Cropland Vegetation Research Articles

The Driving Forces that Influence Land Use Land Cover Changes in Khwisero Sub County, Kakamega County, Kenya

Land use land cover changes have to a great extent changed the world's landscapes, rebuilding environments and what they provide to humans during the time spent supporting the rising population across the globe. The drivers of these changes vary from location to location causing varying effects that challenge the essential design and the functioning capacity of the land quality with flowing consequences to land quality. A cross sectional descriptive design together with a longitudinal design were used. A random sampling was used to obtain a sample size of 384 from a study population of 113,476. Based on agriculture, forest land, bare land and built up land categories, the study classified Landsat images through supervised classification algorithm then applied post - classification comparison change detection to measure land use land cover percentage area change over time. Primary data was collected through interviews and discussions with key informants, field observations, and questionnaires administered to individual households in Khwisero Sub County. Secondary data involved downloading Landsat images (Landsat 7, 8 and 9; 30-meter multispectral), summaries and citation of other works carried in journals articles, original documents, annual reports, development plans and internet. Quantitative data analysis involved measures of central tendency and measures of dispersion (SPSS) and analysis of variance (ANOVA). Qualitative data was analysed by organizing and grouping the arising issues into various categories relevant to the study. Land use land cover classification of the study area realized four land use land cover classes of agriculture 81.34 km2, forest 52.75 km2, built up 8.86 km2 and bare land 2.65 km2 for the study area as at 2023. Change detection noted that agricultural land use has been reducing from 2002 to 2023, built up has been increasing from 2002 to 2023, bare land increased between 2002 to 2012 but decreased between 2012 and 2023 while forests reduced between 2002 to 2012 but increased between 2012 to 2023 Accuracy assessment for the land use land cover classes for 2002 was 85.45% with a Kappa coefficient of 0.756, 2012 was 83.64% with a Kappa coefficient of 0.5454 while 2023 was 81.82% with a Kappa coefficient of 0.6034 for the land use land cover classes revealing that the classification is accurate. The study revealed that LULCCs are driven by settlement, poverty and climate change mostly thus affecting cropland vegetation and soil fertility majorly. The study concluded that LULCC drivers varied from location to location even within the sub county. The study recommends creation of awareness of understanding the various drivers of LULCCs and impact of each on land quality.

The different vegetation types responses to potential evapotranspiration and precipitation in China

Global climate change is considered one of the greatest environmental threats in the world. It is expected to significantly change the global hydrological cycle. The two main water cycle components, potential evapotranspiration (PET) and precipitation (P), are closely related to vegetation dynamics. In this study, the partial correlation analysis method was used to analyzed the relationship between Normalized Difference Vegetation Index (NDVI) and climate factors (PET and P) based on grid cells. PET was calculated by FAO-56 Penman–Monteith method. Moreover, we also investigated the NDVI and climate factors in different vegetation cover types. The results showed that grassland, forest and cropland in China were positively correlated with PET and P. The time scales of the maximum partial correlation coefficients between NDVI and PET of grassland vegetation were mostly longer than 5–6 months. These time scales were longer than the time scales related to P. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China, whereas the spatial distribution of related time scales was the opposite. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China. However, the spatial distribution of related time scales was the opposite. The correlations between NDVI and PET, P of cropland vegetation and the time scales related to PET had clear spatial heterogeneity. The time scale of the correlation between NDVI and P for cropland in the northern China was about 2 months. P had a strong influence on the growth of various types of vegetation in the study area, and grassland vegetation was affected by P over the shortest time scale. We compare and analyze the results of this study with other related studies. These results provide a reference for exploring the dynamic changes in different vegetation types and factors impacting them.

Exploring the Diverse Response of Cropland Vegetation to Climatic Factors and Irrigation across China

Owing to limited research on the interactions between cropland vegetation and climate and irrigation, this study used the normalized difference vegetation index (NDVI) as a cropland vegetation indicator to describe vegetation dynamics. Potential evapotranspiration (PET) was calculated using the Penman–Monteith equation. A partial correlation analysis and a Pearson correlation coefficient were used to determine the spatial response mechanisms of cropland vegetation to different climatic factors and irrigation in China for the period 1985–2015. The results show that different climatic factors (precipitation, PET, and water deficits) display positive correlations with cropland vegetation in China. A stronger correlation was observed between cropland vegetation and meteorological factors in northern China compared to the southern parts; the response time of NDVI values of croplands to precipitation was observed to be short-term (1 to 3 months) and long-term (3 to 6 months) in northern and southern regions, respectively. In contrast, the response time of NDVI values of croplands to PET displayed a complex spatial heterogeneity. Most of the cropland vegetation and the areas with the highest potential crop yields were located in the eastern part of China; these areas also require higher levels of irrigation, which benefits the potential crop yields. This study can provide a better understanding of the agricultural ecosystems and formulate strategies for food security.

Trend Analysis and Driving Factors of Vegetation Dynamics in Northern China from 1982 to 2015

Under the background of global warming, understanding the dynamic of vegetation plays a key role in revealing the structure and function of an ecosystem. Assessing the impact of climate change and human activities on vegetation dynamics is crucial for policy formulation and ecological protection. Based on the Global Inventory Monitoring and Modeling System (GIMMS) third generation of Normalized Difference Vegetation Index (NDVI3g), meteorological data and land cover data, this study analyzed the linear and nonlinear trends of vegetation in northern China from 1982 to 2015, and quantified the relative impact of climate change and human activities on vegetation change. The results showed that more than 53% of the vegetation had changed significantly, and 36.64% of the vegetation had a reverse trend. There were potential risks of vegetation degradation in the southwestern, northwestern and northeastern parts of the study’s area. The linear analysis method cannot disclose the reversal of the vegetation growth trend, which will underestimate or overestimate the risk of vegetation degradation or restoration. Climate change and human activities promoted 76.54% of the vegetation growth in the study area, with an average contribution rate of 51.22% and 48.78%, respectively, while the average contribution rate to the vegetation degradation area was 47.43% and 52.57%, respectively. Vegetation restoration of grassland and woodland was mainly affected by climate change, and human activities dominated their degradation, while cropland vegetation was opposite. The contribution rate of human activities to vegetation change in the southeastern and eastern parts of the study area was generally higher than that of climate change, but it was the opposite in the high altitude area, with obvious spatial heterogeneity. These results are helpful to understand the dynamic mechanism of vegetation in northern China, and provide a scientific basis for vegetation restoration and protection of regional ecosystems.

Near-real-time MODIS-derived vegetation index data products and online services for CONUS based on NASA LANCE

This paper describes a set of Near-Real-Time (NRT) Vegetation Index (VI) data products for the Conterminous United States (CONUS) based on Moderate Resolution Imaging Spectroradiometer (MODIS) data from Land, Atmosphere Near-real-time Capability for EOS (LANCE), an openly accessible NASA NRT Earth observation data repository. The data set offers a variety of commonly used VIs, including Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Mean-referenced Vegetation Condition Index (MVCI), Ratio to Median Vegetation Condition Index (RMVCI), and Ratio to previous-year Vegetation Condition Index (RVCI). LANCE enables the NRT monitoring of U.S. cropland vegetation conditions within 24 hours of observation. With more than 20 years of observations, this continuous data set enables geospatial time series analysis and change detection in many research fields such as agricultural monitoring, natural resource conservation, environmental modeling, and Earth system science. The complete set of VI data products described in the paper is openly distributed via Web Map Service (WMS) and Web Coverage Service (WCS) as well as the VegScape web application (https://nassgeodata.gmu.edu/VegScape/).

Tree and hedgerow configurations on maize farms are key drivers of granivorous passerine bird assemblage patterns

Granivorous birds are responsible for significant cereal crop losses to farmers worldwide. However, how cropland vegetation structural features contribute to granivorous birds’ presence and assemblage patterns remains poorly understood. This study explores how granivorous bird distribution in terms of abundance similarities, densities, species richness and encounter rates, respond to habitat configuration elements and cropping methods across non Bt-maize fields. Here we compare nine intercropped to seven monoculture farms within which influence of eight vegetation structural features and two general habitat attributes on granivorous bird’s assemblages were determined across three maize growing seasons. Bird abundance similarities were most significantly influenced by heights of hedgerows and trees. For assemblage composition, whereas bird species richness was unaffected by any habitat variable, encounter rates were higher on farms with higher hedges or tall trees. On the other hand, bird densities increased on farms with abundant large mature trees, large woodlots, high hedgerows or dominance of indigenous trees, but diminished with both overall tree density and young-tree abundance. These results suggest that keeping hedgerows low, minimizing woodlot sizes and maintaining young isolated trees across the farm, may significantly contribute to reducing granivorous bird aggregation on maize farms thus mitigating potential for maize grain losses.

