Integrating spatial continuous wavelet transform and normalized difference vegetation index to map the agro-pastoral transitional zone in Northern China

The agro-pastoral transitional zone (APTZ) in Northern China is one of the most important ecological barriers of the world. The commonly-used method to identify the spatial distribution of ATPZ is to apply a threshold rule on climatic or land use indicators. This approach is highly subjective, and the quantity standards vary among the studies. In this study, we adopted the spatial continuous wavelet transform (SCWT) technique to detect the spatial fluctuation in normalized difference vegetation index (NDVI) sequences, and as such identify the APTZ. To carry out this analysis, the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI 1-month data (MODND1M) covering the period 2006–2015 were used. Based on the spatial variation in NDVI, we identified two sub-regions within the APTZ. The temporal change of APTZ showed that although vegetation spatial pattern changed annually, certain areas appeared to be stable, while others showed higher sensitivity to environmental variance. Through correlation analysis between the dynamics of APTZ and precipitation, we found that the mean center of the APTZ moved toward the southeast during dry years and toward the northwest during humid years. By comparing the APTZ spatial pattern obtained in the present study with the outcome following the traditional approach based on mean annual precipitation data, it can be concluded that our study provides a reliable basis to advance the methodological framework to identify accurately transitional zones. The identification framework is of high importance to support decision-making in land use management in Northern China as well as other similar regions around the world

Impacts of Anthropogenic Activities on Watersheds in a Changing Climate

The immediate goal of this Special Issue was the characterization of land uses and occupations (LULC) in watersheds and the assessment of impacts caused by anthropogenic activities. The goal was immediate because the ultimate purpose was to help bring disturbed watersheds to a better condition or a utopian sustainable status. The steps followed to attain this objective included publishing studies on the understanding of factors and variables that control hydrology and water quality changes in response to human activities. Following this first step, the Special Issue selected work that described adaption measures capable of improving the watershed condition (water availability and quality), namely LULC conversions (e.g., monocultures into agro-forestry systems). Concerning the LULC measures, however, efficacy was questioned unless supported by public programs that force consumers to participate in concomitant costs, because conversions may be viewed as an environmental service

Copula-based abrupt variations detection in the relationship of seasonal vegetation-climate in the Jing River Basin, China

Understanding the changing relationships between vegetation coverage and precipitation/temperature (P/T) and then exploring their potential drivers are highly necessary for ecosystem management under the backdrop of a changing environment. The Jing River Basin (JRB), a typical eco-environmentally vulnerable region of the Loess Plateau, was chosen to identify abrupt variations of the relationships between seasonal Normalized Difference Vegetation Index (NDVI) and P/T through a copula-based method. By considering the climatic/large-scale atmospheric circulation patterns and human activities, the potential causes of the non-stationarity of the relationship between NDVI and P/T were revealed. Results indicated that (1) the copula-based framework introduced in this study is more reasonable and reliable than the traditional double-mass curves method in detecting change points of vegetation and climate relationships; (2) generally, no significant change points were identified during 1982-2010 at the 95% confidence level, implying the overall stationary relationship still exists, while the relationships between spring NDVI and P/T, autumn NDVI and P have slightly changed; (3) teleconnection factors (including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Nino 3.4, and sunspots) have a more significant influence on the relationship between seasonal NDVI and P/T than local climatic factors (including potential evapotranspiration and soil moisture); (4) negative human activities (expansion of farmland and urban areas) and positive human activities (Grain For Green program) were also potential factors affecting the relationship between NDVI and P/T. This study provides a new and reliable insight into detecting the non-stationarity of the relationship between NDVI and P/T, which will be beneficial for further revealing the connection between the atmosphere and ecosystems

Probabilistic forecasting of dry spells in Kenya and Australia

Kenya and the Murray Darling Basin (MDB) of Australia are largely arid or semi-arid and are important agricultural areas. However, persistent dry periods and the timing of dry spells directly impact on availability of soil moisture and hence crop production in these regions. Research in these regions has not yielded desirable impacts in addressing this problem. This study aimed at examining the characteristics of dry spells and development of monthly dry spell forecasts in these regions. Daily rainfall datasets from 30 locations in Kenya and 47 locations in the MDB were used in the analysis of monthly dry spells. The length of both monthly dry spells and dry spells going across months were separately calculated and compared. The best parametric distribution functions (pdfs) describing the empirical dry spell distribution were examined. A generalized linear model (GLM) and a generalized additive model (GAM) were used to determine the temporal and spatial trends in dry spell length and in forecasting of dry spells at 1-, 3-, and 6-month lead times. Overall, the monthly dry spell lengths mostly followed a lognormal distribution. The mean monthly dry spell length underestimated the observed dry spell length in these regions while the monthly dry spell parameters were negatively correlated with the mean annual and monthly rainfall in Kenya and in the MDB. Increasing dry spell trends occurred in most months and in some locations and the probability of drought risk in the cropping season reach up to 50% in Kenya and 77% in the MDB. The greatest increases were in June-September in Kenya and in autumn season in the MDB. Increasing rates in observed trends in both regions were ≥ 0.026 days/year or 1 day to 37 days increase over the entire period. The performance of binary and continuous forecasts at 1-, 3-, and 6-month lagged SOI phases and SSTs showed modest skill (R2) ranging from < 20% – 72% in Kenya and MDB for the total number of dry days and the maximum dry spell length in a month but better skill was indicated in Kenya than in the MDB. The challenge still remaining is to find a way to capture all the inter-intra annual variability in the dry spell series at the monthly and seasonal time frames. The current skill may be improved by including other predictors in the model such as NINO4, Pacific Ocean thermocline and tropospheric wind anomalies. The current findings can have implications for agriculture in these regions

Probabilistic forecasting of dry spells in Kenya and Australia

Kenya and the Murray Darling Basin (MDB) of Australia are largely arid or semi-arid and are important agricultural areas. However, persistent dry periods and the timing of dry spells directly impact on availability of soil moisture and hence crop production in these regions. Research in these regions has not yielded desirable impacts in addressing this problem. This study aimed at examining the characteristics of dry spells and development of monthly dry spell forecasts in these regions. Daily rainfall datasets from 30 locations in Kenya and 47 locations in the MDB were used in the analysis of monthly dry spells. The length of both monthly dry spells and dry spells going across months were separately calculated and compared. The best parametric distribution functions (pdfs) describing the empirical dry spell distribution were examined. A generalized linear model (GLM) and a generalized additive model (GAM) were used to determine the temporal and spatial trends in dry spell length and in forecasting of dry spells at 1-, 3-, and 6-month lead times. Overall, the monthly dry spell lengths mostly followed a lognormal distribution. The mean monthly dry spell length underestimated the observed dry spell length in these regions while the monthly dry spell parameters were negatively correlated with the mean annual and monthly rainfall in Kenya and in the MDB. Increasing dry spell trends occurred in most months and in some locations and the probability of drought risk in the cropping season reach up to 50% in Kenya and 77% in the MDB. The greatest increases were in June-September in Kenya and in autumn season in the MDB. Increasing rates in observed trends in both regions were ≥ 0.026 days/year or 1 day to 37 days increase over the entire period. The performance of binary and continuous forecasts at 1-, 3-, and 6-month lagged SOI phases and SSTs showed modest skill (R2) ranging from < 20% – 72% in Kenya and MDB for the total number of dry days and the maximum dry spell length in a month but better skill was indicated in Kenya than in the MDB. The challenge still remaining is to find a way to capture all the inter-intra annual variability in the dry spell series at the monthly and seasonal time frames. The current skill may be improved by including other predictors in the model such as NINO4, Pacific Ocean thermocline and tropospheric wind anomalies. The current findings can have implications for agriculture in these regions

