Remote Sensing Solutions GmbH
  • Baierbrunn, Bavaria, Germany
Recent publications
Wetlands are among the most vulnerable, threatened, valuable, diverse, and heterogeneous ecosystems existing on our planet. While they provide invaluable ecosystem services to our society, they have been declining globally for many centuries. Monitoring of these changes is necessary for implementing efficient conservation policies and sustainable management schemes. Earth observation techniques can support the effort of monitoring, assessing, and inventorying wetlands at different scales with ever growing capabilities and toolsets. While the GEO‐Wetlands initiative provides a framework for collaboratively increasing and utilizing these capabilities, global stakeholders like the Ramsar Convention on Wetlands and U.N. Environment are starting to adopt EO‐based methods in their guidelines and technical reports. Many challenges still remain, although different projects and case studies successfully demonstrate the opportunities provided by the growing data archives, analysis algorithms, and processing capabilities. Many of these demonstrations focus on local wetland sites. The mapping and inventorying, specifically of vegetated wetlands, on national or even global scales remains a challenge for the wetlands and EO communities for years to come. Collaboration and partnership between different stakeholders of both communities are key for success. Initiatives like GEO‐Wetlands, in cooperation with global stakeholders, need to provide the framework for this collaborative effort.
The knowledge of tree species distribution at a national scale provides benefits for forest management practices and decision making for site-adapted tree species selection. An accurate assignment of tree species in relation to their location allows conclusions about potential resilience or vulnerability to biotic and abiotic factors. Identifying areas at risk helps the long-term strategy of forest conversion towards a natural, diverse, and climate-resilient forest. In the framework of the national forest inventory (NFI) in Germany, data on forest tree species are collected in sample plots, but there is a lack of a full coverage map of the tree species distribution. The NFI data were used to train and test a machine-learning approach that classifies a dense Sentinel-2 time series with the result of a dominant tree species map of German forests with seven main tree species classes. The test of the model’s accuracy for the forest type classification showed a weighted average F1-score for deciduous tree species (Beech, Oak, Larch, and Other Broadleaf) between 0.77 and 0.91 and for non-deciduous tree species (Spruce, Pine, and Douglas fir) between 0.85 and 0.94. Two additional plausibility checks with independent forest stand inventories and statistics from the NFI show conclusive agreement. The results are provided to the public via a web-based interactive map, in order to initiate a broad discussion about the potential and limitations of satellite-supported forest management.
Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.
Increasing conflicts between farmers and pastoralists continue to be a major challenge in the Sahel. Political and social factors are in tandem important underlying determinants for conflicts in the region, which are amplified by the variability and scarcity of natural resources, often as a result of climate variability and climate change. This study aimed at holistically assessing the main environmental parameters that influence the patterns of seasonal migratory movements (transhumance) in a transboundary area in the southern Republic of Chad and northern Central African Republic through a broad set of Earth observation (EO) data and data from the Transhumance Tracking Tool. A spatial model was applied to the datasets to determine the spatiotemporal dynamics of environmental suitability that reflects suitable areas and corridors for pastoralists. A clear difference in environmental suitability between the origin and destination areas of herders was found in the dry season, proving the main reason for pastoralists’ movements, i.e., the search for grazing areas and water. Potential conflict risk areas could be identified, especially along an agricultural belt, which was proven by conflict location data. The results demonstrate the potential and innovation of EO-derived environmental information to support the planning of transhumance corridors and conflict prevention in the Sahel. In the future, a combination of real-time tracking of herders and EO-derived information can eventually lead to the development of an early warning system for conflicts along transhumance corridors in the Sahel.
Oceanic islands harbour a disproportionately high number of endemic and threatened species. Rapidly growing human populations and tourism are posing an increasing threat to island biota, yet the ecological consequences of these human land uses on small oceanic island systems have not been quantified. Here, we investigated and compared the impact of tourism and urban island development on ground-associated invertebrate biodiversity and habitat composition on oceanic islands. To disentangle tourism and urban land uses, we investigated Indo-Pacific atoll islands, which either exhibit only tourism or urban development, or remain uninhabited. Within the investigated system, we show that species richness, abundance and Shannon diversity of the investigated invertebrate community are significantly decreased under tourism and urban land use, relative to uninhabited islands. Remote-sensing-based spatial data suggest that habitat fragmentation and a reduction in vegetation density are having significant effects on biodiversity on urban islands, whereas land use/cover changes could not be linked to the documented biodiversity loss on tourist islands. This offers the first direct evidence for a major terrestrial invertebrate loss on remote oceanic atoll islands due to different human land uses with yet unforeseeable long-term consequences for the stability and resilience of oceanic island ecosystems.
