Towards precision forestry: Drought response from remote sensing-based disturbance monitoring and fine-scale soil information in Central Europe

Prolonged drought and susceptibility to biotic stressors induced an extensive calamity in Norway spruce (Picea abies (L.) Karst.) and widespread crown defoliation in European beech (Fagus sylvatica L.) in Central Europe. For future management decisions, it is crucial to link changes in canopy cover to site conditions. However, current knowledge on the role of soil properties for drought-induced forest disturbance is limited due to the scarcity and low spatial resolution of soil information. We present a fine-scale assessment on the role of soil properties for forest disturbance in Norway spruce and European beech derived from optical remote sensing. A forest disturbance modeling framework based on Sentinel-2 time series was applied on 340 km² in low mountain ranges of Central Germany. Spatio-temporal information on forest disturbance was calculated at 10 m spatial resolution in the period 2019-2021 and intersected with high-resolution soil information (1:10,000) based on roughly 2850 soil profiles. We found distinct differences in disturbed area, depending on soil type, texture, stoniness, effective rooting depth and available water capacity (AWC). For spruce, we found a polynomial relationship between AWC (R² = 0.7) and disturbance, with highest disturbed area (65 %) for AWC between 90 and 160 mm. Interestingly, we found no evidence for generally higher disturbance on shallow soils, although stands on the deepest soils were significantly less affected. Noteworthy, sites affected first did not necessarily exhibit highest proportions of disturbed area post-drought, indicating recovery or adaptation. We conclude that site- and species-specific understanding of drought impacts benefits from a combination of remote sensing and fine-scale soil information. Since our approach revealed which sites were affected first and most, it qualifies for prioritizing in situ monitoring activities to most vulnerable stands in acute drought conditions as well as for developing long-term strategies for reforestation and site-specific risk assessment for precision forestry.

Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

Appropriate crop type mapping to monitor and control land management is very important in developing countries. It can be very useful where digital cadaster maps are not available or usage of Remote Sensing (RS) data is not utilized in the process of monitoring and inventory. The main goal of the present research is to compare and assess the importance of optical RS data in crop type classification using medium and high spatial resolution RS imagery in 2018. With this goal, Landsat 8 (L8) and Sentinel-2 (S2) data were acquired over the Tashkent Province between the crop growth period of May and October. In addition, this period is the only possible time for having cloud-free satellite images. The following four indices “Normalized Difference Vegetation Index” (NDVI), “Enhanced Vegetation Index” (EVI), and “Normalized Difference Water Index” (NDWI1 and NDWI2) were calculated using blue, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands. Support-Vector-Machine (SVM) and Random Forest (RF) classification methods were used to generate the main crop type maps. As a result, the Overall Accuracy (OA) of all indices was above 84% and the highest OA of 92% was achieved together with EVI-NDVI and the RF method of L8 sensor data. The highest Kappa Accuracy (KA) was found with the RF method of L8 data when EVI (KA of 88%) and EVI-NDVI (KA of 87%) indices were used. A comparison of the classified crop type area with Official State Statistics (OSS) data about sown crops area demonstrated that the smallest absolute weighted average (WA) value difference (0.2 thousand ha) was obtained using EVI-NDVI with RF method and NDVI with SVM method of L8 sensor data. For S2-sensor data, the smallest absolute value difference result (0.1 thousand ha) was obtained using EVI with RF method and 0.4 thousand ha using NDVI with SVM method. Therefore, it can be concluded that the results demonstrate new opportunities in the joint use of Landsat and Sentinel data in the future to capture high temporal resolution during the vegetation growth period for crop type mapping. We believe that the joint use of S2 and L8 data enables the separation of crop types and increases the classification accuracy.

Development and application of GIS-based assessment of land-use impacts on water quality: A case study of the Kharaa River Basin

Effective analytical tools, such as geographical information systems (GIS) and multivariate analysis, help to deal with spatial data and complex interactions in watershed management. To investigate the impact of land-use on chemical water quality in the Mongolian Kharaa River Basin, the whole catchment and sub-catchments in relation to each sampling point were delineated. Chemical water quality over three seasons was assessed with GIS and RDA in a modeling approach with forward selection of variables and cluster analysis. The most powerful predictors of river water quality were altitude, settlements, forest, cropland, and distance to spring. In particular, this was true when instead of full sub-basins riparian buffer zones (max. 3 km) were considered. From a management perspective, this implies that the protection of riparian zones should be a priority in the Kharaa basin and similar river basins in Mongolia and Central Asia. Because of its positive effects on water quality, forest protection should be closely coupled with river basin management.

Identifying the key catastrophic variables of urban social-environmental resilience and early warning signal

Pursuit of sustainability requires a systematic approach to understand a system's specific dynamics to adapt and enhance from disturbances in social-environmental systems. We developed a systematic resilience assessment of social-environmental systems by connecting catastrophe theory and probability distribution equilibrium. Catastrophe models were used to calculate resilience shifts between slow and fast variables; afterwards, two resilience transition modes ("Less resilient" or "More resilient") were addressed by using probability distribution equilibrium analysis. A tipping point that occurs in "Less resilient" system suggests that the critical resilience transition can be an early warning signal of approaching threshold. Catastrophic shifts were explored between the interacting social-environmental sub-systems of land use and energy (fast variables) and environmental pollution (slow variables), which also identifies the critical factors in maintaining the integrated social-environmental resilience. Furthermore, the early warning signals enable the adaptability of urban systems and their resilience to perturbations, and provide guidelines for urban social-environmental management.

Chemical water quality gradients in the Mongolian sub-catchments of the Selenga River basin

Even though the Selenga is the main tributary to Lake Baikal in Russia, the largest part of the Selenga River basin is located in Mongolia. It covers a region that is highly diverse, ranging from almost virgin mountain zones to densely urbanized areas and mining zones. These contrasts have a strong impact on rivers and their ecosystems. Based on two sampling campaigns (summer 2014, spring 2015), we investigated the longitudinal water quality pattern along the Selenga and its tributaries in Mongolia. While headwater regions typically had a very good water quality status, wastewater from urban areas and impacts from mining were found to be main pollution sources in the tributaries. The highest nutrient concentrations in the catchment were found in Tuul River, and severely elevated concentrations of trace elements (As, Cd, Cu, Cr, Fe, Mn, Ni, Pb, Zn), nutrients (NH₄⁺, NO₂^-, NO₃^-, PO₄^3-), and selected major ions (SO₄^2-) were found in main tributaries of Selenga River. Moreover, trace element concentrations during spring 2015 (a time when many mines had not yet started operation) were markedly lower than in summer 2014, indicating that the additional metal loads measured in summer 2014 were related to mining activities. Nevertheless, all taken water samples in 2014 and 2015 from the main channel of the Mongolian Selenga River complied with the Mongolian standard (MNS 1998) for the investigated parameters.

Computing the biomass potentials for maize and two alternative energy crops, triticale and cup plant (Silphium perfoliatum L.), with the crop model BioSTAR in the region of Hannover (Germany)

BACKGROUND

Lower Saxony (Germany) has the highest installed electric capacity from biogas in Germany. Most of this electricity is generated with maize. Reasons for this are the high yields and the economic incentive. In parts of Lower Saxony, an expansion of maize cultivation has led to ecological problems and a negative image of bioenergy as such. Winter triticale and cup plant have both shown their suitability as alternative energy crops for biogas production and could help to reduce maize cultivation.

RESULTS

The model Biomass Simulation Tool for Agricultural Resources (BioSTAR) has been validated with observed yield data from the region of Hannover for the cultures maize and winter wheat. Predicted yields for the cultures show satisfactory error values of 9.36% (maize) and 11.5% (winter wheat). Correlations with observed data are significant (P < 0.01) with R = 0.75 for maize and 0.6 for winter wheat. Biomass potential calculations for triticale and cup plant have shown both crops to be high yielding and a promising alternative to maize in the region of Hanover and other places in Lower Saxony.

CONCLUSIONS

The model BioSTAR simulated yields for maize and winter wheat in the region of Hannover at a good overall level of accuracy (combined error 10.4%). Due to input data aggregation, individual years show high errors though (up to 30%). Nevertheless, the BioSTAR crop model has proven to be a functioning tool for the prediction of agricultural biomass potentials under varying environmental and crop management frame conditions.

Multi-Source Remotely Sensed Data Combination: Projection Transformation Gap-Fill Procedure

In this work a new gap-fill technique entitled projection transformation has been developed and used for filling missing parts of remotely sensed imagery. In general techniques for filling missing areas of an image break down into three main categories: first multi-source techniques that take advantages of other data sources (e.g. using cloud free images to fabricate the cloudy areas of other images); the second ones that fabricate the gap areas using non-gapped parts of an image itself, this group of techniques are referred to as single-source gap-fill procedures; and the third group which applies methods that are a combination of both mentioned techniques, therefore they are called hybrid gap- fill procedures. Here a new developed multi-source methodology called “projection transformation for filling a simulated gapped area in Landsat7/ETM+ imagery” is introduced. The auxiliary imagery for filling the gaps is an earlier obtained L7/ETM+ imagery. Quality of the technique was evaluated from three points of view: statistical accuracy measuring, visual comparison, and post classification accuracy assessment. These evaluation indicators are compared to the results obtained from a commonly used technique by the USGS, the Local Linear Histogram Matching (LLHM) [1]. Results show the superiority of our technique over LLHM in almost all aspects of accuracy.

Tradeoffs between income, biodiversity, and ecosystem functioning during tropical rainforest conversion and agroforestry intensification

Losses of biodiversity and ecosystem functioning due to rainforest destruction and agricultural intensification are prime concerns for science and society alike. Potentially, ecosystems show nonlinear responses to land-use intensification that would open management options with limited ecological losses but satisfying economic gains. However, multidisciplinary studies to quantify ecological losses and socioeconomic tradeoffs under different management options are rare. Here, we evaluate opposing land use strategies in cacao agroforestry in Sulawesi, Indonesia, by using data on species richness of nine plant and animal taxa, six related ecosystem functions, and on socioeconomic drivers of agroforestry expansion. Expansion of cacao cultivation by 230% in the last two decades was triggered not only by economic market mechanisms, but also by rarely considered cultural factors. Transformation from near-primary forest to agroforestry had little effect on overall species richness, but reduced plant biomass and carbon storage by approximately 75% and species richness of forest-using species by approximately 60%. In contrast, increased land use intensity in cacao agroforestry, coupled with a reduction in shade tree cover from 80% to 40%, caused only minor quantitative changes in biodiversity and maintained high levels of ecosystem functioning while doubling farmers' net income. However, unshaded systems further increased income by approximately 40%, implying that current economic incentives and cultural preferences for new intensification practices put shaded systems at risk. We conclude that low-shade agroforestry provides the best available compromise between economic forces and ecological needs. Certification schemes for shade-grown crops may provide a market-based mechanism to slow down current intensification trends.

Symptomatic deep venous thrombosis of the lower limb

BACKGROUND

Deep vein thrombosis (DVT) is a common problem in clinical practice causing severe complications. In this retrospective study, the distribution and extent of DVT in the lower limb in symptomatic patients were evaluated. Certain risk factors were also investigated.

METHODS

The venograms of 187 symptomatic individuals (postoperative, medical, and out-patient) with suspected DVT and symptoms having been present for less than 6 days, were reviewed. Seventy-seven limbs of 76 patients had DVT. Twenty-seven were male (age range 14-82 years, mean 57) and 49 female (age range 12-82, mean 56).

RESULTS

Age over 40 years and gender (female) were significant predisposing factors, (Z = 4.23, p < 0.001 and Z = 2.19, p < 0.05 respectively). Isolated calf DVT alone was the most common pattern (46%, 36 of 77 limbs), and no difference was seen between postoperative and medical patients (chi 2-test, p = 0.7). Postoperative DVT was found in 29 (38%) limbs. Prophylaxis with LMWH had been given in only 15 of them (52%). No difference was seen in the distribution and extent of thrombosis in relation to the prophylaxis with LMWH (chi 2-test, p = 0.34).

CONCLUSIONS

Identification of the predisposing factors may enable us to distinguish patients at high risk of developing DVT. The majority of the thrombi commenced in the calf veins and thus meticulous investigation of these veins in symptomatic patients with suspected DVT is necessary.