Climate Change and Environmental Sustainability-Volume 4

Anthropogenic activities are significant drivers of climate change and environmental degradation. Such activities are particularly influential in the context of the land system that is an important medium connecting earth surface, atmospheric dynamics, ecological systems, and human activities. Assessment of land use land cover changes and associated environmental, economic, and social consequences is essential to provide references for enhancing climate resilience and improving environmental sustainability. On the one hand, this book touches on various environmental topics, including soil erosion, crop yield, bioclimatic variation, carbon emission, natural vegetation dynamics, ecosystem and biodiversity degradation, and habitat quality caused by both climate change and earth surface modifications. On the other hand, it explores a series of socioeconomic facts, such as education equity, population migration, economic growth, sustainable development, and urban structure transformation, along with urbanization. The results of this book are of significance in terms of revealing the impact of land use land cover changes and generating policy recommendations for land management. More broadly, this book is important for understanding the interrelationships among life on land, good health and wellbeing, quality education, climate actions, economic growth, sustainable cities and communities, and responsible consumption and production according to the United Nations Sustainable Development Goals. We expect the book to benefit decision makers, practitioners, and researchers in different fields, such as climate governance, crop science and agricultural engineering, forest ecosystem, land management, urban planning and design, urban governance, and institutional operation.Prof. Bao-Jie He acknowledges the Project NO. 2021CDJQY-004 supported by the Fundamental Research Funds for the Central Universities and the Project NO. 2022ZA01 supported by the State Key Laboratory of Subtropical Building Science, South China University of Technology, China. We appreciate the assistance of Mr. Lifeng Xiong, Mr. Wei Wang, Ms. Xueke Chen, and Ms. Anxian Chen at School of Architecture and Urban Planning, Chongqing University, China

Historical Land use/Land cover classification and its change detection mapping using Different Remotely Sensed Data from LANDSAT (MSS, TM and ETM+) and Terra (ASTER) sensors: a case study of the Euphrates River Basin in Syria with focus on agricultural irrigation projects

This thesis deals spatially and regionally with the natural boundaries of the Euphrates River Basin (ERB) in Syria. Scientifically, the research covers the application of remote sensing science (optical remote sensing: LANDSAT-MSS, TM, and ETM+; and TERRA: ASTER); and methodologically, in Land Use/Land Cover (LULC) classification and mapping, automatically and/or semi-automatically; in LULC-change detection; and finally in the mapping of historical irrigation and agricultural projects for the extraction of differing crop types and the estimation of their areas. With regard to time, the work is based on the years 1975, 1987, 2005 and 2007. Initially, preprocessing of the satellite data (geometric- and radiometric- processing, image enhancement, best bands composite selection, transformation, mosaicing and finally subsetting) was carried out. Then, the Land Use/Land Cover Classification System (LCCS) of the Food and Agriculture Organization (FAO) was chosen. The following steps were followed in LULC- classification and change detection mapping: visual interpretation in addition to digital image processing techniques; pixel-based classification methods; unsupervised classification: ISODATA-method; and supervised classification and multistage supervised approaches using the algorithms: Maximum Likelihood Classifier (MLC), Neural Network classifier (NN) and Support Vector Machines (SVM). These were trialed on a test area to determine the optimized classification approach/algorithm for application on the whole study area (ERB) based on the available imagery. Pre- and post- classification change detection methods (comparison approaches) were used to detect changes in land use/land cover-classes (for the years 1975, 1987 and 2007) in the study area. The remote sensing methods show a high potential in mapping historical and present land use/land cover classes and its changes over time. Significant results are also possible for agricultural crop classification in relatively large regional areas (the ERB in Syria is almost 50,335 km²). Change trends in the study area and period was characterized by land-intensive agricultural expansion. The rapid, more labor- and capital- intensive growth in the agricultural sector was enabled by the introduction of fertilizer, improved access to rural roads and markets, and the expansion of the government irrigation projects. Irrigated areas increased 148 % in the past 32 years from 249,681 ha in 1975 to 596,612 ha in 2007