Sandra Eckert

Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data

Accurate estimation of aboveground biomass and carbon stock has gained importance in the context of the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. In order to develop improved forest stratum–specific aboveground biomass and carbon estimation models for humid rainforest in northeast Madagascar, this study analyzed texture measures derived from WorldView-2 satellite data. A forest inventory was conducted to develop stratum-specific allometric equations for dry biomass. On this basis, carbon was calculated by applying a conversion factor. After satellite data preprocessing, vegetation indices, principal components, and texture measures were calculated. The strength of their relationships with the stratum-specific plot data was analyzed using Pearson’s correlation. Biomass and carbon estimation models were developed by performing stepwise multiple linear regression. Pearson’s correlation coefficients revealed that (a) texture measures correlated more with biomass and carbon than spectral parameters, and (b) correlations were stronger for degraded forest than for non-degraded forest. For degraded forest, the texture measures of Correlation, Angular Second Moment, and Contrast, derived from the red band, contributed to the best estimation model, which explained 84% of the variability in the field data (relative RMSE = 6.8%). For non-degraded forest, the vegetation index EVI and the texture measures of Variance, Mean, and Correlation, derived from the newly introduced coastal blue band, both NIR bands, and the red band, contributed to the best model, which explained 81% of the variability in the field data (relative RMSE = 11.8%). These results indicate that estimation of tropical rainforest biomass/carbon, based on very high resolution satellite data, can be improved by (a) developing and applying forest stratum–specific models, and (b) including textural information in addition to spectral information.

Performance measures for object detection evaluation

We propose a new procedure for quantitative evaluation of object detection algorithms. The procedure consists of a matching stage for finding correspondences between reference and output objects, an accuracy score that is sensitive to object shapes as well as boundary and fragmentation errors, and a ranking step for final ordering of the algorithms using multiple performance indicators. The procedure is illustrated on a building detection task where the resulting rankings are consistent with the visual inspection of the detection maps.

Comparison of Automatic DSM Generation Modules by Processing IKONOS Stereo Data of an Urban Area

This study deals with the evaluation of four different image-processing software modules for the generation of digital surface models from very high-resolution stereo satellite data. The analysis was done in an urban area due to the growing interest in 3-D information over built-up areas. Depending on the different geometric model approaches used by the different software packages, shifts between 3.06-3.27 m between the digital surface models (DSMs) and the reference DSM were measured. The vertical RMSE of the four tested software packages range between 2.96-14.01 m. However, the visual evaluation resulted in a different ranking and does not confirm the quantitative results entirely. The results show that, depending on the building type to be extracted, the choice of software package may vary. The challenges of automatic DSM extraction in urban areas and the performance of current software package modules to address them are discussed. Potential improvements for automatic DSM extraction in urban areas are identified.

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis—competing for nutrients and water—has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pleiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the “Woody Weeds” project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pleiades data (OOB accuracy 0.83 and independent reference accuracy 0.87–0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80–0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pleiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures.

Population Growth and Its Expression in Spatial Built-up Patterns: The Sana’a, Yemen Case Study

Abstract: In light of rapid global urbanisation, monitoring and mapping of urban and population growth is of great importance. Population growth in Sana’a was investigated for this reason. The capital of the Republic of Yemen is a rapidly growing middle sized city where the population doubles almost every ten years. Satellite data from four different sensors were used to explore urban growth in Sana’a between 1989 and 2007, assisted by topographic maps and cadastral vector data. The analysis was conducted by delineating the built-up areas from the various optical satellite data, applying a fuzzy-rule-based composition of anisotropic textural measures and interactive thresholding. The resulting datasets were used to analyse urban growth and changes in built-up density per district, qualitatively as well as quantitatively, using a geographic information system. The built-up area increased by 87 % between 1989 and 2007. Built-up density has increased in all areas, but particularly in the northern and southern suburban districts, also reflecting the natural barrier of surrounding mountain ranges. Based on long-term population figures, geometric population growth was assumed. This hypothesis was used together with census data for 1994 and 2004 to estimate population figures for 1989 and 2007, resulting in overall growth of about 240%. By joining population figures to district boundaries, the spatial patterns of population distribution and growth were examined. Further, urban built-up growth and population changes over time were brought into relation in order to investigate

Large-scale land acquisition and its effects on the water balance in investor and host countries

This study examines the validity of the assumption that international large-scale land acquisition (LSLA) is motivated by the desire to secure control over water resources, which is commonly referred to as ‘water grabbing’. This assumption was repeatedly expressed in recent years, ascribing the said motivation to the Gulf States in particular. However, it must be considered of hypothetical nature, as the few global studies conducted so far focused primarily on the effects of LSLA on host countries or on trade in virtual water. In this study, we analyse the effects of 475 intended or concluded land deals recorded in the Land Matrix database on the water balance in both host and investor countries. We also examine how these effects relate to water stress and how they contribute to global trade in virtual water. The analysis shows that implementation of the LSLAs in our sample would result in global water savings based on virtual water trade. At the level of individual LSLA host countries, however, water use intensity would increase, particularly in 15 sub-Saharan states. From an investor country perspective, the analysis reveals that countries often suspected of using LSLA to relieve pressure on their domestic water resources—such as China, India, and all Gulf States except Saudi Arabia—invest in agricultural activities abroad that are less water-intensive compared to their average domestic crop production. Conversely, large investor countries such as the United States, Saudi Arabia, Singapore, and Japan are disproportionately externalizing crop water consumption through their international land investments. Statistical analyses also show that host countries with abundant water resources are not per se favoured targets of LSLA. Indeed, further analysis reveals that land investments originating in water-stressed countries have only a weak tendency to target areas with a smaller water risk.

