Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey

Long-term monitoring of the extent and intensity of irrigation systems is needed to track crop water consumption and to adapt land use to a changing climate. We mapped the expansion and changes in the intensity of irrigated dry season cropping in Turkey´s Southeastern Anatolia Project annually from 1990 to 2018 using Landsat time series. Irrigated dry season cropping covered 5,779 km² (± 479 km²) in 2018, which represents an increase of 617% over the study period. Dry season cropping was practiced on average every second year, but spatial variability was pronounced. Increases in dry season cropping frequency were observed on 40% of the studied croplands. The presented maps enable the identification of land use intensity hotspots at 30 m spatial resolution, and can thus aid in assessments of water consumption and environmental degradation. All maps are openly available for further use at https://doi.org/10.5281/zenodo.4287661.Peer Reviewe

Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes

Image segmentation is a cost-effective way to obtain information about the sizes and structural composition of agricultural parcels in an area. To accurately obtain such information, the parameters of the segmentation algorithm ought to be optimized using supervised or unsupervised methods. The difficulty in obtaining reference data makes unsupervised methods indispensable. In this study, we evaluated an existing unsupervised evaluation metric that minimizes a global score (GS), which is computed by summing up the intra-segment uniformity and inter-segment dissimilarity within a segmentation output. We modified this metric and proposed a new metric that uses absolute difference to compute the GS. We compared this proposed metric with the existing metric in two optimization approaches based on the Multiresolution Segmentation (MRS) algorithm to optimally delineate agricultural parcels from Sentinel-2 images in Lower Saxony, Germany. The first approach searches for optimal scale while keeping shape and compactness constant, while the second approach uses Bayesian optimization to optimize the three main parameters of the MRS algorithm. Based on a reference data of agricultural parcels, the optimal segmentation result of each optimization approach was evaluated by calculating the quality rate, over-segmentation, and under-segmentation. For both approaches, our proposed metric outperformed the existing metric in different agricultural landscapes. The proposed metric identified optimal segmentations that were less under-segmented compared to the existing metric. A comparison of the optimal segmentation results obtained in this study to existing benchmark results generated via supervised optimization showed that the unsupervised Bayesian optimization approach based on our proposed metric can potentially be used as an alternative to supervised optimization, particularly in geographic regions where reference data is unavailable or an automated evaluation system is sought.Peer Reviewe

Mapping woody plant community turnover with space-borne hyperspectral data: a case study in the Cerrado

Effective conservation measures require the knowledge on the spatial patterns of species communities and their turnover. This knowledge is, however, many times lacking, particularly so for complex systems. On the other hand, recent developments have resulted in tools that enable the mapping of these patterns from remote sensing data, such as Sparse Generalized Dissimilarity Modelling (SGDM). SGDM is a two-stage approach, which combines a Sparse Canonical Component analysis and a Generalized Dissimilarity Modelling (GDM), thus being designed to deal with high-dimensional data to predict community turnover in GDM. In this study, we use space-borne hyperspectral data to map woody plant community patterns collected in two study sites in the Cerrado (Brazilian savannah), namely, the Parque Estadual da Serra Azul (PESA) in Mato Grosso state and Parque Nacional da Chapada dos Veadeiros (PNCV) in Goiás state. Field data were collected in both study sites, following a systematic sampling scheme adapted for the Cerrado. The Cerrado is the most diverse of all the world's savannahs, and while holding a high diversity and endemism of species, this biome is mostly unprotected and understudied. We used Hyperion data acquired over the two study sites, which were subject to data pre-processing (including radiometric and geometric corrections, as well as correction for sensor errors) and quality screening before analysis. Our models were used to map woody plant community patterns and turnover for the study areas. We also inspected the Hyperion spectral bands which most contributed in the SGDM, for each site. Furthermore, the modelled patterns were interpreted with respect to the ecological characteristics of the respective species, this way further enhancing our understanding of this complex system. This study has demonstrated that this approach is suitable for mapping woody plant communities in heterogeneous systems, based on combined field and space-borne hyperspectral data

Landsat derived land surface phenology metrics for the characterization of natural vegetation in the Brazilian savanna

Die Brasilianische Savanne, auch bekannt als der Cerrado, bedeckt ca. 24% der Landoberfläche Brasiliens. Der Cerrado ist von einer einzigartigen Biodiversität und einem starken Gradienten in der Vegetationsstruktur gekennzeichnet. Großflächige Landnutzungsveränderungen haben dazu geführt, dass annähernd die Hälfte der Cerrado in bewirtschaftetes Land umgewandelt wurde. Die Kartierung ökologischer Prozesse ist nützlich, um naturschutzpolitische Entscheidungen auf räumlich explizite Informationen zu stützen, sowie um das Verständnis der Ökosystemdynamik zu verbessern. Neue Erdbeobachtungssensoren, frei verfügbare Daten, sowie Fortschritte in der Datenverarbeitung ermöglichen erstmalig die großflächige Erfassung saisonaler Vegetationsdynamiken mit hohem räumlichen Detail. In dieser Arbeit wird der Mehrwert von Landsat-basierten Landoberflächenphänologischen (LSP) Metriken, für die Charakterisierung der Cerrado-Vegetation, hinsichtlich ihrer strukturellen und phänologischen Diversität, sowie zur Schätzung des oberirdischen Kohlenstoffgehaltes (AGC), analysiert. Die Ergebnisse zeigen, dass LSP-Metriken die saisonale Vegetatiosdynamik erfassen und für die Kartierung von Vegetationsphysiognomien nützlich sind, wobei hier die Grenzen der Einteilung von Vegetationsgradienten in diskrete Klassen erreicht wurden. Basierend auf Ähnlichkeiten in LSP wurden LSP Archetypen definiert, welche die Erfassung und Darstellung der phänologischen Diversität im gesamten Cerrado ermöglichten und somit zur Optimierung aktueller Kartierungskonzepte beitragen können. LSP-Metriken ermöglichten die räumlich explizite Quantifizierung von AGC in drei Untersuchungsgebieten und sollten bei zukünftigen Kohlenstoffschätzungen berücksichtigt werden. Die Erkenntnisse dieser Dissertation zeigen die Vorteile und Nutzungsmöglichkeiten von LSP Metriken im Bereich der Ökosystemüberwachung und haben demnach direkte Implikationen für die Entwicklung und Bewertung nachhaltiger Landnutzungsstrategien.The Brazilian savanna, known as the Cerrado, covers around 24% of Brazil. It is characterized by a unique biodiversity and a strong gradient in vegetation structure. Land-use changes have led to almost half of the Cerrado being converted into cultivated land. The mapping of ecological processes is, therefore, an important prerequisite for supporting nature conservation policies based on spatially explicit information and for deepening our understanding of ecosystem dynamics. New sensors, freely available data, and advances in data processing allow the analysis of large data sets and thus for the first time to capture seasonal vegetation dynamics over large extents with a high spatial detail. This thesis aimed to analyze the benefits of Landsat based land surface phenological (LSP) metrics, for the characterization of Cerrado vegetation, regarding its structural and phenological diversity, and to assess their relation to above ground carbon. The results revealed that LSP metrics enable to capture the seasonal dynamics of photosynthetically active vegetation and are beneficial for the mapping of vegetation physiognomies. However, the results also revealed limitations of hard classification approaches for mapping vegetation gradients in complex ecosystems. Based on similarities in LSP metrics, which were for the first time derived for the whole extent of the Cerrado, LSP archetypes were proposed, which revealed the spatial patterns of LSP diversity at a 30 m spatial resolution and offer potential to enhance current mapping concepts. Further, LSP metrics facilitated the spatially explicit quantification of AGC in three study areas in the central Cerrado and should thus be considered as a valuable variable for future carbon estimations. Overall, the insights highlight that Landsat based LSP metrics are beneficial for ecosystem monitoring approaches, which are crucial to design sustainable land management strategies that maintain key ecosystem functions and services

Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques

Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes in their functioning and may ultimately threaten ecosystem service provision. It is, therefore, necessary to monitor these changes in order to understand their impacts and to support management decisions that help ensuring sustainability. Remote sensing has proven to be a valuable tool for these purposes, and especially hyperspectral sensors are expected to provide valuable data for quantitative characterization of land change processes. In this study, simulated EnMAP data were used for mapping shrub cover fractions along a gradient of shrub encroachment, in a study region in southern Portugal. We compared three machine learning regression techniques: Support Vector Regression (SVR); Random Forest Regression (RF); and Partial Least Squares Regression (PLSR). Additionally, we compared the influence of training sample size on the prediction performance. All techniques showed reasonably good results when trained with large samples, while SVR always outperformed the other algorithms. The best model was applied to produce a fractional shrub cover map for the whole study area. The predicted patterns revealed a gradient of shrub cover between regions affected by special agricultural management schemes for nature protection and areas without land use incentives. Our results highlight the value of EnMAP data in combination with machine learning regression techniques for monitoring gradual land change processes

hyDRaCAT Spectral Reflectance Library: Hyperspectral Field Spectroscopy and Field Spectro-Goniometry of Siberian and Alaskan Tundra

