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.

Assessment of Climate Change and Human Activities on Vegetation Development in Northeast China.

Vegetation in Northeast China (NEC) has faced dual challenges posed by climate change and human activities. However, the factors dominating vegetation development and their contribution remain unclear. In this study, we conducted a comprehensive evaluation of the response of vegetation in different land cover types, climate regions, and time scales to water availability from 1990 to 2018 based on the relationship between normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). The effects of human activities and climate change on vegetation development were quantitatively evaluated using the residual analysis method. We found that the area percentage with positive correlation between NDVI and SPEI increased with time scales. NDVI of grass, sparse vegetation, rain-fed crop, and built-up land as well as sub-humid and semi-arid areas (drylands) correlated positively with SPEI, and the correlations increased with time scales. The negatively correlated area was concentrated in humid areas or areas covered by forests and shrubs. Vegetation water surplus in humid areas weakens with warming, and vegetation water constraints in drylands enhance. Moreover, potential evapotranspiration had an overall negative effect on vegetation, and precipitation was a controlling factor for vegetation development in semi-arid areas. A total of 53% of the total area in NEC showed a trend of improvement, which is mainly attributed to human activities (93%), especially through the implementation of ecological restoration projects in NEC. The relative role of human activities and climate change in vegetation degradation areas were 56% and 44%, respectively. Our findings highlight that the government should more explicitly consider the spatiotemporal heterogeneity of the influence of human activities and water availability on vegetation under changing climate and improve the resilience of regional water resources. The relative proportions and roles map of climate change and human activities in vegetation change areas provide a basis for government to formulate local-based management policies.

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.

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery.

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km² within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets.

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-, PO43-), and selected major ions (SO42-) 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.

Does climate change affect the incidence of skin and allergic diseases in Germany?

BACKGROUND: Skin diseases have great socio-economic importance in Germany due to their high and in some cases still-increasing prevalence. Little attention has yet been paid to the influence of the change in climate on these diseases. OBJECTIVE: Clarify the evidence of the effects of climate change on the prevalence of skin diseases and allergies in Germany. METHODS: First, a theoretical model of the possible mechanisms and influence factors of climate and weather was created for different disease groups (skin malignancies, allergies, skin infections). Then, a systematic online and manual literature search was made for model-derived key words.The relevant publications were selected and evaluated according to a priori criteria. RESULTS: From a total of n = 31,221 hits, n = 320 publications remained for evaluation.Changes in the following parameters can be regarded as essential climatologic factors influencing the prevalence of skin and allergic diseases: temperature, UV radiation, precipitation/humidity, cloudiness, and general weather conditions.There were only a few original articles addressed to this topic. Most of them address recurring phenomena (especially levels of airborne pollen), UV radiation or ozone (and the ozone hole). Quantitative statements, prognosis models and climate scenarios have not yet been published for Germany with respect to skin diseases. CONCLUSION: Only few scientific articles on the relationship between climate changes and the prevalence of skin diseases have been published. They do not allow a reliable statement on future developments. The outlook for changes in prevalence requires further clarification using published climate models.

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 missed parts of remotely sensed imagery. In general techniques for filling missed area of an image are broken down into three main categories: multi-source techniques that take the advantages of other data sources (e.g. using cloud free images to reconstruct the cloudy areas of other images); the second ones 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 third group contains methods that make up 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 the Landsat7/ETM+ imagery is introduced. The auxiliary imagery to filling the gaps is an earlier obtained L7/ETM+ imagery. Ability 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 as 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.