Performance evaluation of building detection and digital surface model extraction algorithms: Outcomes of the PRRS 2008 Algorithm Performance Contest

This paper presents the initial results of the algorithm performance contest that was organized as part of the 5th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2008). The focus of the 2008 contest was automatic building detection and digital surface model (DSM) extraction. A QuickBird data set with manual ground truth was used for building detection evaluation, and a stereo Ikonos data set with a highly accurate reference DSM was used for DSM extraction evaluation. Nine submissions were received for the building detection task, and three submissions were received for the DSM extraction task. We provide an overview of the data sets, the summaries of the methods used for the submissions, the details of the evaluation criteria, and the results of the initial evaluation.

Estimating population and livestock density of mobile pastoralists and sedentary settlements in the south-eastern Lake Chad area.

Mobile pastoralists provide major contributions to the gross domestic product in Chad, but little information is available regarding their demography. The Lake Chad area population is increasing, resulting in competition for scarce land and water resources. For the first time, the density of people and animals from mobile and sedentary populations was assessed using randomly defined sampling areas. Four sampling rounds were conducted over two years in the same areas to show population density dynamics. We identified 42 villages of sedentary communities in the sampling zones; 11 (in 2010) and 16 (in 2011) mobile pastoralist camps at the beginning of the dry season and 34 (in 2011) and 30 (in 2012) camps at the end of the dry season. A mean of 64.0 people per km2 (95% confidence interval, 20.3-107.8) were estimated to live in sedentary villages. In the mobile communities, we found 5.9 people per km2 at the beginning and 17.5 people per km2 at the end of the dry season. We recorded per km2 on average 21.0 cattle and 31.6 small ruminants in the sedentary villages and 66.1 cattle and 102.5 small ruminants in the mobile communities, which amounts to a mean of 86.6 tropical livestock units during the dry season. These numbers exceed, by up to five times, the published carrying capacities for similar Sahelian zones. Our results underline the need for a new institutional framework. Improved land use management must equally consider the needs of mobile communities and sedentary populations.

The suitability of Macadamia and Juglans for cultivation in Nepal: an assessment based on spatial probability modelling using climate scenarios and in situ data

Global climate models predict temperature rises and changes in precipitation regimes that will shift regional climate zones and influence the viability of agricultural crops in Nepal. Understanding the influence of climate change on local climates and the suitability of specific sites for the production of individual crop types at present and in the future is crucial to increasing local crop resilience and ensuring the long-term viability of plantations—especially of high-value, perennial tree crops that require significant investment. This paper focuses on two cash crops, Macadamia and Juglans. A literature review summarises data on temperature, precipitation, and other macro- and microclimatic requirements of both genera. On this basis, we investigate the short- and long-term suitability of areas in Nepal for production of the two crops by means of a spatial model based on extensive in situ measurements, meteorological data, and climatic layers from the WorldClim dataset. Finally, we track changes in potential cultivation area under four Representative Concentration Pathways. Results show that climatic requirements for the cultivation of Macadamia and Juglans are fulfilled across a large part of Nepal at present and in the future: the total suitable area for both trees shrinks only marginally under all four scenarios. However, suitable areas shift considerably in spatial and altitudinal terms, meaning that some currently productive areas will become unproductive in the future, while currently unproductive ones will become productive. We conclude that the consideration of macro- and microclimatic changes in agricultural planning is essential to long-term agricultural success in Nepal.

Reducing landscape heterogeneity for improved land use and land cover (LULC) classification across the large and complex Ethiopian highlands

Abstract This paper presents a land use and land cover (LULC) classification approach that accounts landscape heterogeneity. We addressed this challenge by subdividing the study area into more homogeneous segments using several biophysical and socio-economic factors as well as spectral information. This was followed by unsupervised clustering within each homogeneous segment and supervised class assignment. Two classification schemes differing in their level of detail were successfully applied to four landscape types of distinct LULC composition. The resulting LULC map fulfills two major requirements: (1) differentiation and identification of several LULC classes that are of interest at the local, regional, and national scales, and (2) high accuracy of classification. The approach overcomes commonly encountered difficulties of classifying second-level classes in large and heterogeneous landscapes. The output of the study responds to the need for comprehensive LULC data to support ecosystem assessment, policy formulation, and decision-making towards sustainable land resources management.