Assessment of Drought in Grasslands: Spatio – Temporal Analyses of Soil Moisture and Extreme Climate Effects in Southwestern Mongolia

Soil moisture plays an essential key role in the assessment of hydrological and meteorological droughts that may affect a wide area of the natural grassland and the groundwater resource. The surface soil moisture distribution as a function of time and space is highly relevant for hydrological, ecological, and agricultural applications, especially in water-limited or drought-prone regions. However, gauging soil moisture is challenging because of its high variability. While point-scale in-situ measurements are scarce, the remote sensing tools remain the only practical means to obtain regional and global-scale soil moisture estimates. A Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission ever designed to gauge the Earth’s surface soil moisture (SM) at the near-daily time scales. This work aims to evaluate the spatial and temporal patterns of SMOS soil moisture, determine the effect of the climate extremes on the vegetation growth cycle, and demonstrate the feasibility of using our drought model (GDI) the Gobi Drought Index. The GDI is based on the combination of SMOS soil moisture and several products from the MODIS satellite. We used this index for hydro-meteorological drought monitoring in Southwestern Mongolia. Firstly, we validated bias-corrected SMOS soil moisture for Mongolia by the in-situ soil moisture observations 2000 to 2015. Validation shows satisfactory results for assessing drought and water-stress conditions in the grasslands of Mongolia. The correlation analysis between SMOS and Normalized Difference Vegetation Index (NDVI) index in the various ecosystems shows a high correlation between the bias-corrected, monthly-averaged SMOS and NDVI data (R2 > 0.81). Further analysis of the SMOS and in situ SM data revealed a good match between spatial SM distribution and the rainfall events over Southwestern Mongolia. For example, during dry 2015, SM was decreased by approximately 30% across the forest-steppe and steppe areas. We also notice that both NDVI and rainfall can be used as indicators for grassland monitoring in Mongolia. The second part of this research, analyzed several dzud (specific type of climate winter disaster) events (2000, 2001, 2002, and 2010) related to drought, to comprehend the spatial and temporal variability of vegetation conditions in the Gobi region of Mongolia. We determined how these extreme climatic events affect vegetation cover and local grazing conditions using the seasonal aridity index (aAIZ), NDVI, and livestock mortality data. The NDVI is used as an indicator of vegetation activity and growth. Its spatial and temporal pattern is expected to reflect the changes in surface vegetation density and status induced by water-deficit conditions. The Gobi steppe areas showed the highest degree of vulnerability to climate, with a drastic decline of grassland in arid areas. We found that under certain dzud conditions, rapid regeneration of vegetation can occur. A thick snow layer acting as a water reservoir combined with high livestock losses can lead to an increase of the maximum August NDVI. The snowy winters can cause a 10 to 20-day early peak in NDVI and the following increase in vegetation growth. However, during a year with dry winter conditions, the vegetation growth phase begins later due to water deficiency and the entire year has a weaker vegetation growth. Generally, livestock loss and the reduction of grazing pressure was played a crucial role in vegetation recovery after extreme climatic events in Mongolia. At the last stage of our study, we develop an integrated Gobi drought index (GDI), derived from SMOS and LST, PET, and NDVI MODIS products. GDI can incorporate both, the meteorological and soil moisture drought patterns and sufficiently well represent overall drought conditions in the arid lands. Specifically, the monthly GDI and 1-month standardized precipitation index SPI showed significant correlations. Both of them are useful for drought monitoring in semi-arid lands. But, the SPI requires in situ data that are sparse, while the GDI is free from the meteorological network restriction. Consequently, we compared the GDI with other drought indices (VSWI, NDDI, NDWI, and in-situ SM). Comparison of these drought indices with the GDI allowed assessing the droughts’ behavior from different angles and quantified better their intensity. The GDI maps at fine-scale (< 1km) permit extending the applicability of our drought model to regional and local studies. These maps were generated from 2000 to 2018 across Southwestern Mongolia. Fine-scale GDI drought maps are currently limited to the whole territory for Mongolia but the algorithm is dynamic and can be transported to any region. The GDI drought index can be served as a useful tool for prevention services to detect extremely dry soil and vegetation conditions posing a risk of drought and groundwater resource depletion. It was able to detect the drought events that were underestimated by the National Drought Watch System in Mongolia. In summary, with the help of satellite, climatological, and geophysical data, the integrated GDI can be beneficial for vegetation drought stress characterization and can be a useful tool to monitor the effectiveness of pasture land restoration management practices for Mongolian livelihoods. The future application of the GDI can be extended to monitor potential impacts on water resources and agriculture in Mongolia, which have been impacted by long periods of drought