A Drought Index: The Standardized Precipitation Evapotranspiration Irrigation Index

Drought has had an increasingly serious impact on humans with global climate change. The drought index is an important indicator used to understand and assess different types of droughts. At present, many drought indexes do not sufficiently consider human activity factors. This study presents a modified drought index and the standardized precipitation evapotranspiration irrigation index (SPEII), considering the human activity of irrigation that is based on the theory of the standardized precipitation evapotranspiration index (SPEI). This study aims to compare the modified drought index (SPEII) and ·SPEI and self-calibrating Palmer drought severity index (scPDSI) in the major crop-producing areas and use SPEII to evaluate the possible future drought characteristics based on CMIP5 Model. The Pearson correlation coefficient was used to assess the relevance between drought indexes (SPEII, SPEI, and scPDSI) and vegetation dynamics. The normalized difference vegetation index (NDVI) was used to represent the vegetation dynamics change. The results showed that SPEII had better performance than the SPEI and scPDSI in monitoring cropland vegetation drought, especially in cropland areas with high irrigation. The winter wheat growth period of the SPEII had better performance than that of summer maize in croplands with higher irrigation levels on the North China Plain (NCP) and Loess Plateau (LP). In general, future drought on the NCP and LP showed small changes compared with the base period (2001–2007). The drought intensity of the winter wheat growth period showed an increasing and steady trend in 2020–2080 under the representative concentration pathway (RCP) 4.5 scenario on the NCP and LP; additionally, the severe drought frequency in the central LP showed an increasing trend between 2020 and 2059. Therefore, the SPEII can be more suitable for analyzing and evaluating drought conditions in a large area of irrigated cropland and to assess the impacts of climate change on vegetation.

Probabilistic forecasting of remotely sensed cropland vegetation health and its relevance for food security

In a world where climate change, population growth, and global diseases threaten economic access to food, policies and contingency plans can strongly benefit from reliable forecasts of agricultural vegetation health. To inform decisions, it is also crucial to quantify the forecasting uncertainty and prove its relevance for food security. Yet, in previous studies both these aspects have been largely overlooked. This paper develops a methodology to anticipate the agricultural Vegetation Health Index (VHI) while making the underlying prediction uncertainty explicit. To achieve this aim, a probabilistic machine learning framework modelling weather and climate determinants is introduced and implemented through Quantile Random Forests. In a second step, a statistical link between VHI forecasts and monthly food price variations is established. As a pilot implementation, the framework is applied to nine countries of South-East Asia (SEA) with consideration of national monthly rice prices. Model benchmarks show satisfactory accuracy metrics, suggesting that the probabilistic VHI predictions can provide decision-makers with reliable information about future cropland health and its impact on food price variation weeks or even months ahead, albeit with increasing uncertainty as the forecasting horizon grows. These results - ultimately allowing to anticipate the impact of weather shocks on household food expenditure - contribute to advancing the multidisciplinary literature linking vegetation health, probabilistic forecasting models, and food security policy.