hyDRaCAT Spectral Reflectance Library for tundra provides the surface reflectance data and the bidirectional reflectance distribution function (BRDF) of important Arctic tundra vegetation communities at representative Siberian and Alaskan tundra sites. The aim of this dataset is the hyperspectral and spectro-directional reflectance characterization as basis for the extraction of vegetation parameters, and the normalization of BRDF effects in off-nadir and multi-temporal remote sensing data. The spectroscopic and field spectro-goniometric measurements were undertaken on the YAMAL2011 expedition of representative Siberian vegetation fields and on the North American Arctic Transect NAAT2012 expedition of Alaskan vegetation fields both belonging to the Greening-of-the-Arctic (GOA) program. For the field spectroscopy each 100 m2 vegetation study grid was divided into quadrats of 1 × 1 m. The averaged reflectance of all quadrats represents the spectral reflectance at the scale of the whole grid at the 10 × 10 m scale. For the surface radiometric measurements two GER1500 portable field spectroradiometers (Spectra Vista Corporation, Poughkeepsie, NY, USA) were used. The GER1500 measures radiance across the wavelength range of 350-1,050 nm, with sampling intervals of 1.5 nm and a radiance accuracy of 1.2 × 10**-1 W/cm**2/nm/sr. In order to increase the signal-to-noise ratio, 32 individual measurements were averaged per one target scan. To minimize variations in the target reflectance due to sun zenith angle changes, all measurements at one study location have been performed under similar sun zenith angles and during clear-sky conditions. The field spectrometer measurements were carried out with a GER1500 UV-VIS spectrometer The spectrogoniometer measurements were carried out with a self-designed spectro-goniometer: the Manual Transportable Instrument platform for ground-based Spectro-directional observations (ManTIS, patent publication number: DE 10 2011 117 713.A1). The ManTIS was equipped with the GER1500 spectrometer allowing spectro-directional measurements with up to 30° viewing zenith angle by full 360° viewing azimuth angles. Measurements in central Yamal (Siberia) at the research site 'Vaskiny Dachi' were carried out in the late summer phenological state from August 12 2011 to August 28 2011. All measurements in Alaska along the North South transect on the North Slope were taken between 29 June and 11 July 2012, ensuring that the vegetation was in the same phenological state near peak growing season

sgdm: An R Package for Performing Sparse Generalized Dissimilarity Modelling with Tools for gdm

Global biodiversity change creates a need for standardized monitoring methods. Modelling and mapping spatial patterns of community composition using high-dimensional remotely sensed data requires adapted methods adequate to such datasets. Sparse generalized dissimilarity modelling is designed to deal with high dimensional datasets, such as time series or hyperspectral remote sensing data. In this manuscript we present sgdm, an R package for performing sparse generalized dissimilarity modelling (SGDM). The package includes some general tools that add functionality to both generalized dissimilarity modelling and sparse generalized dissimilarity modelling. It also includes an exemplary dataset that allows for the application of SGDM for mapping the spatial patterns of tree communities in a region of natural vegetation in the Brazilian Cerrado.Peer Reviewe

Ground-Based Hyperspectral Characterization of Alaska Tundra Vegetation along Environmental Gradients

Remote sensing has become a valuable tool in monitoring arctic environments. The aim of this paper is ground-based hyperspectral characterization of Low Arctic Alaskan tundra communities along four environmental gradients (regional climate, soil pH, toposequence, and soil moisture) that all vary in ground cover, biomass, and dominating plant communities. Field spectroscopy in connection with vegetation analysis was carried out in summer 2012, along the North American Arctic Transect (NAAT). Spectral metrics were extracted, including the averaged reflectance and absorption-related metrics such as absorption depths and area of continuum removal. The spectral metrics were investigated with respect to “greenness”, biomass, vegetation height, and soil moisture regimes. The results show that the surface reflectances of all sites are similar in shape with a reduced near-infrared (NIR) reflectance that is specific for low-growing biomes. The main spectro-radiometric findings are: (i) Southern sites along the climate gradient have taller shrubs and greater overall vegetation biomass, which leads to higher reflectance in the NIR. (ii) Vegetation height and surface wetness are two antagonists that balance each other out with respect to the NIR reflectance along the toposequence and soil moisture gradients. (iii) Moist acidic tundra (MAT) sites have “greener” species, more leaf biomass, and green-colored moss species that lead to higher pigment absorption compared to moist non-acidic tundra (MNT) sites. (iv) MAT and MNT plant community separation via narrowband Normalized Difference Vegetation Index (NDVI) shows the potential of hyperspectral remote sensing applications in the tundra

Quantification of Ownership Concentration from Cadastral Records of Agricultural Land in Märkisch-Oderland

Concentration of ownership of agricultural land in fewer hands has often been highlighted as a side effect of the increasing capital resources that are allocated to the agricultural sector. One key concern is that, as more land concentrates in fewer hands, the few large actors can exert power on land markets by dominating prices through regulating supply and demand of land. Unfortunately, empirical evidence on the degree of market power on land markets remains scarce, mainly due to a lack of ownership data. We shed light on the concentration of ownership in agricultural land by analyzing the complete cadastral records of agricultural land at one point in time and for one district in the federal state of Brandenburg. We present the workflow to process the cadastral data for subsequent analysis in GIS and statistical software packages. For our study area, we derive relative and absolute concentration measures for the ownership in agricultural land. Our results suggest high relative concentration on the district level with a Gini coefficient of 0.85. Within the district, we see varying degrees of land concentration, albeit spatial clusters of high and low concentration. Our methodological approach holds great promise because it can be expanded to larger areas and different time periods. However, the cadastral data does not allow to infer on the underlying corporate structures, such as those of large investors who may own several agricultural companies. Such corporative structures may, through their local subsidiaries that could be spatially clustered, exert market power to the detriment of local land supply markets. Additional data and analysis using, for example, registers of company registers, need to be combined with the cadastral data to reveal such structures