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Information on the spatial distribution of human disturbance is important for assessing and monitoring land degradation. In the Eastern Mongolian Steppe Ecosystem, one of the major driving factors of human-induced land degradation is the expansion of road networks mainly due to intensifications of oil exploration and exploitation. So far, neither the extents of road networks nor the extent of surrounding grasslands affected by the oil industry are monitored which is generally labor consuming. This causes that no information on the changes in the area which is affected by those disturbance drivers is available. Consequently, the study aim is to provide a cost-effective methodology to classify infrastructure and oil exploitation areas from remotely sensed images using object-based classifications with Random Forest. By combining satellite data with different spatial and spectral resolutions (PlanetScope, RapidEye, and Landsat ETM+), the product delivers data since 2005. For the classification variables, segmentation, spectral characteristics, and indices were extracted from all above mentioned imagery and used as predictors. Results show that overall accuracies of land use maps ranged 73%–93% mainly depending on satellites’ spatial resolution. Since 2005, the area of grassland disturbed by dirt roads and oil exploitation infrastructure increased by 88% with its highest expansion by 47% in the period 2005–2010. Settlements and croplands remained relatively constant throughout the 13 years. Comparison of multiscale classification suggests that, although high spatial resolutions are clearly beneficial, all datasets were useful to delineate linear features such as roads. Consequently, the results of this study provide an effective evaluation for the potential of Random Forest for extracting relatively narrow linear features such as roads from multiscale satellite images and map products that are possible to use for detailed land degradation assessments

Remote Sensing Application to Grassland Monitoring

Application of remote sensing to the management of grassland resources, the role this plays in developing sustainable grassland farming systems and opportunities for further development are outlined. Use of remote sensing technologies in grassland monitoring has a history of more than 30 years. Both fine- and coarse-grained remote sensing techniques are used to monitor and study grasslands. Fine-grained techniques are used to study landscape scale processes through the use of sensors providing spatial resolution of a few meters, whereas coarse-grained techniques are used to study catchment scale areas, and even entire biomes, using satellite-based sensors with a spatial resolution of kilometers. Remote sensing information is obtained from aerial photography, radar systems, video systems, and satellite-based sensors including the Landsat satellites’ Multispectral Scanner (MSS) and Thematic mapper (TM) and the National Oceanic and Atmospheric Administration (NOAA) polar orbiters’ Advanced Very High Resolution Radiometer (AVHRR). Various normalized difference vegetation indices (NDVI) have been developed and used extensively with data from the Landsat sensors (MSS and TM) and NOAA’s AVHRR. The NDVI has been used for grassland classification and inventory, monitoring grassland-use change, determination of site productivity and herbivore carrying capacity, water and soil conservation, integrated management of grassland pests, and suitability for recreational use and wild life protection. Special techniques have also been developed for monitoring where fires occur on grasslands. To date the remote sensing techniques have become a powerful tool for scientists, farmers and policy makers to study and manage grassland resources. World demand for sustainable development of grasslands will increase the reliance on remote sensing as a tool in grassland management. However, the adaptation of existing remote sensing technology in grassland management will require more scientists and technicians to be trained in both remote sensing and grassland science. Additional training programs targeting scientists in developing countries will be needed. System approaches will be required that lead to better understanding of the interfacing of ground and remote sensing data sets. There is also a need for research on low cost, high resolution systems to be flown from aircraft and helicopters using narrow filters for assessing the condition of grassland health