土地管理对植被变绿的潜在贡献——以中国东北农业区为例

植被变绿现象一般指绿度指标统计上呈现年际增加趋势。大尺度植被变绿现象及其归因与影响研究广泛开展中。其中, 量化各种驱动因素的贡献仍然十分困难, 特别是土地管理影响的研究尚显不足。选择了中国农业分布最广泛的东北农业区, 尝试从植被变绿的年际变化和季节特征对土地管理因素的潜在贡献进行了推断分析, 并选择增强植被指数EVI作为指标。在2000—2019年期间, EVI在农田和自然植被(林地和草地)的变化趋势分别为2.19×10^-3/a(P < 0.05)和1.86×10^-3/a(P < 0.05)(1.83×10^-3/a(P < 0.05)和1.94×10^-3/a(P < 0.05)), 即处于相同量级。但是, 农田EVI的增长主要集中在6—9月, 增长率为4.99×10^-3/a(P < 0.05), 而同期自然植被的增长速率仅为2.30×10^-3/a(P < 0.05)(林地和草地分别为1.79×10^-3/a(P < 0.05)和3.71×10^-3/a(P < 0.05)), 气候驱动因素很难解释这种季节性的不对称变化。土地管理选取种植类型变迁、农药与化肥施用量以及农业机械化等指标, 结果表明该地区农田的土地管理强度变化较大(数据范围2000—2018年)。其中, 小麦种植面积呈现下降(-85.61%), 同期玉米和水稻种植面积显著扩张, 增长率分别为144.64%和90.68%。化肥和农药施用量分别增加了112.61%和80.24%, 农业机械总功率与大中型拖拉机数量增长量分别为185.04%和1192.30%。这些农田土地管理活动对农作物生长起到显著促进作用, 能够较好解释农田植被在6—9月份相比自然植被具有更高的变绿速度。为了进一步量化相关机制, 模式需要优化土地管理过程, 完善其对变绿贡献的模拟。;The vegetation greening phenomenon usually refers to a statistical increase in annual vegetation greenness index. Researches on large-scale vegetation greening phenomenon, corresponding driving factors and their influence have been widely found. It is still difficult to quantify the contribution of the drivers, especially the research on the impact of land management is still insufficient. In this paper, the northeast agricultural region, which has the most extensive agricultural distribution in China, was selected to analyze the potential contribution of land management to greening through the inter-annual variation and seasonal characteristics of vegetation changes and Enhanced Vegetation Index (EVI) was applied to indicate vegetation condition. During 2000—2019, the interannual trends of EVI over croplands and natural vegetation (forest and grassland) were 2.19×10^-3/a (P < 0.05) and 1.86×10^-3/a (P < 0.05) (1.83×10^-3/a (P < 0.05) and 1.94×10^-3/a (P < 0.05), respectively, that is, at the same magnitude. However, the growth of cropland EVI was mainly concentrated in June to September, with a growth rate of 4.99×10^-3/a (P < 0.05), while the growth rate of natural vegetation was only 2.30×10^-3/a (P < 0.05) (forest and grassland were 1.79×10^-3/a (P < 0.05) and 3.71×10^-3/a (P < 0.05), respectively)at the same time. Climate driving factors are difficult to explain the seasonal asymmetric growth alone. The land management here mainly contains the change of planting type, the use of pesticides and chemical fertilizers and agricultural mechanization. Results demonstrate that the intensity of land management of croplands in this area has changed greatly during 2000—2018. The sowing area of wheat decreases (-85.61%), and that of corn and rice increased significantly by 144.64% and 90.68%, respectively. During the same period, the use of chemical fertilizer and pesticide increased by 112.61% and 80.24%, respectively. The total power of agricultural machinery and the number of large and medium-sized tractors increased by 185.04% and 1192.30% respectively. These land management activities have played a significant role in promoting the growth of crops, which can better explain the higher rate of greening of cropland vegetation compared with natural vegetation during June to September. It is suggested that to further clarify this mechanism, models need to optimize the land management process, and improve the simulation of land management contribution to greening.

2000-2019年京津冀地区植被覆盖状况变化及驱动因素

PDF HTML阅读 XML下载 导出引用 引用提醒 2000-2019年京津冀地区植被覆盖状况变化及驱动因素 DOI: 10.5846/stxb202104070900 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划项目（2016YFC0501904）；生物多样性调查评估项目（2019HJ2096001006） Change of vegetation coverage and the driving factor in the Beijing-Tianjin-Hebei region from 2000 to 2019 Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:近20年来中国和印度通过土地利用活动改变地表覆盖使植被变得更绿，京津冀地区是植被变得更绿的典型地区。收集京津冀地区2000-2019年MODIS增强型植被指数（EVI）、植被覆盖百分比数据，以及年平均温度和降水量等数据，分析该区近20年来自然植被和农田植被EVI变化过程和趋势，揭示其变化的驱动因素，结果显示2000-2019年京津冀地区自然植被和农田植被EVI显著增加，自然植被EVI增加速率是农田植被的1.8倍。99.51%的自然植被和96.95%的农田植被生长状况改善。2000-2019年京津冀地区自然植被EVI与年平均温度和年降水量相关性不显著。农田植被EVI与年平均温度显著相关，与年降水量相关性不显著。农田灌溉和年平均温度变化是农田植被EVI显著增加的主要驱动因素。近20年京津冀地区通过实施以造林为主的生态建设工程，自然植被生长覆盖状况呈现极显著变好。同时森林植被比非森林植被覆盖百分比增加趋势更明显。以造林为主的生态建设工程是京津冀地区自然植被生长覆盖状况显著变好的主要驱动因素。 Abstract:With the development of remote sensing technology, large-scale vegetation change monitoring becomes possible. It was found that global vegetation had become greener in last decades. China and India have changed the vegetation coverage through land use activities to make land greener in the past 20 years under climate change. The Beijing-Tianjin-Hebei region is a typical area where land has become greener. The remote sensing vegetation index was widely used in monitoring the coverage change of surface vegetation. In this study we collected MODIS Enhanced Vegetation Index(EVI) data, MODIS vegetation growth and coverage data, land use and land cover data of the Beijing-Tianjin-Hebei region from 2000 to 2019, as well as annual average temperature and precipitation data. Then we analyzed the annual average EVI variation process and trend of the natural vegetation and cropland vegetation of the study area in the past 20 years, and revealed the driving factor, with the expectation of providing a scientific basis for objectively evaluating the effectiveness of the Beijing-Tianjin-Hebei ecological construction and coordinated development, and building the regional ecological security. The results showed that the EVI of natural vegetation and farmland vegetation in the Beijing-Tianjin-Hebei region increased significantly from 2000 to 2019. The increase rate of natural vegetation was 6.96%/10 a, the rate of increase of farmland vegetation was 3.91%/10 a, and the rate of increase of natural vegetation EVI was 1.8 times that of farmland vegetation. The growth of 99.51% of natural vegetation and 96.95% of farmland vegetation had improved. From 2000 to 2019, the EVI of natural vegetation in the Beijing-Tianjin-Hebei region had no significant correlation with annual average temperature and annual precipitation. The EVI of farmland vegetation was significantly correlated with the annual average temperature, but not significantly related to the annual precipitation. The main reason was that the irrigation water for the farmland vegetation in the Beijing-Tianjin-Hebei region comes from groundwater and agricultural water for the South-to-North Water Diversion Project. The growth of the farmland vegetation was not correlated with the fluctuation of the annual precipitation in the area. The annual average temperature was directly related to the growth of farmland vegetation. Farmland irrigation and annual average temperature changes were the main driving factors for the significant increase in farmland vegetation EVI. In the past 20 years, the Beijing-Tianjin-Hebei region had implemented a number of ecological construction projects focusing on afforestation, and the growth and coverage of natural vegetation have shown a remarkable improvement. Statistics showed that in 2019, the total afforestation area in the Beijing-Tianjin-Hebei region was 5712.66 km2, accounting for about 2.62% of the total area of the region. The accumulated afforestation area in the Beijing-Tianjin-Hebei region from 2000 to 2019 was 84367.27 km2, and the afforestation area in the Beijing-Tianjin-Hebei region had increased year by year since 2006. At the same time, the percentage increase of forest vegetation coverage was more obvious than that of non-forest vegetation. The ecological construction project focusing on afforestation was the main driving factor for the remarkable improvement of the growth and coverage of natural vegetation in the Beijing-Tianjin-Hebei region. 参考文献 相似文献 引证文献

Topography modifies the effect of land‐use change on soil respiration: A meta‐analysis

AbstractSoil respiration is controlled by land‐use changes associated with cropland vegetation restoration, and this can vary with topography, although the magnitude of this effect and the underlying mechanisms are still unclear. We synthesized 69 recently published papers from China to explore the influence of topography‐related changes in land use on soil respiration where croplands were converted into orchards (abbreviated as C‐O), grasslands (abbreviated as C‐G), or woodlands (abbreviated as C‐W). Our results showed that not only did land‐use change have a significant influence on soil respiration, but this varied with topography. While C‐O resulted in a decrease in soil respiration by, on average, 3.1%, both the magnitude and sign of the change varied with topography, with an 8.2% increase for flat areas and a 14.7% decrease for slopes. For the C‐G conversions, soil respiration increased, on average, by 21.4%, with a 19.5% and a 24.3% increase for flat and slope topography, respectively. For C‐W, soil respiration increased, on average, by 28.0%, with only a 1.6% increase for flat areas, but a 39.9% increase for slopes. Soil temperature, soil moisture, root biomass, soil organic carbon (SOC), soil microbial biomass carbon (MBC), soil carbon‐to‐nitrogen ratio (C:N ratio), and soil density also varied with topography, with larger changes for slopes than for flat topography. Increases in soil respiration associated with different topography were closely related to changes in soil temperature, SOC, MBC, and root biomass (P &lt; 0.05), but were not correlated with changes in soil moisture, C:N ratio, or soil density (P &gt; 0.05). The contribution of different drivers to the change in soil respiration under different topographical conditions showed a trend of SOC &gt; root biomass &gt; MBC &gt; soil temperature. Based on these results, topography‐related variations in soil respiration need to be accounted for when quantifying the impact of land‐use change on C budgets.

Vegetation Changes in Response to Climatic Factors and Human Activities in Jilin Province, China, 2000–2019

Dynamic change in vegetation is an integral component of terrestrial ecosystems, which has become a significant research area in the current context of global climate warming. Jilin Province in northeast China is an ecologically fragile area, and there is an urgent need to understand its vegetation changes and responses to both climatic factors and human activities. The normalized difference vegetation index (NDVI) was used to analyze trends in vegetation growth, and indicated significant growth overall. The NDVI of different vegetation cover types is increasing, indicating that the vegetation is continuously greening, and in descending order, the growth trends were grassland (0.0035/year) &gt; permanent wetland (0.0028/year) &gt; cropland (0.0027/year) &gt; forest land (0.0022/year) &gt; barren land (−0.0001/year). Grassland and cropland vegetation types included the most severely degraded areas, with fluctuating NDVI values. Precipitation was the main positive controlling climatic factor of NDVI in the western regions of the study area, while average temperature was the main factor in the eastern regions. Precipitation was the main climatic control factor for grassland and cropland, while forest land was limited by precipitation and average temperature. Barren land and permanent wetland were slightly negatively correlated with precipitation. From 2000 to 2019, the residual values for NDVI increased from −0.0121 to 0.0116, and the impact of human activities on vegetation changed from negative to positive. By 2019, the proportion of positively affected zones was as high as 94.01%, and the negatively affected zones were mainly distributed across transitional areas of cropland and grassland, and urban and built-up land and forest land.

The Relevance of Francolin Vocalization in Local Time Indication in Mamfe Municipality, Southwest Region, Cameroon

Avian vocalizations can have intra-specific and inter-specific functions such as communication alarm, resource location, pair-bond maintenance, territory defense, and mate attraction. Majority of studies have focused on the diurnal vocalization of birds. Very little research has been done on nocturnal vocalization behavior of birds such as francolins in Cameroon. For this reason, this study was aimed at assessing the importance of francolin vocalization in local time indication to the inhabitants of Mamfe municipality. The data collection method of this study comprised of random spot sampling of the francolin number, recording vocalizations in the dawn and dusk, and visiting the francolin nesting sites. Surveys were conducted 5 days per week, from Monday to Friday for a period of one month. In the dawn, call recording started 7:00am and ended 5:00pm, while in the dusk it started 7:00 pm and ended 6:00am. This study revealed a significant association between francolin number and the day-period, χ 2 = 19.219 df=6 P=0.004. Vocalization time and frequency showed a significant link, χ 2 = 6.834 df=8 P<0.05. Similar results were obtained from the association of bird number and vocalization frequency, χ 2 = 21.294 df=4 P=0.000. The bird number was proportionate to call frequency and intensity, 5 birds and above generated a call frequency of 64.79%. The day-period and bird number associated significantly, χ 2 = 51.278 df=44 P<0.05. Additionally, cropland vegetation showed a significant correlation on bird number, r = 0.244 P<0.05. Furthermore, the bird group-size witnessed an influence on the call location, with more calls from cropland (70.42%) than the forest vegetation (29.58%). The consistent vocalization frequency during the early hours of the morning period is useful to the local inhabitants of the municipality. Almost all the local crop-farmers interviewed acknowledged that the francolin vocalization were time-specific, hence, reminding them upon the preparation of early morning farming engagements. Moreover, it is believed in most part of Cameroon, especially in remote communities that the francolin vocalizations help to wake up local farmers from sleep during the early of the day. However, the declining population of this bird species raises many questions on its conservation. Keywords : Francolin vocalization, Communication alarm, Bird number, Cropland vegetation DOI: 10.7176/CER/12-9-03 Publication date: September 30 th 2020

Improved mapping and change detection of the start of the crop growing season in the US Corn Belt from long-term AVHRR NDVI

Long-term monitoring of crop phenology is a critical process to understand changes in cropland vegetation dynamics related to climate and human management. During the last four decades, seasonal changes in vegetation have been measured by satellite instruments, the Advanced Very High Resolution Radiometers (AVHRRs). However, an acceptable detection of crop phenology has been prevented due to its coarse spatial resolution (~8 km) pixels in which croplands are frequently mixed with non-cropland. This study introduces a novel method for detecting the start of the growing season in cropland (SOSC) from the AVHRR Normalized Difference Vegetation Index (NDVI) to examine the long-term changes in SOSC at the 8-km pixel level across the core cropping regions of the Midwestern United States (the so-called ‘the Corn Belt’) for 1982–2015. The AVHRR-based SOSC was compared with the SOSC retrieved from the pixels covering mostly croplands of the 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. These two satellite-based SOSC shows reasonable correspondence in spatial patterns so that the annual values of the root mean square difference ranges from 3.6 to 7.4 days during the overlapping period of 2008–2015. Also, an acceptable correspondence was found between the interannual variations in the AVHRR SOSCs and in the state-level survey of crop stages. For sowing stages, correlation coefficients ranged from 0.43 to 0.68; for flowering stages those ranged from 0.51 to 0.83. By analyzing long-term SOSC changes with climate factors, we found a strong tendency that warmer pre-season condition is accompanied with earlier SOSC. In addition, larger pre-season precipitation tends to postpone SOSC as strongly as the pre-season temperature affects SOSC in a considerable portion of the core cropland. This study suggests that the postponing of sowing due to larger precipitation, which has rarely been highlighted in previous studies, requires more attentions in order for better understanding of vegetation dynamics in agriculture-climate studies.

A comparative assessment of pollen in modern vegetation and bat guano in Pipulbari Cave of Meghalaya, India

This study presents a palynological examination of modern bat guano to understand the quality of pollen preservation in relation to the extant vegetation in and around the vicinity of the Pipulbari Cave. Surface soil samples from the nearby forest, cropland and wetland areas were also studied for the comparison of pollen rain in relation to the current vegetation and landscape in the region. The pollen data recovered from the forest are indicative of tropical mixed deciduous forest composed of Shorea, Salmalia, Mesua, and Duabanga which exactly conforms to the extant vegetation in the region. The wetland vegetation was characterized by the consistent recovery of marshy and aquatic taxa including Cyperaceae, Polygonum, and Nymphaea in the pollen assemblage. The pollen preserved in cropland was characterized by the presence of cereal and Brassica. The pollen data from the modern bat guano samples display a relative dominance of the forest constituents as compared to wetland and cropland vegetation which reflects the preferential foraging of bats in dense forest areas. A multivariate principal component analysis (PCA) was applied to quantify data obtained from pollen frequency analyses of different areas inside Pipulbari Cave, which clearly revealed a significant group variation in vegetation type on the basis of dispersal and deposition of pollen in surface soil and bat guano samples. The pollen data from the modern bat guano samples are shown to provide a reliable basis for paleoecological reconstructions if they were to be based on the modern pollen analog method.

Identification of Natural and Anthropogenic Drivers of Vegetation Change in the Beijing-Tianjin-Hebei Megacity Region

Identifying the natural and anthropogenic mechanisms of vegetation changes is the basis for adapting to climate change and optimizing human activities. The Beijing-Tianjin-Hebei megacity region, which is characterized by significant geomorphic gradients, was chosen as the case study area. The ordinary least squares (OLS) method was used to calculate the NDVI trends and related factors from 2000 to 2015. A geographic weighted regression (GWR) model of NDVI trends was constructed using 14 elements of seven categories. Combined with the GWR calculation results, the mechanisms of the effects of explanatory variables on NDVI changes were analyzed. The findings suggest that the overall vegetation displayed an increasing trend from 2000 to 2015, with an NDVI increase of ca. 0.005/year. Additionally, the NDVI fluctuations in individual years were closely related to precipitation and temperature anomalies. The spatial pattern of the NDVI change was highly consistent with the gradients of geomorphology, climate, and human activities, which have a tendency to gradually change from northwest to southeast. The dominant climate-driven area accounted for only 5.98% of the total study area. The vegetation improvement areas were regionally concentrated and had various driving factors, and vegetation degradation exhibited strong spatial heterogeneity. The vegetation degradation was mainly caused by human activities. Natural vegetation was improved because of natural factors and reductions in human activities. Moreover, cropland vegetation as well as urban and built-up area improvements were related to increased human actions and decreased natural effects. This study can assist in ecological restoration planning and ecological engineering implementation in the study area.

Modeling transient soil moisture dichotomies in landscapes with intermixed land covers

Large-scale in situ soil moisture monitoring networks are becoming increasingly valuable research tools, but existing networks feature almost exclusive deployment of stations in grassland vegetation. These grassland soil moisture observations are unlikely to adequately represent the real soil moisture patterns in landscapes with intermixed land cover types. Here we demonstrate the severity of the problem for one particular landscape and introduce a flexible new method for solving the problem. The specific objectives of this study were (i) to compare root-zone soil moisture dynamics of two dominant vegetation types across Oklahoma, grassland (observed) and winter wheat cropland (simulated); (ii) to relate the soil moisture dynamics of grassland and cropland vegetation using an artificial neural network (ANN) as an observation operator; and (iii) to use the resulting ANN to estimate the soil moisture spatial patterns for a landscape of intermixed grassland and wheat cropland. Root-zone soil moisture was represented by plant available water (PAW) in the top 0.8 m of the soil profile. PAW under grassland was calculated from 18 years of soil moisture observations at 83 stations of the Oklahoma Mesonet, whereas PAW under winter wheat was simulated for the same 83 locations using a calibrated and validated soil water balance model. Then, we trained an ANN to reproduce the simulated PAW under winter wheat using only six inputs: day of the year, latitude and longitude, measured PAW under grassland, and percent sand and clay. The resulting ANN was used, along with grassland soil moisture observations, to estimate the detailed soil moisture pattern for a 9 × 9 km2 Soil Moisture Active Passive (SMAP) grid cell. The seasonal dynamics of root-zone PAW for grassland and winter wheat were strongly asynchronous, so grassland soil moisture observations rarely reflect cropland soil moisture conditions in the region. The simple ANN approach facilitated efficient and accurate prediction of the simulated PAW under winter wheat, RMSD = 21 mm and normalized RMSD = 0.17, using observed PAW under grassland as an input. This new method for estimating soil moisture under adjacent, contrasting land covers at a relatively low computational cost could be employed for any region and land cover pairing with training data available, and it may significantly enhance the applications of existing large-scale soil moisture monitoring networks.

Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data

Accurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too coarse to capture cropland heterogeneity. The fusion of data from different sensors can provide enhanced information and overcome many of the limitations of individual sensors. In thitables study, we estimate annual crop yields for seven important crop types across Montana in the continental USA from 2008–2015, including alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat. We used a satellite data-driven light use efficiency (LUE) model to estimate gross primary productivity (GPP) over croplands at 30-m spatial resolution and eight-day time steps using a fused NDVI dataset constructed by blending Landsat (5 or 7) and Terra MODIS reflectance data. The fused 30-m NDVI record showed good consistency with the original Landsat and MODIS data, but provides better spatiotemporal delineations of cropland vegetation growth. Crop yields were estimated at 30-m resolution as the product of estimated GPP accumulated over the growing season and a crop-specific harvest index (HIGPP). The resulting GPP estimates capture characteristic cropland productivity patterns and seasonal variations, while the estimated annual crop production results correspond favorably with reported county-level crop production data (r = 0.96, relative RMSE = 37.0%, p &lt; 0.05) from the U.S. Department of Agriculture (USDA). The performance of estimated crop yields at a finer (field) scale was generally lower, but still meaningful (r = 0.42, relative RMSE = 50.8%, p &lt; 0.05). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, suggesting the potential of using global satellite observations to improve agricultural management, policy decisions and regional/global food security.

Cropping Intensity in the Aral Sea Basin and Its Dependency from the Runoff Formation 2000–2012

This study is aimed at a better understanding of how upstream runoff formation affected the cropping intensity (CI: number of harvests) in the Aral Sea Basin (ASB) between 2000 and 2012. MODIS 250 m NDVI time series and knowledge-based pixel masking that included settlement layers and topography features enabled to map the irrigated cropland extent (iCE). Random forest models supported the classification of cropland vegetation phenology (CVP: winter/summer crops, double cropping, etc.). CI and the percentage of fallow cropland (PF) were derived from CVP. Spearman’s rho was selected for assessing the statistical relation of CI and PF to runoff formation in the Amu Darya and Syr Darya catchments per hydrological year. Validation in 12 reference sites using multi-annual Landsat-7 ETM+ images revealed an average overall accuracy of 0.85 for the iCE maps. MODIS maps overestimated that based on Landsat by an average factor of ~1.15 (MODIS iCE/Landsat iCE). Exceptional overestimations occurred in case of inaccurate settlement layers. The CVP and CI maps achieved overall accuracies of 0.91 and 0.96, respectively. The Amu Darya catchment disclosed significant positive (negative) relations between upstream runoff with CI (PF) and a high pressure on the river water resources in 2000–2012. Along the Syr Darya, reduced dependencies could be observed, which is potentially linked to the high number of water constructions in that catchment. Intensified double cropping after drought years occurred in Uzbekistan. However, a 10 km × 10 km grid of Spearman’s rho (CI and PF vs. upstream runoff) emphasized locations at different CI levels that are directly affected by runoff fluctuations in both river systems. The resulting maps may thus be supportive on the way to achieve long-term sustainability of crop production and to simultaneously protect the severely threatened environment in the ASB. The gained knowledge can be further used for investigating climatic impacts of irrigation in the region.

畑と森林の植生域におけるメタン（CH4）発生

畑と森林の植生域におけるメタン（CH4）発生