expansion, net grass expansion and net wetland loss. Finally, Namibia’s most significant changes were the net deforestation and net grass expansion.The only noticeable environmental driver was in Malawi, which had a significant net wetland loss and could be due to the fact that it was the only country that had a reduction in total precipitation between the periods when the LCLU maps were developed. Not only that, but Malawi also happened to have a slight increase in temperature, which would cause more evaporation and net decrease in wetlands if the precipitation didn’t increase as was the case in that country. In addition, within our studied countries, forestland expansion and loss as well as crop expansion and loss were happening in the same country almost equally in some cases. All of that implies that non-environmental factors, such as socioeconomics and governmental policies, could have been the main drivers of these LCLU changes in many of these countries in E&S Africa. It will be important to further study in the future the detailed effects of such drivers on these LCLU changes in this part of the world.

Identification of high nature value grassland with remote sensing and minimal field data

In the last 50 years intensification of agricultural land use systems drastically reduced extensively used grassland areas. These areas are of high ecological value due to high species richness and occurring rare species. Therefore, recent European Union (EU) laws stipulate the conservation and monitoring of this farmland, also called “high nature value” (HNV) farmland. As a consequence of these new laws, a so called HNV indicator system was implemented that requires all EU member states to establish a nationwide monitoring system for HNV areas. These monitoring systems are challenged among other by the difficult differentiation between grassland types which today at fine scale is only possible with time and cost intensive field work. Due to this high work-load and financial limitations, nationwide field campaigns have to be sample-based and hence will not deliver a spatially consistent result.In this study, we examine whether low and high nature value grasslands can be differentiated with remotely-sensed reflectance data, which could support existing field survey-based monitoring approaches. We used multi-seasonal, multispectral remote sensing data (RapidEye) in combination with sparse field data (collected in southern Germany) and three one-class classifiers to classify A) HNV grassland against other areas and to differentiate between B) three quality classes of HNV grassland according to the current German HNV monitoring approach.The results for A) indicated high performances of the tested approaches to identify HNV grassland areas. Biased support vector machine delivered best overall results (high detection rate and low false positive rates). However, the results also showed a consistent underestimation of HNV grasslands. Results for B) showed that a separation into several HNV quality classes is not possible with any of the tested approaches.We conclude that with the presented approach HNV grasslands can be identified from the landscape matrix based on its spectral signal. Combining the presented approach with an object oriented classifier or with land registry data could further improve the results.

Estimating net primary production of natural grassland and its spatio-temporal distribution in China

The net primary production (NPP) of grassland largely determines terrestrial carbon (C) sinks, and thus plays an important role in the global C cycle. Comprehensive and sequential classification system of grasslands (CSCS) is a unique vegetation classification system (mainly for grassland) that is dependent on quantitative measurement indices [>0°C annual cumulative temperature (Σθ) and moisture index (K-value)]. Based on the relationship of the quantitative classification of CSCS and grassland NPP, a modified model of Carnegie–Ames–Stanford Approach (CASA) was used to predict the grassland NPP and its temporal and spatial distribution in China from 2004 to 2008. The scatter plot of the estimated NPP and the observed NPP showed that the estimated data can be accepted with correlation coefficient of 0.896 (P<0.05). The average annual NPP of grassland from 2004 to 2008 in China ranged from 443.23 to 554.40gCm−2yr.−1. The NPP also showed spatial–temporal variations. There existed an increasing trend of NPP from the northwest to southeast due to the zonal distribution of vegetation. From the trend of monthly variations, it can be drawn that the NPP accumulation primarily occurred between April and October. The average NPP over seven months from April to October was 482.19gCm−2, or about 88.78% of the annual total. The spatial–temporal trend suggests the importance of water and thermal regimes in determining the grassland NPP (i.e. water and thermal are key limited factors for the grassland production), which is also confirmed by a cluster analysis. The mean annual NPP and the total annual NPP differed significantly among grassland classes corresponding with different Σθ and K-value. The results demonstrate that the grassland NPP and the classes/super-classes in CSCS achieve the optimum coupling.