Geoecological analysis of forest distribution and post-disturbance tree regrowth in the forest-steppe of central Mongolia

Die mongolische Waldsteppe bildet den Übergangsbereich zwischen der sibirischen Taiga im Norden und der Wüste Gobi im Süden. Die Baumvegetation in diesem Ökoton ist, aufgrund es hochkontinentalen, semiariden Klimas, in besonderem Maße von Wasserverfügbarkeit abhängig. Waldflächen sind aufgrund des niedrigeren Evapotranspiration auf Nordhängen anzutreffen, während die Mehrheit der Flächen mit Grasvegetation bedeckt ist. Innerhalb der letzten Jahrzehnte haben Dürren zu vermehrter Wachstumsverminderung und erhöhter Baumsterblichkeit in der Waldsteppe geführt. Zudem haben Waldbrände und Waldnutzung, vor allem Abholzung und Waldweide, die Waldverbreitung deutlich reduziert und die Bestandsstruktur beeinflusst. Geländebefunde verdeutlichten, dass sich stark gestörte Waldflächen, verursacht durch Feuer und Kahlschlag, nicht gleichmäßig unter augenscheinlich gleichwertigen Bedingungen erholen. Hierbei wurden Unterschiede von rascher Baumsukzession bis hin zum kompletten Ausbleiben der Baumsukzession dokumentiert. Um diese Vegetationsmuster und die Entwicklung der Waldvegetation in der mongolischen Waldsteppe zu verstehen, ist eine Untersuchung der geoökologischen Steuerungsfaktoren dieses Umweltsystems zwingend erforderlich. Aus diesem Grund hat diese Arbeit folgende Ziele: (1) die Waldverbreitung und die Voraussetzungen für Waldwachstum im nördlichen Khangai Gebirge, welches ein Teil der Mongolischen Waldsteppe bildet, zu bestimmen (2) die entscheidenden Unterschiede in den Standortbedingungen, insbesondere der Bodeneigenschaften und der Permafrostverbreitung, von gesunden Waldstandorten sowie von gestörten Standorten mit und ohne Baumsukzession zu identifizieren. Eine Vielzahl von Methoden auf verschiedenen räumlichen Skalen wurde genutzt um dieses Umweltsystem weitreichend zu analysieren. Landsat, TanDEM-X und Sentinel Satellitenaufnahmen sowie Klimadaten wurden mit Hilfe von Anwendungen im Bereich der Fernerkundung und der Geoinformationssysteme genutzt, um die aktuelle und potentielle Waldverbreitung zu ermitteln. Des Weiteren wurden diese Anwendungen genutzt, um die Darstellung und Interpretation der gemessenen Daten zu unterstützen. Eine umfassende Recherche deutscher, englischer und russischer Literatur wurde durchgeführt, um die Anforderungen für das Wachstum der sibirischen Lärche, welche die vorherrschende Baumart in der mongolischen Waldsteppe ist, zu charakterisieren. Während der Geländeaufenthalte 2017 und 2018 wurden 54 Bodenprofilen analysiert, beprobt und hydrologische Bodenparameter gemessen. Zudem wurde die Permafrosttiefe mittels Sondierungen, Bodenprofilen, Temperaturmessungen und dem Einsatz eines Georadars bestimmt. Im Labor wurden die Bodenproben anschließend auf deren chemische, physikalische und hydrologische Eigenschaften untersucht. Die Ergebnisse weisen darauf hin, dass die potenzielle Waldfläche im nördlichen Khangai Gebirge erheblich größer ist als die aktuelle Waldverbreitung. Mehrere Brände haben die Waldfläche im Vergleich zum Jahr 1986 um 40 % reduziert. Zudem verringern Abholzung und Waldweide vor allem die untere Grenze der Waldverbreitung und öffnen die Waldstruktur. Basierend auf vorhandener Literatur ist Feuer keine grundsätzliche Bedrohung für die sibirische Lärche, da sie sich auf Brandflächen schnell reetablieren kann. Im Gegensatz dazu gefährden intensive Dürreperioden und der Einfluss des Menschen die Waldverbreitung in der mongolischen Waldsteppe zunehmend, was sich in einer erhöhten Baumsterblichkeit und einem eingeschränktem Baumnachwuchs wiederspiegelt. Die Untersuchungen der Böden der gestörten Waldflächen haben gezeigt, dass die Flächen mit Baumsukzession signifikant höhere Schluffgehalte im Vergleich zu den Flächen ohne Baumsukzession aufweisen. Messungen der pflanzenverfügbaren Feldkapazität bestätigen diesen Unterschied mit vergleichsweise höheren Werten in Böden unter Baumsukzessionxiii vergleichen zu Boden, auf denen keine Baumsukzession festgestellt wurde. Bei den bodenchemischen Eigenschaften, wie zum Beispiel Kohlenstoff- und Stickstoffvorräte, effektive Kationenaustauschkapazität und austauschbare Kationen, konnte kein signifikanter Unterschied zwischen den beiden Gruppen ausgemacht werden. Aus diesem Grund wird geschlussfolgert, dass bodenhydrologische Eigenschaften, welche die Wasserverfügbarkeit für Baumvegetation erhöhen, entscheidend für das Aufkommen von Bäumen auf gestörten Waldflächen sind. Die Permafrostverbreitung ist abhängig von der Beschattung durch eine geschlossene Vegetation, von der thermalen Isolation durch eine organische Auflage und von der Verfügbarkeit von Wasser. Im Untersuchungsgebiet wurde Permafrost unter dichten Waldbeständen in einer Tiefe von 50 bis 200 cm aufgefunden. Im Gegensatz dazu war Permafrost auf den gestörten Waldflächen nicht mehr nachweisbar. Daher hat die diskontinuierliche Permafrostverbreitung keinen Einfluss auf das Wiederaufkommen von Bäumen nach schweren Störungen. Basierend auf den Erkenntnissen ist zu schlussfolgern, dass die Verfügbarkeit von Wasser der entscheidende Faktor für das Wiederaufkommen von Baumvegetation auf gestörten Waldflächen in der mongolischen Waldsteppe ist. Jedoch unterliegt die Etablierung der Waldvegetation weiterer Einflüsse, insbesondere der klimatischen Bedingungen und des Einflusses des Menschen. Daher ist eine regulierende Forstwirtschaft zwingend nötig, um einen dramatischen Rückgang der Waldflächen in Zukunft zu verhindern.The Mongolian forest-steppe is an ecotone at the transition between the Siberian Taiga in the north and the Gobi Desert in the south. The highly continental, semiarid climate magnifies the importance of water availability for tree vegetation. Forests exclusively appear on northern slopes, due to less evapotranspiration, while the majority of surfaces in the area is covered by grass vegetation. Drought-induced growth reduction and increased tree mortality, intensified by climate change, was frequently observed in the forest-steppe during the last decades. Furthermore, forest fires and forest use, in particular logging and forest grazing, considerably reduced the forest distribution and affected the forest structure. Field investigations showed that severely disturbed forest stands, e.g. by fire or clear-cutting, do not recover equally under apparently similar conditions, ranging from quick tree regrowth to no regrowth of trees at all. It is obligatory to investigate the geoecological factors to understand the recovery pattern and the current development of the forest vegetation in the Mongolian forest-steppe. Therefore, two objectives were aimed in this research as follows: (1) to evaluate the forest vitality, and to determine the forest distribution and the specific requirements for tree growth in the northern Khangai Mountains, situated in the forest-steppe of central Mongolia, (2) to identify the difference in the site-specific conditions, including soil- and permafrost analyses, of healthy forest stands and disturbed forests with and without tree regrowth. A set of methods on different spatial scales was used to investigate this environment comprehensively. Remote sensing and GIS techniques were applied on Landsat, TanDEM-X and Sentinel satellite images as well as on climate data to characterise the present and potential forest distribution. Those techniques supported the illustration and interpretation of the conducted measurements. A literature review on English, German and Russian literature was carried out to identify the plant-specific needs of Siberian larch, the predominant tree species of the Mongolian forest-steppe. During two field campaigns, 2017 and 2018, 54 soil profiles were analysed, sampled, and hydraulic soil parameters were measured. Moreover, the permafrost depth was analysed using soil profiles, drillings, temperature measurements and ground-penetrating