Wetlands are abundant across the African continent and provide a range of ecosystem services on different scales but are threatened by overuse and degradation. It is essential that national governments enable and ensure the sustainable use of wetland resources to maintain these services in the long run. As informed management decisions require reliable, up-to-date, and large coverage spatial data, we propose a modular Earth observation-based framework for the geo-localisation and characterization of wetlands in East Africa. In this study, we identify four major challenges in spatial data supported wetland management and present a framework to address them. We then apply the framework comprising Wetland Delineation, Surface Water Occurrence, Land Use/Land Cover classification and Wetland Use Intensity for the whole of Rwanda and evaluate the ability of these layers to meet the identified challenges. The layers' spatial and temporal characteristics make them combinable and the information content, of each layer alone as well as in combination, renders them useful for different wetland management contexts.
In the Eastern Africa highlands, the gradual transformation of natural ecosystems to smallholding coffee-based agrosystems has resulted in more fragmented landscapes. Major pests of coffee find appropriate living conditions leading to high infestation rates and the need for smallholder farmers to implement pest control measures. This study aims to understand the influence of landscapes on the ecology of three major coffee pests: the coffee berry borer (CBB), Hypothenemus hampei, and the Antestia bugs Antestiopsis thunbergii (ABT) and A. facetoides (ABF). The study was conducted on a typical smallholder coffee-based landscape in central Kenya. The pest abundance was assessed monthly for two years in a network of 30 coffee plots spread across the coffee agro-ecological subzones (AEsZ), namely upper midland UM1 and UM2, and the transition zones between UM1 and UM2 and between UM2 and UM3, herein referred to as TZ1 and TZ2, respectively. Landscape metrics, viz. patch density, Euclidean nearest neighbour distance, proximity index, contagion index, interspersion and juxtaposition index were derived from a spatially explicit land cover map, based on 10 m Sentinel 2 data for nine buffer zones of radius ranging from 50 m to 1000 m around each sampled plot. Redundancy analysis (RDA) was used to establish the relationships between the observed pest abundances and landscape metrics, elevation, and AEsZ. Landscape indicators achieved the highest correlation with the pest abundances within a 300 m radius (Adjusted R² > 0.5). Whereas beyond 300 m landscape scale, the predictor variables resulted in weak relationships (Adjusted R² < 0.5) between the pests abundance and landscape metrics. We noted a strong influence of elevation and adjacency to cropland on Antestia bug populations. Specifically, ABF populations were negatively correlated with low elevation, whereas ABT’s were positively correlated with high elevation zone. On the other hand, CBB was strongly influenced by contiguous coffee patches, especially in UM1 and UM2. Therefore, we recommend reducing connectivity between coffee patches for the management of CBB, whereas further studies should be conducted to identify secondary hosts of Antestia bugs that should not be adjacent or within coffee stands.
Landscape fragmentation and habitat loss at multiple scales directly affect species abundance, diversity and their productivity. There is a paucity of information about the effect of the landscape structure and diversity on honeybee colony strength in Africa. Here, we present new insights into the relationship between landscape metrics such as patch size, shape, connectivity, composition and configuration and honeybee (Apis mellifera) colony strength characteristics. Remote sensing‐based landscape variables were linked to honeybee colony strength variables in a typical highly fragmented small holder agro‐ecological region in Kenya. We examined colonies in six sites with varying degrees of land degradation, during the period from 2017 to 2018. Landscape structure was first mapped using medium resolution bi‐temporal Sentinel‐1 and Sentinel‐2 satellite imagery with an optimized random forest model. The influence of the surrounding landscape matrix was then constrained to two buffer distances i.e., 1 km representing the local foraging scale and 2.5 km representing the wider foraging scale around each investigated apiary and for each of the six sites. The results of zero‐inflated negative binomial regression with mixed effects showed that lower complexity of patch geometries represented by fractal dimension and reduced proportions of croplands were most influential at local foraging scales (1 km) from the apiary. In addition, higher proportions of woody vegetation and hedges resulted in higher colony strength at longer distances from the apiary (2.5 km). Honeybees in moderately degraded landscapes demonstrated the most consistently strong colonies throughout the study period. Efforts towards improving beekeeper livelihoods, through higher hive productivity, should target moderately degraded and heterogeneous landscapes, which provide forage from diverse land covers.
Insect pollinators provide an important ecosystem service by improving agricultural productivity. However, their populations have been declining in recent years due to excessive use of synthetic pesticides, climate and land use/land cover (LULC) changes. Climate and LULC changes have resulted in land fragmentation and consequently pollinator habitat loss. To conserve pollinators, there is a need for sustainable agricultural practices such as integrated pest and pollinator management (IPPM), which is a holistic landscape management approach that minimizes pesticides use while conserving pollinator abundance and diversity. This study aimed to use earth observation (EO) data to characterize landscape dynamics in terms of vegetation productivity to guide the implementation of IPPM interventions in an avocado production system in Murang'a (Kenya). Specifically, we utilized Sentinel-2 (S-2)-derived normalized difference vegetation index (NDVI) as a proxy for vegetation productivity to assess IPPM implementation sites. The NDVI was calculated using multi-date S-2 data acquired during the dry and wet seasons and categorized into three vegetation productivity classes - low, medium, and high - using a K-means unsupervised clustering method. We also collected socio-economic baseline data from 410 farmers with a focus on their perception to implement one of four avocado pest management and pollination options: (1) IPPM, (2) integrated pest management (IPM), (3) pollinator supplementation (P), and (4) no intervention (control). The three landscape vegetation productivity classes were then linked with the four farmer preferences with regards to the implementation options. Criteria based on the distances among the sites for implementing the different four options were set for farmer selection and the experiment was replicated three times in each vegetation productivity class (i.e. in total 12 farmers in each class). The results showed that the K-means method was successful in characterizing the landscape vegetation productivity with an overall accuracy of 86.2%. One the other hand, we successfully selected the 36 (12 in each of the 3 vegetation productivity classes) out of 410 farmers who met our distance-based criteria and participated in the implementation of one of the four technology options (i.e. IPPM, IPM, P, and control). In conclusion, NDVI proved to be a vital proxy for assessing the landscape dynamics as it provided a robust view of vegetation productivity patterns, which enabled a well-representative distribution of the four pest management and pollination options across the landscape. Overall, the study shows the utility of integrating EO and socio-economic data in selecting sites for implementing agro-technologies at a landscape scale.
One of the major sources of uncertainty in large-scale crop modeling is the lack of information capturing the spatiotemporal variability of crop sowing dates. Remote sensing can contribute to reducing such uncertainties by providing essential spatial and temporal information to crop models and improving the accuracy of yield predictions. However, little is known about the impacts of the differences in crop sowing dates estimated by using remote sensing (RS) and other established methods, the uncertainties introduced by the thresholds used in these methods, and the sensitivity of simulated crop yields to these uncertainties in crop sowing dates. In the present study, we performed a systematic sensitivity analysis using various scenarios. The LINTUL-5 crop model implemented in the SIMPLACE modeling platform was applied during the period 2001–2016 to simulate maize yields across four provinces in South Africa using previously defined scenarios of sowing dates. As expected, the selected methodology and the selected threshold considerably influenced the estimated sowing dates (up to 51 days) and resulted in differences in the long-term mean maize yield reaching up to 1.7 t ha−1 (48% of the mean yield) at the province level. Using RS-derived sowing date estimations resulted in a better representation of the yield variability in space and time since the use of RS information not only relies on precipitation but also captures the impacts of socioeconomic factors on the sowing decision, particularly for smallholder farmers. The model was not able to reproduce the observed yield anomalies in Free State (Pearson correlation coefficient: 0.16 to 0.23) and Mpumalanga (Pearson correlation coefficient: 0.11 to 0.18) in South Africa when using fixed and precipitation rule-based sowing date estimations. Further research with high-resolution climate and soil data and ground-based observations is required to better understand the sources of the uncertainties in RS information and to test whether the results presented herein can be generalized among crop models with different levels of complexity and across distinct field crops.
In Africa there is a scarcity of information on how plant species that can provide forage for honey bees vary across differentially structured landscapes, and what are the implications of such variabilities on colony integrity. This research presents new insights into the diversity and richness of pollen collected by Apis mellifera scutellata, a subspecies of the Western honey bee native to sub-Saharan Africa, at six study sites of different degradation levels within a semi-arid landscape in Kenya. Ten colonies were established at each site and land cover characteristics were extracted using novel remote sensing methods. The sites differed by the proportions of natural vegetation, cropland, grassland and hedges within each site. Bee bread was collected five times, with three colonies in each of the six sites repeatedly sampled during the period from May 2017 to November 2018. Pollen identification and protein analysis within the study sites were thereafter conducted to establish the linkage between landscape degradation levels and abundance and diversity of pollen. Out of 124 plant species identified, Terminalia spp., Cleome spp. and Acacia spp. were identified as the most abundant species. Moreover, species richness and diversity were highest in the two sites located in moderately degraded landscapes. Pollen protein content showed statistically significant differences across season rather than geographical location. This study demonstrated that landscape degradation negatively affected the diversity and richness of pollen collected by honey bees. Consequently, this helps our understanding of native honey bees’ forage resource usage and plant species preferences in landscapes with varying degrees of degradation.
We present findings from an outbreak of a heartwater-like disease in camels that killed at least 2000 adult animals in Kenya in 2016. Clinical signs included excitability, head pressing, aimless wandering, recumbency, and fast breathing followed by death after about 4 days. The observed morbidity in one herd was 40% with an average mortality of 7.5% in animals that received early antibiotic treatments. In untreated adults, the case fatality rate reached 100%. Gross pathology showed pulmonary edema, pleural exudate, hydrothorax, hydropericardium, ascites, enlarged “cooked” liver, nephrosis, and blood in the abomasum and intestine. Using established PCR-based protocols for tick-borne pathogens, a sequence close to Ehrlichia regneryi and Ehrlichia canis amplified in blood from two sick camels. We also amplified an Ehrlichia sp. sequence close to Ehrlichia ruminantium Welgevonden from a pool of Amblyomma spp. ticks collected from a sick camel and in a pool of Rhipicephalus spp. ticks from healthy camels.
Maize lethal necrosis (MLN) is a severe disease in maize that significantly reduces yields by up to 90% in maize-growing regions such as Kenya and other countries in Africa. The disease causes chlorotic mottling of leaves and severe stunting which leads to plant death. The spread of MLN in the maize-growing regions of Kenya has intensified since the first outbreak was reported in September 2011. In this study, the RapidEye (5 m) imagery was combined with field-based data of MLN severity to map three MLN severity levels in Bomet County, Kenya. Two RapidEye images were acquired during maize stem elongation and inflorescence stages, respectively, and thirty spectral indices for each RapidEye time step were computed. A two-step random forest (RF) classification algorithm was used to firstly create a maize field mask and to predict the MLN severity levels (mild, moderate, and high). The RF algorithm yielded an overall accuracy of 91.0%, representing high model performance in predicting the MLN severity levels in a complex cropping system. The normalized difference red edge index (NDRE) was highly sensitive to MLN detection and demonstrated the ability to detect MLN-caused crop stress earlier than the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI). These results confirm the potential of the RapidEye sensor and machine learning to detect crop disease infestation rates and for use in MLN monitoring in fragmented agro-ecological landscapes.
Peatlands in Indonesia are one of the primary global storages for terrestrial organic carbon. Poor land management, drainage, and recurrent fires lead to the release of huge amounts of carbon dioxide. Accurate information about the extent of the peatlands and its 3D surface topography is crucial for assessing and quantifying this globally relevant carbon store. To identify the most carbon-rich peatlands-dome-shaped ombrogenous peat-by collecting GPS-based terrain data is almost impossible, as these peatlands are often located in remote areas, frequently flooded, and usually covered by dense tropical forest vegetation. The detection by airborne LiDAR or spaceborne remote sensing in Indonesia is costly and laborious. This study investigated the potential of the ICESat-2/ATLAS LiDAR satellite data to identify and map carbon-rich peatlands. The spaceborne ICESat-2 LiDAR data were compared and correlated with highly accurate field validated digital terrain models (DTM) generated from airborne LiDAR as well as the commercial global WorldDEM DTM dataset. Compared to the airborne DTM, the ICESat-2 LiDAR data produced an R 2 of 0.89 and an RMSE of 0.83 m. For the comparison with the WorldDEM DTM, the resulting R 2 lay at 0.94 and the RMSE at 0.86 m. We model the peat dome surface from individual peat hydrological units by performing ordinary kriging on ICESat-2 DTM-footprint data. These ICESat-2 based peatland models, compared to a WorldDEM DTM and airborne DTM, produced an R 2 of 0.78, 0.84, and 0.94 in Kalimantan and an R 2 of 0.69, 0.72, and 0.85 in Sumatra. The RMSE ranged from 0.68 m to 2.68 m. These results demonstrate the potential of ICESat-2 in assessing peat surface topography. Since ICESat-2 will collect more data worldwide in the years to come, it can be used to survey and map carbon-rich tropical peatlands globally and free of charge.
Bangladesh is one of the most vulnerable countries exposed to natural disasters. This paper attempts to analyze the vulnerability of coastal areas of the Chittagong District in the southeastern coast of Bangladesh. This study explores the spatial variations of coastal vulnerability of the study area using three physical variables and four social variables with a spatial analytical method. In this regard, a Physical Vulnerability Index (PVI), and a Social Vulnerability Index (SVI) have been prepared for the research area. Finally, a Coastal Vulnerability Index (CVI) was constructed within a geographic information system combining both PVI and CVI. The vulnerability indices reveal that the rural areas are more vulnerable to disaster than the urban areas. According to the PVI, SVI, and CVI about 66%, 48%, and 43% areas are highly vulnerable respectively. Despite of data limitations in constructing the PVI and SVI, this research is the first endeavor to apply a composite CVI based on geospatial techniques in the southeastern coast of Bangladesh. The CVI results illustrate that the coasts are the most vulnerable to flooding, storm surges and cyclones. In this study, CVI is presented as a tool to identify areas of vulnerability in the zones of the coastal district Chittagong. The CVI will serve as a practical guideline for policy makers to introduce policies and plans to mitigate the natural hazard-induced disaster impacts on the coastal Bangladesh.
Information on weed occurrence within croplands is vital but is often unavailable to support weeding practices and improve cropland productivity assessments. To date, few studies have been conducted to estimate and map weed abundances within agroecological systems from spaceborne images over wide-area landscapes, particularly for the genus Striga. Therefore, this study attempts to increase the detection capacity of Striga at subpixel size using spaceborne high-resolution imagery. In this study, a two-step classification approach was used to detect Striga (Striga hermonthica) weed occurrence within croplands in Rongo, Kenya. Firstly, multidate and multiyear Sentinel-2 (S2) data (2017 to 2018) were utilized to map cropland and non-cropland areas using the random forest algorithm within the Google Earth Engine. The non-cropland class was thereafter masked out from a single date S2 image of the 13th of December 2017. The remaining cropland area was then used in a subpixel multiple endmember spectral mixture analysis (MESMA) to detect Striga occurrence and infestation using endmembers (EMs) obtained from the in-situ hyperspectral data. The gathered in-situ hyperspectral data were resampled to the spectral waveband configurations of S2 and three representative EMs were inferred, namely: (1) Striga, (2) crop and other weeds, and (3) soil. Overall classification accuracies of 88% and 78% for the pixel-based cropland mapping and subpixel Striga detection were achieved, respectively. Furthermore, an F-score (0.84) and a root mean square error (0.0075) showed that the MESMA subpixel algorithm provides plausible results for predicting the relative abundance of Striga within each S2 pixel at a landscape scale. The capability ofMESMA together with a cropland classification hierarchical approach was thus proven to be suited for Striga detection in a heterogeneous agroecological system. These results can be used to guide in the adaptation, mitigation, and remediation of already infested areas, thereby avoiding further Striga infestation of new croplands.
Wetlands are the most fragile and threatened ecosystems worldwide, and also one of the most rapidly declining. At the same time wetlands are typically biodiversity hotspots and provide a range of valuable ecosystem services, such as water supply and purification, disaster risk reduction, climate change adaptation, and carbon sequestration. Pressures on wetlands are likely to further intensify in the coming decades due to increased global demand for land and water, and due to climate change. Stakeholders at all levels of governance have to be involved to slow, stop and reverse these processes. However, the information they need on wetland extent, their ecological character, and their ecosystem services is often scattered, sparse and difficult to find and access. The freely available Sentinel satellite data of the Copernicus Programme, as well as the Landsat archive, provide a comprehensive basis to map and inventory wetland areas (extent), to derive information on the ecological status, as well as long- and short-term trends in wetland characteristics. However, making use of these Earth Observation (EO) resources for robust and standardized wetland monitoring requires expert knowledge on often complex data processing techniques, which impedes practical implementation. In this respect, the Satellite-based Wetland Observation Service (SWOS), a Horizon 2020 funded project ( has developed and made disseminated monitoring approaches based on EO data, specifically designed for less experienced satellite data users. The SWOS monitoring tools aim at assisting countries in conducting national wetland inventories for their Sustainable Development Goals (SDG) reporting and monitoring obligations, and additionally facilitates other monitoring obligations such as those required by the Ramsar Convention and supports decision-making in local conservation activities. The four main components of the SWOS approach are: map and indicator production; software development; capacity building; and initializing the GEO Wetlands Community Portal. Wetland managers and data analysists from more than fifty wetland sites and river basins across Europe, the Middle East, and Africa investigated the benefits and limitations of this EO-based wetland mapping and monitoring approach. We describe research that applies the SWOS tools to test their potential for the mapping of wetlands in a case study based in Albania, and show its effectiveness to derive metrics relevant to the monitoring of SDG indicator 6.6.1.
Due to high spatiotemporal variability of aquatic systems, relationships between microplastic sources and sinks are highly complex and transportation pathways yet to be understood. Field data acquisitions are a necessary component for monitoring of microplastic contamination but alone cannot capture such complex relationships. Remote sensing is a key technology for environmental monitoring through which extrapolation of spatially limited field data to larger areas can be obtained. In this field study we tested whether microplastic distribution follows the same transport pattern as water constituents depictable from satellite images, namely chlorophyll-a, suspended particulate matter, and colored dissolved organic matter, and discuss their applicability as proxies. As rivers are a major source for marine microplastic contamination, we sampled three example river systems: the lower courses and river mouths of the Trave and Elbe estuary in Germany and the Po delta in Italy. For a full quantitative analysis of microplastics (>300 μm), ATR- and FPA-based μFT-IR spectroscopy and NIR imaging spectroscopy were utilized. Comparing water constituents with in-situ data using regression analysis, neither a relationship for the Elbe estuary nor for the Po delta was found. Only for the Trave river, a positive relationship between microplastics and water constituents was present. Differences in hydrodynamic conditions and spatiotemporal dynamics of water constituents and microplastic emissions among the river systems are possible explanations for the contrary results. Based on our results no conclusions on other river systems and likewise different seasons can be drawn. For remote sensing algorithms of water constituents to be used as microplastic proxy an adaption for each system as well as for different seasons would thus be necessary. The lower detection limit of 300 μm for microplastics could also have influenced relationships as microplastic abundance exponentially increases with decreasing size class. Further studies with improved sampling methods are necessary to assess our proposed method.
Coral reefs in the wider Caribbean declined in hard coral cover by ~80% since the 1970s, but spatiotemporal analyses for sub-regions are lacking. Here, we explored benthic change patterns in the Mexican Caribbean reefs through meta-analysis between 1978 and 2016 including 125 coral reef sites. Findings revealed that hard coral cover decreased from ~26% in the 1970s to 16% in 2016, whereas macroalgae cover increased to ~30% in 2016. Both groups showed high spatiotemporal variability. Hard coral cover declined in total by 12% from 1978 to 2004 but increased again by 5% between 2005 and 2016 indicating some coral recovery after the 2005 mass bleaching event and hurricane impacts. In 2016, more than 80% of studied reefs were dominated by macroalgae, while only 15% were dominated by hard corals. This stands in contrast to 1978 when all reef sites surveyed were dominated by hard corals. This study is among the first within the Caribbean region that reports local recovery in coral cover in the Caribbean, while other Caribbean reefs have failed to recover. Most Mexican Caribbean coral reefs are now no longer dominated by hard corals. In order to prevent further reef degradation, viable and reliable conservation alternatives are required.
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