An assessment of land degradation in the Save catchment of Zimbabwe

The lack of reliable baseline information on land degradation is a hindrance towards its monitoring and mitigation. Of particular interest is the identification of areas susceptible to degradation. In this study, remote sensing and GIS technologies were applied to detect and map susceptibility to land degradation in Buhera district, in Save catchment, Zimbabwe. Data used included Landsat TM and ETM imagery for 1992 and 2002, agro-ecological zones, vegetation cover and population density. The study identified five preliminary categories of degradation susceptibility ranging from very high to low.     

利用NOAA NDVI数据集监测冬小麦生育期的研究

探索了利用NDVI研究作物生育期的方法，对黄淮海冬麦区的返青期、抽穗期、成熟期进行了估测，并利用地面实际观测资料进行了验证。结果表明，NDVI数据对大范围农作物生育期监测是可行的。冬小麦遥感反青期由南到北依次推迟，符合春季绿波由南到北推移规律。对冬小麦遥感生育期年际变化分析表明，黄淮海平原返青期变化相对较大，而抽穗期和成熟期变化较小。根据历年月平均温度与返青期分析，冬小麦返青日期与2月份平均温度密切相关。对于局部地区，利用5d合成1km分辨率数据，且按农业生态分区分别制定生育期判别标准，估测效果将更好。     

Spatiotemporal characteristics of white mold and impacts on yield in soybean fields in South Dakota

ABSTRACT

White mold of soybeans is one of the most important fungal diseases that affect soybean production in South Dakota. However, there is a lack of information on the spatial characteristics of the disease and relationship with soybean yield. This relationship can be explored with the Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 and a fusion of Landsat 8 and the Moderate Resolution Imaging Spectroradiometer (MODIS) images. This study investigated the patterns of yield in two soybean fields infected with white mold between 2016 and 2017, and estimated yield loss caused by white mold. Results show evidence of clustering in the spatial distribution of yield (Moran’s I = 0.38; p < 0.05 in 2016 and Moran’s I = 0.45; p < 0.05 in 2017) that can be explained by the spatial distribution of white mold in the observed fields. Yield loss caused by white mold was estimated at 36% in 2016 and 56% in 2017 for the worse disease pixels, with the most accurate period for estimating this loss on 21 August and 8 September for 2016 field and 2017 field, respectively. This study shows the potential of free remotely sensed satellite data in estimating yield loss caused by white mold.     

Multi-scale remote sensing to support insurance policies in agriculture: from mid-term to instantaneous deductions

ABSTRACT

Climate change is today one of the biggest issues for farmers. The increasing number of natural disasters and change of seasonal trends is making insurance companies more interested in new technologies that can somehow support them in quantifying and mapping risks. Remotely sensed data, with special focus on free ones, can certainly provide the most of information they need, making possible to better calibrate insurance fees in space and time. In this work, a prototype of service based on free remotely sensed data is proposed with the aim of supporting insurance companies’ strategies. The service is thought to calibrate annual insurance rates, longing for their reduction at such level that new customers could be attracted. The study moves from the entire Piemonte region (NW Italy), to specifically focus onto the Cuneo province (Southern Piemonte), which is mainly devoted to agriculture. MODIS MOD13Q1-v6 and Sentinel-2 L2A image time series were jointly used. NDVI maps from MODIS data were useful to describe the midterm phenological trends of main crops at regional level in the period 2000–2018; differently, Sentinel-2 data permitted to map local crop differences at field level in 2016 and 2017 years. With reference to MODIS data, the average phenological behavior of main crop classes in the area, obtained from the CORINE Land Cover map Level 3, was considered using a time series decomposition approach. Trend analyses showed that the most of the crop classes alternated three phases (about 7 years) suggesting that, presently, this is probably the time horizon to be considered to tune mid-term algorithms for risk estimates in the agricultural context. Crop classes trends were consequently split into three phases and each of them modeled by a first-order polynomial function used to update correspondent insurance risk rate. Sentinel-2 data were used to map phenological anomalies at field level for the 2016 and 2017 growing seasons; shifts from class average behavior were considered to locally and temporarily tune insurance premium around its average trend as described at the previous step. Synthesizing, one can say that this approach, integrating MODIS and Sentnel-2 data, makes possible to locally and temporarily calibrate premiums of indexed insurance policies by describing the average trends of crop performance (NDVI) at regional level by MODIS data and refining it at field and specific crop level by Sentinel-2 data.     

Study on NDVI- T s space by combining LAI and evapotranspiration

This paper focuses on interpreting the different spatial relationships between NDVI and T _s, a triangular or a trapezoid, and on analyzing transformation conditions, the physical and ecological meanings of the vegetation index-surface temperature space as well. Further, we use the Temperature-Vegetation Dryness Index (TVDI) to explain the existent meaning of a triangular space after NDVI reaches its saturated state by employing the relationships between NDVI, LAI and evapotranspiration. The specific relations between NDVI and T _s are useful for describing, validating and updating land surface models.     

额济纳荒漠绿洲植被生态需水量研究

荒漠绿洲的生态需水量主要指维持荒漠绿洲植被正常生长所需要消耗的水量.采用3S技术与野外生产力测定相结合的方法, 通过建立植被归一化指数(NDVI)、生产力、蒸腾系数之间的关系方程, 计算了额济纳荒漠绿洲的植被生态需水量.结果表明, 维持额济纳绿洲现状的需水量为1.53×10⁸ m³, 若使现有的植被恢复到目前最高生产力水平的生态需水量为3.49×10⁸ m³. 考虑到城镇居民生活用水、河道输水损失、绿洲植被耗水、绿洲内农田用水和降水补充等, 额济纳旗绿洲维持现状需要黑河下泄水量(狼心山)为1.93~2.23×10⁸ m³之间, 若使现有的植被恢复到目前最高生产力水平, 需要黑河下泄水量(狼心山)为4.28~5.17×10⁸ m³之间.     

基于NDVI背景场的雪盖制图算法探索

NDSI算法提取MSS雪盖面积时,受到MSS影像缺少短波红外波段的局限。为充分精确提取MSS影像的雪盖面积,本文探索一种以NDVI为背景场的雪盖制图新思路。该方法首先在辐射校正时利用6S模型反演地表反射率,然后根据各地物的光谱特性差异和NDVI特性差异,在ENVI软件SPECTRAL模块中创建冰雪光谱阈值查找表。通过ETM＋和TM影像的三个例证,详细阐明该算法流程以及查找表的创建,并以NDSI对其雪盖制图进行精度验证。结果一致表明,与常规的分类方法（最大似然法）相比较,本文探索的NDVI背景场算法有更高的总体精度和Kappa系数。     

利用MERIS数据植被指数分析福建省植被长势季节变化

监测植被长势动态变化可以提供生态系统状况有价值的信息,可以检测到人类或气候作用引起的变化。本研究利用2004—2005年间10期MERIS影像数据,以福建省为例,探讨MERIS数据在区域植被长势季节变化监测中的应用效果;分析了MERIS数据用于区域植被季节变化监测时的数据处理方法;比较了MERIS数据几种植被指数,提出了利用10和8波段组合改进MERISNDVI的建议;利用多时相合成的NDVI简单分析了2004年夏季—2005年夏季三个季节的植被长势状况。结果表明,MERIS植被指数的时空变化有效反映了气候变化对植被长势的影响。     

Estimation of potential evapotranspiration over the Yellow River basin: reference crop evaporation or Shuttleworth–Wallace?

Potential evapotranspiration (PET) is a key input to hydrological models. Its estimation has often been via the Penman–Monteith (P–M) equation, most recently in the form of an estimate of reference evapotranspiration (RET) as recommended by FAO‐56. In this paper the Shuttleworth–Wallace (S–W) model is implemented to estimate PET directly in a form that recognizes vegetation diversity and temporal change without reference to experimental measurements and without calibration. The threshold values of vegetation parameters are drawn from the literature based on the International Geosphere–Biosphere Programme land cover classification. The spatial and temporal variation of the LAI of vegetation is derived from the composite NOAA‐AVHRR normalized difference vegetation index (NDVI) using a method based on the SiB2 model, and the Climate Research Unit database is used to provide the required meteorological data. All these data inputs are publicly and globally available. Consequently, the implementation of the S–W model developed in this study is applicable at the global scale, an essential requirement if it is to be applied in data‐poor or ungauged large basins. A comparison is made between the FAO‐56 method and the S–W model when applied to the Yellow River basin for the whole of the last century. The resulting estimates of RET and PET and their association with vegetation types and leaf area index (LAI) are examined over the whole basin both annual and monthly and at six specific points. The effect of NDVI on the PET estimate is further evaluated by replacing the monthly NDVI product with the 10‐day product. Multiple regression relationships between monthly PET, RET, LAI, and climatic variables are explored for categories of vegetation types. The estimated RET is a good climatic index that adequately reflects the temporal change and spatial distribution of climate over the basin, but the PET estimated using the S–W model not only reflects the changes in climate, but also the vegetation distribution and the development of vegetation in response to climate. Although good statistical relationships can be established between PET, RET and/or climatic variables, applying these relationships likely will result in large errors because of the strong non‐linearity and scatter between the PET and the LAI of vegetation. It is concluded that use of the implementation of the S–W model described in this study results in a physically sound estimate of PET that accounts for changing land surface conditions. Copyright © 2006 John Wiley & Sons, Ltd.