radar. Soil samples were investigated for their physical, chemical and hydrological properties in the laboratory. Statistical procedures were applied to the measured data. The results indicated that the potential forest area in the northern Khangai Mountains is substantially larger than the present forests. Several fires diminished the forested area by more than 40 % compared to the distribution in 1986. Moreover, logging and grazing livestock opened the forest structure and notably reduced the forest stands at their lower boundary. Based on the findings of the review, fire can be considered as a minor issue for Siberian larch forests due to their quick recovery on burned sites. In contrast, intensified drought events and human impact severely threaten the tree vegetation of the Mongolian forest-steppe by increased tree mortality and hampered tree regrowth. The soil investigations of the disturbed sites in the study area showed that silt contents of sites with regrowth of trees are significantly higher compared to those without regrowth of trees. Measurements on plant-available field capacity proved this difference by higher capacity in soils of areas with regrowth. Chemical soil properties, such as carbon and nitrogen stocks, effective cation exchange capacity and exchangeable cations, could not prove any significant differences between post-disturbance regrowth and no regrowth. Therefore, soil hydrological properties, which increase the water availability for tree vegetation, are decisive for the tree regrowth on disturbed sites. The permafrost distribution strongly depends on the shadowing of closed vegetation, the thermal isolation by a thick organic layer and the water availability. Dense forest stands contained permafrost within a depth of 50 to 200 cm. On disturbed sites,xi permafrost was not encountered anymore. Thus, the discontinuous permafrost distribution is vanished after severe disturbance and does not influence the tree regrowth pattern. Based on the findings, it is concluded that water availability is the crucial factor for the tree regrowth pattern after disturbances in the Mongolian forest-steppe. However, the reestablishment of forest vegetation underlies other influences, in particular climate conditions and human impact. Regulating forest management is therefore needed to prevent a dramatic decline of forested areas in future.2021-09-1

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Exploring the Use of Sentinel-2 Data to Monitor Heterogeneous Effects of Contextual Drought and Heatwaves on Mediterranean Forests

The use of satellite data to detect forest areas impacted by extreme events, such as droughts, heatwaves, or fires is largely documented, however, the use of these data to identify the heterogeneity of the forests’ response to determine fine scale spatially irregular damage is less explored. This paper evaluates the health status of forests in southern Italy affected by adverse climate conditions during the hot and dry summer of 2017, using Sentinel-2 images (10m) and in situ data. Our analysis shows that the post-event—NDVI (Normalized Difference Vegetation Index) decrease, observed in five experimental sites, well accounts for the heterogeneity of the local response to the climate event evaluated in situ through the Mannerucci and the Raunkiaer methods. As a result, Sentinel-2 data can be effectively integrated with biological information from field surveys to introduce continuity in the estimation of climate change impacts even in very heterogeneous areas whose details could not be captured by lower resolution observations. This integration appears to be a successful strategy in the study of the relationships between the climate and forests from a dynamical perspective

Desertification

IPCC SPECIAL REPORT ON CLIMATE CHANGE AND LAND (SRCCL) Chapter 3: Climate Change and Land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystem

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools