Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey

Long-term monitoring of the extent and intensity of irrigation systems is needed to track crop water consumption and to adapt land use to a changing climate. We mapped the expansion and changes in the intensity of irrigated dry season cropping in Turkey´s Southeastern Anatolia Project annually from 1990 to 2018 using Landsat time series. Irrigated dry season cropping covered 5,779 km² (± 479 km²) in 2018, which represents an increase of 617% over the study period. Dry season cropping was practiced on average every second year, but spatial variability was pronounced. Increases in dry season cropping frequency were observed on 40% of the studied croplands. The presented maps enable the identification of land use intensity hotspots at 30 m spatial resolution, and can thus aid in assessments of water consumption and environmental degradation. All maps are openly available for further use at https://doi.org/10.5281/zenodo.4287661.Peer Reviewe

Remote sensing environmental change in southern African savannahs : a case study of Namibia

Savannah biomes cover a fifth of Earth’s surface, harbour many of the world’s most iconic species and most of its livestock and rangeland, while sustaining the livelihoods of an important proportion of its human population. They provide essential ecosystem services and functions, ranging from forest, grazing and water resources, to global climate regulation and carbon sequestration. However, savannahs are highly sensitive to human activities and climate change. Across sub-Saharan Africa, climatic shifts, destructive wars and increasing anthropogenic disturbances in the form of agricultural intensification and urbanization, have resulted in widespread land degradation and loss of ecosystem services. Yet, these threatened ecosystems are some of the least studied or protected, and hence should be given high conservation priority. Importantly, the scale of land degradation has not been fully explored, thereby comprising an important knowledge gap in our understanding of ecosystem services and processes, and effectively impeding conservation and management of these biodiversity hotspots. The primary drivers of land degradation include deforestation, triggered by the increasing need for urban and arable land, and concurrently, shrub encroachment, a process in which the herbaceous layer, a defining characteristic of savannahs, is replaced with hardy shrubs. These processes have significant repercussions on ecosystem service provision, both locally and globally, although the extents, drivers and impacts of either remain poorly quantified and understood. Additionally, regional aridification anticipated under climate change, will lead to important shifts in vegetation composition, amplified warming and reduced carbon sequestration. Together with a growing human population, these processes are expected to compound the risk of land degradation, thus further impacting key ecosystem services. Namibia is undergoing significant environmental and socio-economic changes. The most pervasive change processes affecting its savannahs are deforestation, degradation and shrub encroachment. Yet, the extent and drivers of such change processes are not comprehensively quantified, nor are the implications for rural livelihoods, sustainable land management, the carbon cycle, climate and conservation fully explored. This is partly due to the complexities of mapping vegetation changes with satellite data in savannahs. They are naturally spatially and temporally variable owing to erratic rainfall, divergent plant functional type phenologies and extensive anthropogenic impacts such as fire and grazing. Accordingly, this thesis aims to (i) quantify distinct vegetation change processes across Namibia, and (ii) develop methodologies to overcome limitations inherent in savannah mapping. Multi-sensor satellite data spanning a range of spatial, temporal and spectral resolutions are integrated with field datasets to achieve these aims, which are addressed in four journal articles. Chapters 1 and 2 are introductory. Chapter 3 exploits the Landsat archive to track changes in land cover classes over five decades throughout the Namibian Kalahari woodlands. The approach addresses issues implicit in change detection of savannahs by capturing the distinct phenological phases of woody vegetation and integrating multi-sensor, multi-source data. Vegetation extent was found to have decreased due to urbanization and small-scale arable farming. An assessment of the limitations leads to Chapter 4, which elaborates on the previous chapter by quantifying aboveground biomass changes associated with deforestation and shrub encroachment. The approach centres on fusing multiple satellite datasets, each acting as a proxy for distinct vegetation properties, with calibration/validation data consisting of concurrent field and LiDAR measurements. Biomass losses predominate, demonstrating the contribution of land management to ecosystem carbon changes. To identify whether biomass is declining across the country, Chapter 5 focuses on regional, moderate spatial resolution time-series analyses. Phenological metrics extracted from MODIS data are used to model observed fractional woody vegetation cover, a proxy for biomass. Trends in modelled fractional woody cover are then evaluated in relation to the predominant land-uses and precipitation. Negative trends slightly outweighed positive trends, with decreases arising largely in protected, urban and communal areas. Since precipitation is a fundamental control on vegetation, Chapter 6 investigates its relation to NDVI, by assessing to what extent observed trends in vegetation cover are driven by rainfall. NDVI is modelled as a function of precipitation, with residuals assumed to describe the fraction of NDVI not explained by rainfall. Mean annual rainfall and rainfall amplitude show a positive trend, although extensive “greening” is unrelated to rainfall. NDVI amplitude, used as a proxy for vegetation density, indicates a widespread shift to a denser condition. In Chapter 7, trend analysis is applied to a Landsat time-series to overcome spatial and temporal limitations characteristic of the previous approaches. Results, together with those of the previous chapters, are synthesized and a synopsis of the main findings is presented. Vegetation loss is predominantly caused by demand for urban and arable land. Greening trends are attributed to shrub encroachment and to a lesser extent conservation laws, agroforestry and rangeland management, with precipitation presenting little influence. Despite prevalent greening, degradation processes associated with shrub encroachment, including soil erosion, are likely to be widespread. Deforestation occurs locally while shrub encroachment occurs regionally. This thesis successfully integrates multi-source data to map, measure and monitor distinct change processes across scales

Spectral Characteristics and Mapping of Rice Fields using Multi-Temporal Landsat and MODIS Data: A Case of District Narowal

Availability of remote sensed data provides powerful access to the spatial and temporal information of the earth surface Real-time earth observation data acquired during a cropping season can assist in assessing crop growth and development performance As remote sensed data is generally available at large scale rather than at field-plot level use of this information would help to improve crop management at broad-scale Utilizing the Landsat TM ETM ISODATA clustering algorithm and MODIS Terra the normalized difference vegetation index NDVI and enhanced vegetation index EVI datasets allowed the capturing of relevant rice cropping differences In this study we tried to analyze the MODIS Terra EVI NDVI February 2000 to February 2013 datasets for rice fractional yield estimation in Narowal Punjab province of Pakistan For large scale applications time integrated series of EVI NDVI 250-m spatial resolution offer a practical approach to measure crop production as they relate to the overall plant vigor and photosynthetic activity during the growing season The required data preparation for the integration of MODIS data into GIS is described with a focus on the projection from the MODIS Sinusoidal to the national coordinate systems However its low spatial resolution has been an impediment to researchers pursuing more accurate classification results and will support environmental planning to develop sustainable land-use practices These results have important implications for parameterization of land surface process models using biophysical variables estimated from remotely sensed data and assist for forthcoming rice fractional yield assessmen

Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data

Landscape Ecology

This book has been written to present major and efficient applications in landscape ecology, as well as to propose a solid action for this category of topics. The book aims to illustrate various treatment methods of the land-use models impact on landscape ecology creation. The book is divided into three parts: Part I: Ecological interpretation of land-use act - in this part, ecosystem and land use turn out to be a significant factor in the process of creating an ecological landscape. Part II: Landscape district in applied ecological analysis - this part attempts to illustrate the best possible model of analysis integrated with landscape in practical case studies. Part III: The anthropogenic impacts on landscape creation - this part discusses the human impact on landscape creation

Sustainable Intensification of Agriculture: Opportunities and Challenges for Food Security and Agrarian Adaptation to Environmental Change in Bangladesh

This dissertation investigates three unique aspects of sustainable agricultural intensification (SAI) in the context of Bangladeshi rice production. The first article presents a qualitative analysis of SAI and farmer surveys in the embanked polder region of coastal Bangladesh. The second article investigates the global food security and environmental impacts of already adopted High Yielding Variety (HYV) rice and double-cropped rice systems in Bangladesh using a spatial partial equilibrium trade model and a Life Cycle Assessment (LCA). The final article demonstrates a remote sensing methodology for monitoring dry season rice production at 30 m resolution in Bangladesh using a harmonic time series model, the Landsat archive, and Google Earth Engine. Major findings from this dissertation include: (1) agrarian communities in the polder region face food insecurity during the peak of monsoonal paddy rice production and could improve production by adopting HYV or second season crops, (2) agrarian communities in the polders identify water management issues as the primary agricultural concern, followed by pest infestation and soil salinity, (3) HYV rice provides enough additional production in Bangladesh to feed nearly 26 million Bangladeshis per annum and is more environmentally efficient than traditional rice in terms of global warming potential, land use, water use, and fertilizer use, and (4) the combination of a harmonic time series model, spectral indices, and rice phenology can produce relatively accurate predictions of dry season rice in Bangladesh compared to district-level reference information. Overall, the findings from this investigation of SAI support continued efforts to improve food security, increase agricultural output, and decrease environmental impacts in Bangladesh

ANALYSIS OF AGRO-ECOSYSTEMS EXPLOITING OPTICAL SATELLITE DATA TIME SERIES: THE CASE STUDY OF CAMARGUE REGION, FRANCE

The research activities presented in this manuscript were conducted in the frame of the international project SCENARICE, whose aim is to demonstrate the contribution of different technical and scientific competences, to assess current characteristics of analyzed cropping systems and to define sustainable future agricultural scenarios. Dynamic simulation crop models are used to evaluate the efficiency of current cropping systems and to predict their performances as consequence of climate change scenarios. In this context, a lack of information regarding the intra- and inter-annual variability of crop practices was highlighted for crops such as winter wheat, for the study area of Camargue. Moreover, a description of possible future cropping systems adaptation strategies was needed to formulate short term scenario farming system assessment. To perform this analysis it is fundamental to identify the different farm typologies representing the study area. Since it was required to take into account inter-annual variability of crop practices and farm diversities to build farm typologies, representative data of the study region in both time and space were needed. To address this issue, in this work long term time series of satellite data (2003-2013) were exploited with the specific aims to: (i) provide winter wheat sowing dates estimations variability on a long term period (11 years) to contribute in base line scenario definition and (ii) reconstruct farms land use changes through the analysis of time series of satellite data to provide helpful information for farm typologies definition. Two main research activities were carried out to address the defined objectives. Firstly a rule-based methodology was developed to automatically identify winter wheat cultivated areas in order to retrieve crop sowing occurrences in the satellite time series. Detection criteria were derived on the basis of agronomic expert knowledge and by interpretation of high confidence temporal signature. The distinction of winter wheat from other crops was based on the individuation of the crop heading and establishment periods and considering the length of the crop cycle. The detection of winter wheat cultivated areas showed that 56% of the target in the study area was correctly detected with low commissions (11%). Once winter wheat area was detected, additional rules were designed to identify sowing dates. The method was able to capture the seasonal variability of sowing dates with errors of \ub18 and \ub116 days in 45% and 65% of cases respectively. Extending the analysis to the 11 years period it was observed that in Camargue the most frequent sowing period was about October 31th (\ub14 days of uncertainty). The 2004 and 2006 seasons showed early sowings (late September) the 2003 and 2008 seasons were slightly delayed at the beginning of November. Sowing dates were not correlated to the seasonal rainfall events; this led us to formulate the hypothesis that sowing dates could be much more influenced by the harvest date of the preceding crop and soil moisture, which are related to rains but also to the date of last irrigations and to the wind. The second activity was related to define farm typologies. Temporal trajectories of winter and summer crops cultivated areas were estimated at farm scale level based on satellite data time series in the 2003-2013 periods. The validation demonstrated that the method was able to produce maps with high overall accuracy (OA 92%) and very low commission errors (3% for summer crops and 7% for winter crops). Omission errors were very low for summer crops (3%) and higher but within an acceptable level for winter crops (31%). Temporal trajectories of annual winter and summer crop land use at farm level were assumed as indicators of farm management (e.g. intensive monoculture farm or diversified crop producer). Trajectories were analysed through a hierarchical clustering procedure to identify farm management typologies. We were able to identify six typologies out of 140 farm samples, covering 75% of the arable land in the study area. A semantic interpretation of the farm types, allowed formulating hypothesis to describe farming systems. The size of the farms seemed to be an explanatory variable of the intensive or extensive farm management. The two main activities presented in this thesis highlighted the importance of time series spatial and temporal resolution for crop monitoring purposes. Currently, only heterogeneous remotely sensed data in terms of spatial and temporal resolutions are available for agricultural monitoring. Forthcoming sensors (i.e. ESA Sentinel-II A/B) will offer the chance to exploit coexisting high spatial and temporal resolutions for the first time. A preliminary application of an innovative methodology for the fusion of heterogeneous spatio-temporal resolution remotely sensed datasets was provided in the final section of the thesis with the aim to (i) produce high spatio-temporal resolution time series and (ii) verify the quality and the usefulness of the generated time series for monitoring the main European cultivated crops. The experiment positively demonstrated the contribution of data fusion techniques for the production of time series at high space-time resolution for crop monitoring purposes. The application of data fusion techniques in the main methodologies presented in this work appears to be beneficial. To conclude this thesis framework, satellite remotely sensed data properly analyzed has shown to be a reliable tool to study large-scale crop cultivations and to retrieve spatially and temporally distributed information of cropping systems. Remote sensing time series analyses lead to highlight patterns of intra- and inter-annual dynamics of agro-practices and were also useful to define farm typologies based on multi-temporal land use trajectories. Results contribute in enriching the studies and the characterization of the Camargue study area, in particular providing information such as sowing dates that are not available at present for the considered study area and represent a step forward in respect to the actual (static) available crop calendar informations. Moreover, the achieved results provide supplementary information layers for summarize and classify the diversity of the farm in the study area and to characterize farming systems

Mapping agricultural intensification in the Brazilian savanna: a machine learning approach using harmonized data from Landsat Sentinel-2.

Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products and artificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021-2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99%. The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring agricultural intensification, with an aim toward the sustainable development of agriculture

Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice