Assessing and Mapping Rice Provisioning Ecosystem Services

Reisproduktion und damit verbundene Ökosystemleistungen sind abhängig von ökologischen und sozio-ökonomischen Faktoren. Auf wissenschaftlicher sowie auf politischer Ebene bestehen immer noch Wissenslücken zum Thema Entwicklung nachhaltiger Strategien für die Landwirtschaft und zur Verbesserung der Nahrungsmittelsicherheit. Landnutzer verstehen oftmals nicht die Probleme bezüglich Angebot und Nachfrage von Reisprodukten und Ökonomen können nicht alle landwirtschaftlichen Aspekte nachvollziehen. Diese Lücken haben zu einer Steigerung ökologischer Risiken (z.B. Dürre, Erosion und Verschmutzung) und zu Hungersnöten in Entwicklungsländern beigetragen. Darum ist es zwingend notwendig, einen integrativen Ansatz zu erarbeiten, welcher die Vorteile und das Wissen der verschiedenen Stakeholder, wie z.B. Landwirte, Politiker, Zwischenhändler und Konsumenten, integriert und eine ausbalancierte Bewertung ermöglicht.Rice production and related ecosystem services provision are strongly dependent on environmental characteristics and socio-economic factors. There are still various knowledge gaps among decision makers for the development of sustainable agriculture strategies and to improve food security. Farmers can often not clearly understand issues related to rice supply chains, while economists can often not clearly understand farming issues. These gaps have led to the increase of environmental risks (e.g. droughts, erosion and pollution), as well as famine threats in developing countries. Therefore, it is necessary to find out an integrated approach to balance the benefits and knowledge between stakeholders such as farmers, politicians, intermediate traders and consumers

Deep learning models integrating multi-sensor and -temporal remote sensing to monitor landslide traces in Vietnam

Landslides pose significant threats to lives and public infrastructure in mountainous regions. Real-time landslide monitoring presents challenges for scientists, often involving substantial costs and risks due to challenging terrain and instability. Recent technological advancements offer the potential to identify landslide-prone areas and provide timely warnings to local populations when adverse weather conditions arise. This study aims to achieve three key objectives: (1) propose indicators for detecting landslides in both field and remote sensing images; (2) develop deep learning (DL) models capable of automatically identifying landslides from fusion data of Sentinel-1 (SAR) and Sentinel-2 (optical) images; and (3) employ DL-trained models to detect this natural hazard in specific regions of Vietnam. Twenty DL models were trained, utilizing three U-shaped architectures, which include U-Net and U-Net3+, combined with different data-fusion choices. The training data consisted of multi-temporal Sentinel images and increased the accuracy of DL models using Adam optimizer to 99% in landslide detection with low loss function values. Using two bands of the Sentinel-1 could not define the characteristics of landslide traces. However, the integration between Sentinel-2 data and these bands makes the landslide detection process more effective. Therefore, the authors proposed a consolidated strategy based on three models: (1) UNet using four S2-bands, (2) UNet3+ using four S2-bands, (3) UNet using four S2-bands and VV S1-band, and (4) UNet using four S2-bands and VH S1-band for fully detect landslides. This integrated strategy uses the capabilities of each model and overcomes model result constraints to better describe landslide traces in varied geographical locations

New Approach to Assess Multi-Scale Coastal Landscape Vulnerability to Erosion in Tropical Storms in Vietnam

The increase of coastal erosion due to intense tropical storms and unsustainable urban development in Vietnam demands vulnerability assessments at different research scales. This study proposes (1) a new approach to classify coastlines and (2) suitable criteria to evaluate coastal vulnerability index (CVI) at national and regional/local scales. At the national scale, the Vietnamese coastline was separated into 72 cells from 8 coast types based on natural features, whereas the Center region of Vietnam was separated into 495 cells from 41 coast types based on both natural and socio-economic features. The assessments were carried out by using 17 criteria related to local land use/cover, socio-economic, and natural datasets. Some simplified variables for CVI calculation at the national scale were replaced by quantitative variables at regional/local scales, particularly geomorphology and socio-economic variables. As a result, more than 20% of Vietnam’s coastline has high CVI values, significantly more than 350 km of the coasts in the center part. The coastal landscapes with residential and tourism lands close to the beaches without protection forests have been strongly affected by storms’ erosion. The new approach is cost-effective in data use and processing and is ideal for identifying and evaluating the CVI index at different scales

Economic valuation of wetland ecosystem services in northeastern part of Vietnam

Coastal wetlands have been heavily exploited in the world. Valuation of ecosystem services help to provide the necessary improvements in coastal policy and management to monitor the driving forces of ecological changes in wetland ecosystems. In this study, the monetary values of wetland ecosystem services (WES) in the northeastern part of Vietnam were evaluated based on the integration of different quantitative methods, including interview, remote sensing, ecological modeling, statistic, and cost-benefit analyses. Particularly, seven wetland ecosystems and eleven services obtained from them were identified. As a result, the annual net WES value is evaluated at more than 390 million USD. The intensive and industrial aquaculture ecosystems in the northeastern part represent the highest economic value with more than 2100 USD/ha/year. A “planning” scenario was formulated to predict WES for the next ten years based on policy changes published by local managers. The framework developed here can serve as a decision support tool for environmental and economic managers in wetlands planning

Integrating Landsat 7 and 8 data to improve basalt formation classification: A case study at Buon Ma Thuot region, Central Highland, Vietnam

Cenozoic basalt regions contain various natural resources that can be used for socio-economic development. Different quantitative and qualitative methods have been applied to understand the geological and geomorphological characteristics of basalt formations. Nowadays the integration of remote sensing and geographic information systems (GIS) has become a powerful method to distinguish geological formations. In this paper, authors combined satellite and fieldwork data to analyze the structure and morphology of highland geological formations in order to distinguish two main volcanic eruption episodes. Based on remote sensing analysis in this study, different spectral band ratios were generated to select the best one for basalt classification. Lastly, two spectral combinations (including band ratios 4/3, 6/2, 7/4 in Landsat 8 and 3/2, 5/1, 7/3 in Landsat 7) were chosen for the Maximum Likelihood classification. The final geological map based on the integration of Landsat 7 and 8 outcomes shows precisely the boundary of the basalt formations with the accuracy up to 93.7%. This outcome contributed significantly to the correction of geological maps. In further studies, authors suggest the integration of Landsat 7 and 8 data in geological studies and natural resource and environmental management at both local and regional scales

Ecosystem service value assessment of a natural reserve region for strengthening protection and conservation

Ecosystem Services (ESs) refer to the direct and indirect contributions of ecosystems to human well-being and subsistence. Ecosystem valuation is an approach to assign monetary values to an ecosystem and its key ecosystem goods and services, generally referred to as Ecosystem Service Value (ESV). We have measured spatiotemporal ESV of 17 key ESs of Sundarbans Biosphere Reserve (SBR) in India using temporal remote sensing (RS) data (for years 1973, 1988, 2003, 2013, and 2018). These mangrove ecosystems are crucial for providing valuable supporting, regulatory, provisioning, and cultural ecosystem services. We have adopted supervised machine learning algorithms for classifying the region into different ecosystem units. Among the used machine learning models, Support Vector Machine (SVM) and Random Forest (RF) algorithms performed the most accurate and produced the best classification estimates with maximum kappa and an overall accuracy value. The maximum ESV (derived from both adjusted and non-adjusted units, million US$ year −1 ) is produced by mangrove forest, followed by the coastal estuary, cropland, inland wetland, mixed vegetation, and finally urban land. Out of all the ESs, the waste treatment (WT) service is the dominant ecosystem service of SBR. Additionally, the mangrove ecosystem was found to be the most sensitive to land use and land cover changes. The synergy and trade-offs between the ESs are closely associated with the spatial extent. Therefore, accurate estimates of ES valuation and mapping can be a robust tool for assessing the effects of poor decision making and overexploitation of natural resources on ESs. </p

Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam

The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted from the natural wetland to the human-made wetland. It is anticipated that the conversion rate continues to increase due to economic development and urbanization. Therefore, monitoring and assessment of the wetland are essential for the coastal vulnerability assessment and geo-ecosystem management. The aim of this study is to propose and verify a new deep learning approach to interpret 9 of 19 coastal wetland types classified in the RAMSAR and MONRE systems for the Tien Yen estuary of Vietnam. Herein, a Resnet framework was integrated into the U-Net to optimize the performance of the proposed deep learning model. The Sentinel-2, ALOS-DEM, and NOAA-DEM satellite images were used as the input data, whereas the output is the predefined nine wetland types. As a result, two ResU-Net models using Adam and RMSprop optimizer functions show the accuracy higher than 85%, especially in forested intertidal wetlands, aquaculture ponds, and farm ponds. The better performance of these models was proved, compared to Random Forest and Support Vector Machine methods. After optimizing the ResU-Net models, they were also used to map the coastal wetland areas correctly in the northeastern part of Vietnam. The final model can potentially update new wetland types in the southern parts and islands in Vietnam towards wetland change monitoring in real time

U-shaped deep-learning models for island ecosystem type classification, a case study in Con Dao Island of Vietnam

The monitoring of ecosystem dynamics utilises time and resources from scientists and land-use managers, especially in wetland ecosystems in islands that have been affected significantly by both the current state of oceans and human-made activities. Deep-learning models for natural and anthropogenic ecosystem type classification, based on remote sensing data, have become a tool to potentially replace manual image interpretation. This study proposes a U-Net model to develop a deep learning model for classifying 10 island ecosystems with cloud- and shadow-based data using Sentinel-2, ALOS and NOAA remote sensing data. We tested and compared different optimiser methods with two benchmark methods, including support vector machines and random forests. In total, 48 U-Net models were trained and compared. The U-Net model with the Adadelta optimiser and 64 filters showed the best result, because it could classify all island ecosystems with 93 percent accuracy and a loss function value of 0.17. The model was used to classify and successfully manage ecosystems on a particular island in Vietnam. Compared to island ecosystems, it is not easy to detect coral reefs due to seasonal ocean currents. However, the trained deep-learning models proved to have high performances compared to the two traditional methods. The best U-Net model, which needs about two minutes to create a new classification, could become a suitable tool for island research and management in the future

Assessing ecosystem service potentials to evaluate terrestrial, coastal and marine ecosystem types in Northern Germany - An expert-based matrix approach

In this article, a revised and enlarged version of a qualitative assessment matrix for the appraisal of ecosystem service potentials is introduced. The product is a simple tool for scoring terrestrial, coastal and marine ecosystem types with respect to their abilities to provide provisioning, regulating and cultural ecosystem services as well as indicators of ecosystem state by applying criteria of ecosystem integrity. The methodological steps of matrix development are described, and the emerging expert opinions are illustrated by characterizing different ecosystem types, analysing different ecosystem services and showing the outcomes of linked GIS-based mapping exercises. The applicability of the matrix is demonstrated by some case studies. The related uncertainties are characterized and discussed in context with limitations, arising challenges and conceptual problems. The tool is made available on the internet, and the authors are looking forward to critical checks and proposals for improvement