Markov chains–cellular automata modeling and multicriteria analysis of land cover change in the Lower Nhecolândia subregion of the Brazilian Pantanal wetland

The dynamics of land use/land cover change in the Lower Nhecolândia wetland are marked by deforestation for pasture expansion, resulting in a real threat to the ecological stability. The aim of our work was to analyze the spatial distribution of land cover changes in the Lower Nhecolândia from 1985 to 2013 and to predict changes in trends for 2040. The mapping of land cover changes was developed using Landsat satellite images of 1985, 1999, 2007, and 2013, based on geographic object-based image analysis approach. This study uses integrated Markov chains and cellular automata modeling and multicriteria evaluation techniques to produce transition probability maps and describe the trajectory analysis methodology to construct a continuity of spatial and temporal changes for the wetland. The results of the multitemporal change detection classification show that, from 1985 to 2013, the forest woodland decreased by 6.89% and the grassland class increased by 18.29%. On the other hand, all water bodies showed a reducing trend, while the bare soil class increased compared to 1985, but did not present a regular trend of increase or decrease. From the present day, the trend for the future is a reduction of almost 6.4% by 2040. We found that deforestation actions will be concentrated in the areas with the highest concentration of saline lakes, constituting a serious threat to the natural functioning of this environmental system.

Spatial-Temporal NDVI Variation of Different Alpine Grassland Classes and Groups in Northern Tibet from 2000 to 2013

The Normalized Difference Vegetation Index (NDVI) can usually be used as a good proxy for evaluating potential variability in regional ecosystems and under climate change. We used 16-day MODIS-NDVI composite satellite data with 250-m resolution for the period 2000 to 2013 to assess the temporal and spatial variation of the NDVI among different alpine grassland classes and groups in northern Tibet. The annual average NDVI of the whole alpine grassland area in northern Tibet generally increased slightly from 2000 to 2003, and the annual average NDVI values ranged from 0.112 to 0.492 across all alpine grassland groups and years. The NDVI clearly decreased from the southeastern to the northwestern areas, with 22.50% of total grasslands significantly having increased or decreased, while 77.50% presented little change during 2000-2013. Both temperature and precipitation were key factors that controlled the NDVI variations of the entire alpine grassland. However, for different alpine grassland classes and groups, the NDVI displayed different correlation patterns with temperature and precipitation. Our results demonstrate that the NDVI variations of alpine grassland generally increased slightly but differed among different classes and groups. Although temperature and precipitation were the driving forces influencing the NDVI of the entire alpine grassland, it was more difficult to define the driving forces for the individual classes and groups, and more detailed analyses covering prolonged observation periods are still needed.

Pattern of Land-Use and Land Cover Changes in Driefontein Grassland Important Bird Area, Zimbabwe

We assessed land cover and land use change in Driefontein Grasslands Important Bird Area (Driefontein IBA), Zimbabwe, after the land reforms that took place in 2000, using remotely sensed satellite land cover images of 1995, 2000, 2005 and 2010. A drive transect was done for ground truthing. Data were analysed spatially in a Geographical Information System environment. Changes in land cover and land use proportions were recorded from 1995 to 2010 throughout the Driefontein IBA. The land cover classes of grassland and wetland showed a decrease, whereas land area under cultivation increased during 1995–2010. However, the woodland area marginally remained constant over the same time. We concluded that changes in land cover in the study area were largely driven by land use change, i.e., increase in cultivation, following the resettlement of people within the Driefontein IBA. It is likely that with increasing human population the natural habitat of the study area will be degraded further, negatively impacting key sensitive habitats such as wetlands, bird species, and other biodiversity. Threats to birds and wetlands habitats are also threats to human livelihoods because people in this semi-arid area depend on wetland ecosystem services for their survival. The study recommends continuous monitoring of Driefontein IBA for conservation of birds and their habitats.

Using information layers for mapping grassland habitat distribution at local to regional scales

The Natura 2000 network of protected sites is one of the means to enable biodiversity conservation in Europe. EU member states have to undertake surveillance of habitats and species of community interest protected under the Habitat Directive. Remote sensing techniques have been applied successfully to monitor biodiversity aspects according to Natura 2000, but many challenges remain in assessing dynamics and habitat changes outside protected sites. Grasslands are among the most threatened habitats in Europe. In this paper we tested the integration of expert knowledge into different standard classification approaches to map grassland habitats in Schleswig Holstein, Germany. Knowledge about habitat features is represented as raster information layers, and used in subsequent grassland classifications. Overall classification accuracies were highest for the maximum likelihood and support vector machine approaches using RapidEye time series, but results improved for specific grassland classes when information layers were included in the classification process.

Grassland habitat mapping by intra-annual time series analysis – Comparison of RapidEye and TerraSAR-X satellite data

Remote sensing concepts are needed to monitor open landscape habitats for environmental change and biodiversity loss. However, existing operational approaches are limited to the monitoring of European dry heaths only. They need to be extended to further habitats. Thus far, reported studies lack the exploitation of intra-annual time series of high spatial resolution data to take advantage of the vegetations’ phenological differences. In this study, we investigated the usefulness of such data to classify grassland habitats in a nature reserve area in northeastern Germany. Intra-annual time series of 21 observations were used, acquired by a multi-spectral (RapidEye) and a synthetic aperture radar (TerraSAR-X) satellite system, to differentiate seven grassland classes using a Support Vector Machine classifier. The classification accuracy was evaluated and compared with respect to the sensor type – multi-spectral or radar – and the number of acquisitions needed. Our results showed that very dense time series allowed for very high accuracy classifications (>90%) of small scale vegetation types. The classification for TerraSAR-X obtained similar accuracy as compared to RapidEye although distinctly more acquisitions were needed. This study introduces a new approach to enable the monitoring of small-scale grassland habitats and gives an estimate of the amount of data required for operational surveys.

Dry Grassland Types in the Prespa National Park (Nw Greece), Including the Southernmost Occurrence of the Priority Habitat Type “Pannonic Sand Steppes” (Code 6260)

Abstract A recently completed project on the Natura 2000 sites of Prespa National Park revealed that the area hosts 49 habitat types according to the EU classification, of which eight have a narrowly restricted distribution in Greece. The priority habitat type “*6260 Pannonic sand steppes” is reported here for the first time for Greece. The new locality represents its southernmost occurrence on the Balkan Peninsula. The aim of this paper therefore is to describe the Greek stands of this habitat type, investigate the factors affecting its occurrence and discuss the associated conservation issues. This is based on a total of 87 releves sampled for this study and 8 additional releves from the literature. The releves were classified by applying TWINSPAN and ordinated using Detrended Correspondence Analysis (DCA). Six vegetation units could be distinguished. One of these corresponds to the habitat type *6260 and was found exclusively on inland sand dunes. Syntaxonomically, we assigned this unit to the alliance Sileno conicae-Cerastion semidecandri s.l. of the class Koelerio-Corynephoretea. Its occurrence in the study area seems to be determined by climatic factors, as well as by the sandy substrate, while anthropogenic disturbances such as grazing also appear to be beneficial to some degree. Apart from the Koelerio-Corynephoretea, another five grassland classes were distinguished in the national park, namely the Thero-Brachypodietea, Stellarietea mediae, Festuco-Brometea, Daphno-Festucetea and Juncetea trifidi.

Classification and Net Primary Productivity of the Southern China's Grasslands Ecosystem Based on Improved Comprehensive and Sequential Classification System (CSCS) Approach

This research classified vegetation types and evaluated net primary productivity (NPP) of southern China's grasslands based on the improved comprehensive and sequential classification system (CSCS), and proposed 5 thermal grades and 6 humidity grades. Four classes of grasslands vegetation were recognized by improved CSCS, namely, tundra grassland class, typical grassland class, mixed grassland class and alpine grassland class. At the type level, 14 types of vegetations (9 grasslands and 5 forests) were classified. The NPP had a trend to decrease from east to west and south to north, and the annual mean NPP was estimated to be 656.3 g C m−2 yr−1. The NPP value of alpine grassland class was relatively high, generally more than 1 200 g C m−2 yr−1. The NPP value of mixed grassland class was in a range from 1 000 to 1 200 g C m−2 yr−1. Tundra grassland class was located in southeastern Tibet with high elevation, and its NPP value was the lowest (<600 g C m−2 yr−1). The typical grassland class distributed in most of the area, and its NPP value was generally from 600 to 1 000 g C m−2 yr−1. The total NPP value in the study area was 68.46 Tg C. The NPP value of typical grassland class was the highest (48.44 Tg C), and mixed grassland class was the second (16.54 Tg C), followed by alpine grassland class (3.22 Tg C), with tundra grassland class being the lowest (0.25 Tg C). For all the grasslands types, the total NPP of forest meadow was the highest (34.81 Tg C), followed by sparse forest brush (16.54 Tg C), and montane meadow was the lowest (0.01 Tg C).

Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data

The patchy and heterogeneous arrangement of vegetation in dryland areas complicates the extraction of phenological signals using existing remote sensing data. This study examined whether the phenological analysis of a range of dryland land cover classes would benefit from the availability of synthetic images at Landsat spatial resolution and MODIS time intervals. We assembled a series of 500m MODIS and Landsat-5 TM datasets from April to November, 2005–2009, over a study site in central Arizona that encompasses diverse dryland vegetation classes along an elevation gradient of 2000m. We applied the spatial and temporal adaptive reflectance fusion model (STARFM) to each MODIS image to create a time series of synthetic images at 30m resolution. We subjected a subset of the synthetic imagery to a pixel-based regression analysis with temporally coincident Landsat images to analyze the effect of the underlying vegetation class on the accuracy of the STARFM results. To evaluate the usefulness of the increased spatial resolution compared to a MODIS product, we analyzed the variability of the date of peak greenness values of all 30mpixels within unmixed MODIS pixels. Finally, we examined differences in the temporal distributions of peak greenness extracted from both the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) synthetic imagery time series. Our results indicate that characteristics of the vegetation classes strongly influence STARFM algorithm performance, with Pearson correlation coefficient values ranging from 0.72 to 0.96 depending on the Landsat band and the land cover class. Responses in the near-infrared (NIR) spectrum yielded the lowest correlations, particularly for the Ponderosa Pine class. The phenological variability exhibited by each land cover class was dependent on the precipitation patterns of each growing season, but was sufficiently high to make the application of STARFM imagery at this scale uniformly beneficial. The peak greenness dates extracted from EVI and NDVI time series were temporally synchronized for the Grassland class but diverged for the classes of mixed woody and herbaceous vegetation types.

Spatially-explicit modelling of grassland classes – an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterised by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the inverse distance-weighted approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were ‘cool temperate-arid temperate zonal semi-desert’, ‘cool temperate-humid forest steppe and deciduous broad-leaved forest’, ‘temperate-extra-arid temperate zonal desert’, and ‘frigid per-humid rain tundra and alpine meadow’. The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies’ decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities, which will help to prevent overgrazing and land degradation.

Detecting a moorland wildfire scar in the Peak District, UK, using synthetic aperture radar from ERS-2 and Envisat ASAR

Wildfires occur annually in UK moorland environments, especially in drought years. They can be severely damaging to the ecosystem when they burn deep into the peat, killing ground-nesting birds and releasing CO2 into the atmosphere. Synthetic aperture radar (SAR) was evaluated for detecting the 18 April 2003 Bleaklow wildfire scar (7.4 km2). SAR’s ability to penetrate cloud is advantageous in this inherently overcast area. SAR can provide fire scar boundary information which is otherwise labour intensive to collect in the field using a global positioning system (GPS). This article evaluates the potential of SAR intensity and InSAR coherence to detect a large peat moorland wildfire scar in the Peak District of northern England. A time-series of pre-fire and post-fire ERS-2 and advanced synthetic aperture radar (ASAR) Single Look Complex (SLC) data were pre-processed using SARScape 4.2 to produce georeferenced greyscale images. SAR intensity and InSAR coherence values were analysed against Coordinate Information on the Environment (CORINE) land‐cover classes and precipitation data. SAR intensity detected burnt peat well after a precipitation event and for previous fire events within the CORINE peat bog class. For the 18 April 2003 fire event, intensity increased to 0.84 dB post-fire inside the fire scar for the peat bog class. InSAR coherence peaked post-fire for moors and heathland and natural grassland classes inside the fire scar, but peat bog exposed from previous fires was less responsive. Overall, SAR was found to be effective for detecting the Bleaklow moorland wildfire scar and monitoring wildfire scar persistence in a degraded peat landscape up to 71 days later. Heavy precipitation amplified the SAR fire scar signal, with precipitation after wildfires being typical in UK moorlands. Further work is required to disentangle the effects of fire size, topography, and less generalized land‐cover classes on SAR intensity and InSAR coherence for detecting fire scars in degraded peat moorlands.