Long-Term Land Use/Land Cover Change Assessment of the Kilombero Catchment in Tanzania Using Random Forest Classification and Robust Change Vector Analysis

Different groups' grazing technique preferences in the Miombo woodlands of Eastern Tanzania

Grazing in Miombo woodlands is essential for the livelihoods of the pastoral and agropastoral communities that live adjacent to these fragile ecosystems. However, Miombo woodlands offer not only fodder for livestock but also fertile land for crop farming; hence, they are equally important to the farmers residing in these areas. Due to the importance of the Miombo woodlands for the livelihoods of several groups, the consumption of and competition for Miombo resources has increased over time and now threatens the overall health of the ecosystem. This study aimed to identify the grazing techniques practiced by different livelihood groups in Miombo woodlands; their preferences for different practices, as well as the factors that influence these preferences, so as to understand how sustainable grazing can be achieved for better ecosystem health. The study was conducted in Handeni, Kilombero, and Kilosa districts and covered pastoralists, agropastoralists, and farmers. We carried out focus group discussions, key informant interviews, and a survey of 246 respondents. Un-patterned rotational grazing was the most preferred grazing technique by all three groups, and the only technique the three groups shared a preference for. All the groups took a neutral stance in relation to continuous grazing. Their preferences for other grazing techniques differ. The study highlights the need to raise awareness amongst pastoralists about land ownership and management and recommends enhancing land property rights for all groups in order to harmonize livestock keeping and other land uses for achieving sustainable grazing and overall ecosystem health in the fragile Miombo woodlands of Tanzania.

Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods

Accurate and real-time land use/land cover (LULC) maps are important to provide precise information for dynamic monitoring, planning, and management of the Earth. With the advent of cloud computing platforms, time series feature extraction techniques, and machine learning classifiers, new opportunities are arising in more accurate and large-scale LULC mapping. In this study, we aimed at finding out how two composition methods and spectral–temporal metrics extracted from satellite time series can affect the ability of a machine learning classifier to produce accurate LULC maps. We used the Google Earth Engine (GEE) cloud computing platform to create cloud-free Sentinel-2 (S-2) and Landsat-8 (L-8) time series over the Tehran Province (Iran) as of 2020. Two composition methods, namely, seasonal composites and percentiles metrics, were used to define four datasets based on satellite time series, vegetation indices, and topographic layers. The random forest classifier was used in LULC classification and for identifying the most important variables. Accuracy assessment results showed that the S-2 outperformed the L-8 spectral–temporal metrics at the overall and class level. Moreover, the comparison of composition methods indicated that seasonal composites outperformed percentile metrics in both S-2 and L-8 time series. At the class level, the improved performance of seasonal composites was related to their ability to provide better information about the phenological variation of different LULC classes. Finally, we conclude that this methodology can produce LULC maps based on cloud computing GEE in an accurate and fast way and can be used in large-scale LULC mapping.

A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018–2020 Drought Years

Central Europe was hit by several unusually strong periods of drought and heat between 2018 and 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast forest areas in Germany. We present the first assessment of canopy cover loss in Germany for the period of January 2018–April 2021. Our approach makes use of dense Sentinel-2 and Landsat-8 time-series data. We computed the disturbance index (DI) from the tasseled cap components brightness, greenness, and wetness. Using quantiles, we generated monthly DI composites and calculated anomalies in a reference period (2017). From the resulting map, we calculated the canopy cover loss statistics for administrative entities. Our results show a canopy cover loss of 501,000 ha for Germany, with large regional differences. The losses were largest in central Germany and reached up to two-thirds of coniferous forest loss in some districts. Our map has high spatial (10 m) and temporal (monthly) resolution and can be updated at any time.

SUNet: Change Detection for Heterogeneous Remote Sensing Images from Satellite and UAV Using a Dual-Channel Fully Convolution Network

Change Detection in heterogeneous remote sensing images plays an increasingly essential role in many real-world applications, e.g., urban growth tracking, land use monitoring, disaster evaluation and damage assessment. The objective of change detection is to identify changes of geo-graphical entities or phenomena through two or more bitemporal images. Researchers have invested a lot in the homologous change detection and yielded fruitful results. However, change detection between heterogenous remote sensing images is still a great challenge, especially for change detection of heterogenous remote sensing images obtained from satellites and Unmanned Aerial Vehicles (UAV). The main challenges in satellite-UAV change detection tasks lie in the intensive difference of color for the same ground objects, various resolutions, the parallax effect and image distortion caused by different shooting angles and platform altitudes. To address these issues, we propose a novel method based on dual-channel fully convolution network. First, in order to alleviate the influence of differences between heterogeneous images, we employ two different channels to map heterogeneous remote sensing images from satellite and UAV, respectively, to a mutual high dimension latent space for the downstream change detection task. Second, we adopt Hough method to extract the edge of ground objects as auxiliary information to help the change detection model to pay more attention to shapes and contours, instead of colors. Then, IoU-WCE loss is designed to deal with the problem of imbalanced samples in change detection task. Finally, we conduct extensive experiments to verify the proposed method using a new Satellite-UAV heterogeneous image data set, named HTCD, which is annotated by us and has been open to public. The experimental results show that our method significantly outperforms the state-of-the-art change detection methods.

Complex Socio-Ecological Systems: Translating Narratives into Future Land Use and Land Cover Scenarios in the Kilombero Catchment, Tanzania

The Kilombero wetland in Tanzania is affected by advancing land use and land cover changes (LULCC), where we observe a conflict between development interests and the necessity of conservation measures to maintain the functionalities of the ecosystem. Thus, assessing patterns of LULCC is crucial to foresee potential future developments and to develop sustainable future management strategies. In this study, we use a multi-method scenario approach to assess the spatial implications and underlying driving forces of potential change by (1) developing a System Dynamics Causal Loop Diagram (CLD) to disentangle the underlying socio-economic and ecologic driving forces, (2) deriving a qualitative business-as-usual (BAU) and a conservation scenario from participatory narratives elaborated during a stakeholder workshop, and (3) quantifying the spatial implications of these scenarios with the Land Change Modeler (LCM). Results indicate that under the BAU assumption only 37% of the natural vegetation is expected to persist until 2030 in the wetland. In contrast, strict enforcement of protected areas (conservation scenario) halts further conversion of the wetland. However, both scenarios pinpoint considerable expansions of cropland in the western highlands with potentially serious impacts on catchment-wide hydrological processes. The produced qualitative and quantitative outputs reveal hotspots of possible future change and starting points for advisable further research and management interventions.

Monitoring of Urban Sprawl and Densification Processes in Western Germany in the Light of SDG Indicator 11.3.1 Based on an Automated Retrospective Classification Approach

By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning.