Uncertainties in classification system conversion and an analysis of inconsistencies in global land cover products

In this study, using the common classification systems of IGBP-17, IGBP-9, IPCC-5 and TC (vegetation, wetlands and others only), we studied spatial and areal inconsistencies in the three most recent multi-resource land cover products in a complex mountain-oasis-desert system and quantitatively discussed the uncertainties in classification system conversion. This is the first study to compare these products based on terrain and to quantitatively study the uncertainties in classification system conversion. The inconsistencies and uncertainties decreased from high to low levels of aggregation (IGBP-17 to TC) and from mountain to desert areas, indicating that the inconsistencies are not only influenced by the level of thematic detail and landscape complexity but also related to the conversion uncertainties. The overall areal inconsistency in the comparison of the FROM-GLC and GlobCover 2009 datasets is the smallest among the three pairs, but the smallest overall spatial inconsistency was observed between the FROM-GLC and MODISLC. The GlobCover 2009 had the largest conversion uncertainties due to mosaic land cover definition, with values up to 23.9%, 9.68% and 0.11% in mountainous, oasis and desert areas, respectively. The FROM-GLC had the smallest inconsistency, with values less than 4.58%, 1.89% and 1.2% in corresponding areas. Because the FROM-GLC dataset uses a hierarchical classification scheme with explicit attribution from the second level to the first, this system is suggested for producers of map land cover products in the future

Use and Improvement of Remote Sensing and Geospatial Technologies in Support of Crop Area and Yield Estimations in the West African Sahel

In arid and semi-arid West Africa, agricultural production and regional food security depend largely on small-scale subsistence farming and rainfed crops, both of which are vulnerable to climate variability and drought. Efforts made to improve crop monitoring and our ability to estimate crop production (areas planted and yield estimations by crop type) in the major agricultural zones of the region are critical paths for minimizing climate risks and to support food security planning. The main objective of this dissertation research was to contribute to these efforts using remote sensing technologies. In this regard, the first analysis documented the low reliability of existing land cover products for cropland area estimation (Chapter 2). Then two satellite remote sensing-based datasets were developed that 1) accurately map cropland areas in the five countries of Sahelian West Africa (Senegal, Mauritania, Mali, Burkina Faso and Niger; Chapter 3), and 2) focus on the country of Mali to identify the location and prevalence of the major subsistence crops (millet, sorghum, maize and non-irrigated rice; Chapter 4). The regional cropland area product is distributed as the West African Sahel Cropland area at 30 m (WASC30). The development of the new dataset involved high density training data (380,000 samples) developed by USGS in collaboration with CILSS for training about 200 locally optimized random forest (RF) classifiers using Landsat 8 surface reflectances and vegetation indices and the Google Earth Engine platform. WASC30 greatly improves earlier estimates through inclusion of cropland information for both rainfed and irrigated areas mapped with a class-specific accuracy of 79% across the West Africa Sahel. Used as a mask in crop monitoring systems, the new cropland area data could bring critical insights by reducing uncertainties in xv identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security. Keywords: Agricultural identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security

Assessing inconsistency in global land cover products and a synthesis of studies on land use and land cover dynamics during 2001-2017 in the southeastern region of Bangladesh

The high-quality Land Use and Land Cover data is important for monitoring and analyzing environmental changes in the background of global warming. This study accessed the spatial and areal inconsistencies in the four most recent multi-resources land cover products in a complex manner using the common classification systems of IGBP-17, IGBP-9, IPCC-5 and TC (vegetation, wetlands and others only). Based on inconsistencies and multi temporal land cover datasets, a synthesis of study was triggered out on land use and land cover dynamics during 2001-2017 in the southeastern region of Bangladesh. The overall areal and spatial inconsistencies decreased from high to low levels of aggregation (IGBP-17 to TC), indicating that the inconsistencies are not only influenced by the level of thematic detail and landscape complexity but also related to the conversion uncertainties. Overall areal inconsistency in the comparison of the FROM-GLC and GlobeLand30 datasets was the smallest among the six pairs, while, the pair of MODISLC and LULC was observed the highest inconsistencies. Based on overall lower inconsistencies classification system (IGBP-9), the synthetic land use cover changes at the study area were assessed. During the period of study, the areal distribution of forest cover, built-up areas and water were found increased in annually by 0.4%, 1.32%, and 0.3% respectively, while the croplands and wetlands were respectively decreased by 0.5% and 0.3%. The dynamic changes of croplands, forest, and artificial surface were identified the prime cyclic land cover change. This research is helpful in providing training areas for the producer of land cover products

Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014

A new global information baseline describing the spatial evolution of the human settlements in the past 40 years is presented. It is the most spatially global detailed data available today dedicated to human settlements, and it shows the greatest temporal depth. The core processing methodology relies on a new supervised classification paradigm based on symbolic machine learning. The information is extracted from Landsat image records organized in four collections corresponding to the epochs 1975, 1990, 2000, and 2014. The experiment reported here is the first known attempt to exploit global Multispectral Scanner data for historical land cover assessment. As primary goal, the Landsat-made Global Human Settlement Layer (GHSL) reports about the presence of built-up areas in the different epochs at the spatial resolution allowed by the Landsat sensor. Preliminary tests confirm that the quality of the information on built-up areas delivered by GHSL is better than other available global information layers extracted by automatic processing from Earth Observation data. An experimental multiple-class land-cover product is also produced from the epoch 2014 collection using low-resolution space-derived products as training set. The classification schema of the settlement distinguishes built-up areas based on vegetation contents and volume of buildings, the latter estimated from integration of SRTM and ASTER-GDEM data. On the overall, the experiment demonstrated a step forward in production of land cover information from global fine-scale satellite data using automatic and reproducible methodology.JRC.G.2-Global security and crisis managemen

Multi-layer Land Cover Data for Remote-Sensing based Vegetation Modelling for South Korea

Land cover data is essential input for vegetation productivity models that are often driven by coarse resolution data. In this study, we analyze how well 1 km land cover data represent land cover at 30 m for South Korea. We derive multi-layer 1 km land cover classes and coverages and analyze how much of land cover heterogeneity is represented by the successive layers. Comparison to global land cover data shows varying agreement. The multilayer land cover data can be used for example for net primary productivity modelling. Especially, for models that can include more than one vegetation type per pixel, multi-layer land cover data and their corresponding coverages are a major asset

Potential of using remote sensing techniques for global assessment of water footprint of crops

Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. The WF of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. In this paper evapotranspiration, precipitation, water storage, runoff and land use are identified as key variables to potentially be estimated by remote sensing and used for WF assessment. A mass water balance is proposed to calculate the volume of irrigation applied, and green and blue WF are obtained from the green and blue evapotranspiration components. The source of remote sensing data is described and a simplified example is included, which uses evapotranspiration estimates from the geostationary satellite Meteosat 9 and precipitation estimates obtained with the Climatic Prediction Center Morphing Technique (CMORPH). The combination of data in this approach brings several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which are discussed in detail. This work provides new tools for global WF assessment and represents an innovative approach to global irrigation mapping, enabling the estimation of green and blue water use

Mapping Congo Basin vegetation types from 300 m and 1 km multi-sensor time series for carbon stocks and forest areas estimation

This study aims to contribute to the understanding of the Congo Basin forests by delivering a detailed map of vegetation types with an improved spatial discrimination and coherence for the whole Congo Basin region. A total of 20 land cover classes were described with the standardized Land Cover Classification System (LCCS) developed by the FAO. Based on a semi-automatic processing chain, the Congo Basin vegetation types map was produced by combining 19 months of observations from the Envisat MERIS full resolution products (300 m) and 8 yr of daily SPOT VEGETATION (VGT) reflectances (1 km). Four zones (north, south and two central) were delineated and processed separately according to their seasonal and cloud cover specificities. The discrimination between different vegetation types (e.g. forest and savannas) was significantly improved thanks to the MERIS sharp spatial resolution. A better discrimination was achieved in cloudy areas by taking advantage of the temporal consistency of the SPOT VGT observations. This resulted in a precise delineation of the spatial extent of the rural complex in the countries situated along the Atlantic coast. Based on this new map, more accurate estimates of the surface areas of forest types were produced for each country of the Congo Basin. Carbon stocks of the Basin were evaluated to a total of 49 360 million metric tons. The regional scale of the map was an opportunity to investigate what could be an appropriate tree cover threshold for a forest class definition in the Congo Basin countries. A 30% tree cover threshold was suggested. Furthermore, the phenology of the different vegetation types was illustrated systematically with EVI temporal profiles. This Congo Basin forest types map reached a satisfactory overall accuracy of 71.5% and even 78.9% when some classes are aggregated. The values of the Cohen's kappa coefficient, respectively 0.64 and 0.76 indicates a result significantly better than random

접근불가지역인 북한의 시계열 토지피복도 매핑 및 산림 변화 동향 분석

학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 생태조경·지역시스템공학부(생태조경학), 2021.8. 이동근.North Korea, as an inaccessible area, has little research on land cover change, but it is very important to understand the changing trend of LULCC and provide information previously unknown to North Korea. This study therefore aimed to construct and analyze a 30-m resolution modern time-series land use land cover (LULC) map to identify the LULCCs over long time periods across North Korea and understand the forest change trends. A land use and land cover (LULC) map of North Korea from 2001 to 2018 was constructed herein using semi-permanent point classification and machine learning techniques on satellite image time-series data. The resultant relationship between cropland and forest cover, and the LULC changes were examined. The classification results show the effectiveness of the methods used in classifying the time series of Landsat images for LULC, wherein the overall accuracy of the LULC classification results was 97.5% ± 0.9%, and the Kappa coefficient was 0.94 ± 0.02. Using LULC change detection, our research effectively explains the change trajectory of North Korea’s current LULC, providing new insights into the change characteristics of North Korea’s croplands and forests. Further, our results show that North Korea’s urban area has increased significantly, its forest cover has increased slightly, and its cropland cover has decreased. We determined that North Korea’s Forest protection policies have led to the forest restoration. Thus, as agriculture is one of North Korea’s main economic contributors, croplands have been forced to relocate, expanding to other regions to compensate for the land loss caused by forest restoration.북한은 세계에서 가장 심각하게 황폐화된 산림 중 하나를 포함하고 있지만 최근에는 산림 복원을 강조하고 있다. 산림 복원이 일어나는 정도를 이해하기 위해서는 토지 이용과 토지 피복 변화 경향 (LULCC)을 이해해야 한다. 따라서 본 연구는 30m 해상도의 현대 시계열 토지 이용 토지 피복 (LULC)지도를 구성 및 분석하여 북한 전역의 장기 LULCC를 식별하고 산림 변화 추세를 이해하는 것을 목표로 한다. 2001 - 2018 년 기간 동안 국가의 LULC지도는 30m 해상도 위성 이미지 시계열 데이터를 기반으로 반영구적 포인트 분류 및 기계 학습을 사용하여 구성되었으며, 이는 GEE (Google Earth Engine)에서 수집 한 현상 학적 정보와 함께 사용되고 있다. 또한 LULCC 탐지기 법과 경작지 변화와 고도의 관계를 고려하여 2001 - 2018 년 북한의 산림 변화를 평가하였다. LULC 맵 결과의 전체 분류 정확도는 97.5 % ± 0.9 %이고, Kappa 계수는 0.94 ± 0.02 이다. LULCC 탐지는 또한 2001 - 2018 년에 북한의 산림 면적이 약간 증가한 것으로 나타났다. 일반적으로 산림 피복 면적은 크게 변하지 않았으나 남부와 중부 지역의 산림 복원과 북부와 서부의 경작지 상대적 증가 측면에서 뚜렷한 공간적 변화가 관찰되었다. 북한의 특성과 산림 정책 문서를 검토 한 결과 북한 근대 산림의 일부 지역이 복원되고 있음을 확인하였다.Chapter 1. Introduction 1 Chapter 2. Study Area 7 Chapter 3. Materials and Methods 8 3.1. Study overview 8 3.2. Data Collection 9 3.3. Data Processing 11 3.4. Classification Process 12 3.5. LULCC Analysis 14 3.6. Reference Data Collection and Classification Accuracy Validation 15 Chapter 4. Results 17 4.1. LULC Classification Accuracy Assessment 17 4.2. LULC Classification Results 20 4.3. LULC Change Detection 22 4.4. Relation with mountainous cropland and elevation 26 Chapter 5. Discussion 28 5.1. Interpretation and explanation of the forest change in North Korea 28 5.2. Importance of spatial analysis and future research directions 30 5.3. Limits and Advantages 32 Chapter 6. Conclusion 34 Bibliography 36 Appendix 44 Abstract in Korean 51석