Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p <0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p <0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management

Exacerbated grassland degradation and desertification in Central Asia during 2000-2014.

Grassland degradation and desertification is a complex process, including both state conversion (e.g., grasslands to deserts) and gradual within-state change (e.g., greenness dynamics). Existing studies hardly separated the two components and analyzed it as a whole based on time series vegetation index data, which cannot provide a clear and comprehensive picture for grassland degradation and desertification. Here we propose an integrated assessment strategy, by considering both state conversion and within-state change of grasslands, to investigate grassland degradation and desertification process in Central Asia. First, annual maps of grasslands and sparsely vegetated land were generated to track the state conversions between them. The results showed increasing grasslands were converted to sparsely vegetated lands from 2000 to 2014, with the desertification region concentrating in the latitude range of 43-48° N. A frequency analysis of grassland vs. sparsely vegetated land classification in the last 15 yr allowed a recognition of persistent desert zone (PDZ), persistent grassland zone (PGZ), and transitional zone (TZ). The TZ was identified in southern Kazakhstan as one hotspot that was unstable and vulnerable to desertification. Furthermore, the trend analysis of Enhanced Vegetation Index during thermal growing season (EVITGS ) was investigated in individual zones using linear regression and Mann-Kendall approaches. An overall degradation across the area was found; moreover, the second desertification hotspot was identified in northern Kazakhstan with significant decreasing in EVITGS , which was located in PGZ. Finally, attribution analyses of grassland degradation and desertification were conducted by considering precipitation, temperature, and three different drought indices. We found persistent droughts were the main factor for grassland degradation and desertification in Central Asia. Considering both state conversion and gradual within-state change processes, this study provided reference information for identification of desertification hotspots to support further grassland degradation and desertification treatment, and the method could be useful to be extended to other regions

What four decades of earth observation tell us about land degradation in the Sahel?

The assessment of land degradation and the quantification of its effects on land productivity have been both a scientific and political challenge. After four decades of Earth Observation (EO) applications, little agreement has been gained on the magnitude and direction of land degradation in the Sahel. The large number of EO datasets and methods associated with the complex interactions among biophysical and social drivers of ecosystem changes make it difficult to apply aggregated EO indices for these non-linear processes. Hence, while many studies stress that the Sahel is greening, others indicate no trend or browning. The different generations of sensors, the granularity of studies, the study period, the applied indices and the assumptions and/or computational methods impact these trends. Consequently, many uncertainties exist in regression models between rainfall, biomass and various indices that limit the ability of EO science to adequately assess and develop a consistent message on the magnitude of land degradation. We suggest several improvements: (1) harmonize time-series data, (2) promote knowledge networks, (3) improve data-access, (4) fill data gaps, (5) agree on scales and assumptions, (6) set up a denser network of long-term field-surveys and (7) consider local perceptions and social dynamics. To allow multiple perspectives and avoid erroneous interpretations, we underline that EO results should not be interpreted without contextual knowledge

Drought impacts assessment in Brazil - a remote sensing approach

Climate extremes are becoming more frequent in Brazil; studies project an increase in drought occurrences in many regions of the country. In the south, drought events lead to crop yield losses affecting the value chain and, therefore, the local economy. In the northeast, extended periods of drought lead to potential land degradation, affecting the livelihood and hindering local development. In the southern Amazon, an area that experienced intense land use change (LUC) in the last, the impacts are even more complex, ranging from crop yield loss and forest resilience loss, affecting ecosystem health and putting a threat on the native population traditional way of living. In the studies here we analyzed the drought impacts in these regions during the 2000s, which vary in nature and outcomes. We addressed some of the key problems in each of the three regions: i) for the southern agriculture, we tackled the problem of predicting soybean yield based on within-season remote sensing (RS) data, ii) in the northeast we mapped areas presenting trends of land degradation in the wake of an extended drought and, iii) in southern Amazon, we characterized a complex degradation cycle encompassing LUC, fire occurrence, forest resilience loss, carbon balance, and the interconnectedness of these factors impacting the local climate. Advisor: Brian D. Wardlo

Use of remote sensing in landscape-scale vegetation degradation assessment in the semi- arid areas of the Save catchment, Zimbabwe.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.The deteriorating condition of land in parts of the world is negatively affecting livelihoods, especially, in rural communities of the developing world. Zimbabwe has experienced significant vegetation cover losses, particularly, in low and varied rainfall areas of the Save catchment. The concern that Save catchment is undergoing huge vegetation losses has been largely expressed, with the causes being environmental and anthropogenic. Given the magnitude of the problem, research studies have been undertaken to assess the extent of the problem in the south eastern region of Zimbabwe, which, nevertheless, have been mainly localized. The present study seeks to identify and quantify vegetation degradation at a landscape scale in the Save catchment of Zimbabwe, using remote sensing technologies. To achieve this, two objectives were set. The first objective provided a review of the application of satellite earth observations in assessing vegetation degradation, the causes, as well as associated impacts at different geographical scales. A review of literature has revealed the effectiveness of satellite information in identifying changes in vegetation condition. A second objective sought to establish the extent of vegetation degradation in the Save catchment. Moderate Resolution Imaging Spectroradiometer- Normalised Difference Vegetation Index (MODIS NDVI) datasets were used for mapping NDVI trends over the period 2000-2015. Further analysis involved application of residual trend (RESTREND) method to separate human influences from climatic signal on vegetation degradation. RESTREND results showed an increasing trend in NDVI values in about 33.6% of the Save catchment and a decreasing trend in about 18.3% from 2000 to 2015. The results of the study revealed that about 3,609,955 hectares experienced significant human induced vegetation degradation. Approximately 38.8% of the Save Catchment was significantly degraded (p< 0.05), 3.6%, 12.8%, and 22.4% of which were classified as severely, moderately, and lightly degraded, respectively. Severe degradation was mainly found in the central districts of the Save Catchment, mainly Bikita, Chipinge and northern Chiredzi. The results of this study support earlier reports about ongoing degradation in the catchment. Vegetation changes observed across the landscape revealed different degrees of the impacts of land use activities in altering the terrestrial ecosystems. The study demonstrated the usefulness of the RESTREND method in identifying vegetation loss due to human actions in very low rainfall areas

Combining Remote Sensing and Space-Time Analysis for Desertification Monitoring in the Semiarid Dryland of Nigeria

Desertification has been identified as the resultant effect of dryland loss. Desertification is catalysed by anthropogenic modifications and variations in environmental/climatic conditions. The situation in Nigeria is further exacerbated by the growing demand for land by the population. To this effect, this study performed a space-time analysis of vegetative cover between 2001 and 2020 to unravel patterns and trends across the semiarid region of the dryland system in Nigeria. The dynamics during the rainy season (May and September) were examined using the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) dataset subjected to space-time analysis. Generalised Difference Vegetation Index (GDVI) was computed to the power of 2 to quantify vegetative cover across the study area. The results showed that the average of the GDVI ranges between -0.40 and 0.94, with a standard deviation of 0.11. Time series cluster analysis revealed that there are two temporal clusters: (1) no statistically significant trend (Statistics= 1.33, p-value = 0.18) and (2) statistically significant downtrend (Statistics = -2.37, p=0.02), with cluster 1 covering 95% of the areas examined. The most dominant (97% of the area) emerging space-time pattern was cold-spots (persistent, diminishing, sporadic, oscillating, and historical types). In conclusion, most of the areas showed no definite temporal pattern of vegetation pattern during the period, while more than 90% of the areas have witnessed a decline in vegetative cover. There is a need for a more coordinated approach to desertification control, constant monitoring is pertinent while new approaches to restoring degraded land are recommended

THE BIG PICTURE - SATELLITE REMOTE SENSING APPLICATIONS IN RANGELAND ASSESSMENT AND CROP INSURANCE

Livestock Production/Industries, Risk and Uncertainty,

The rolle of methodology and spatiotemporal scale in understanding environmental chance in peri-urban Ouagadougou, Burkina Faso

In recent decades, investigations of NPP (net primary production) or proxies here of (normalized difference vegetation index, NDVI) and land degradation in Sahelian West Africa have yielded inconsistent and sometimes contradicting results. Large-scale, long-term investigations using remote sensing have shown greening and an increase in NPP in locations and periods where specific, small scale field studies have documented environmental degradation. Our purpose is to cast some light on the reasons for this phenomenon. This investigation focuses on the south of Ouagadougou, Burkina Faso, a city undergoing rapid growth and urban sprawl. We combine long-term MODIS (moderate resolution imaging spectroradiometer) image analysis of NDVI between 2002 and 2009, and by using high resolution satellite images for the same area and a field study, we compare trends of NDVI to trends of change in different categories of land cover for a selected number of MODIS pixels. Our results indicate a strong, positive association between changes in tree cover vegetation and trends of NDVI and moderate association between man-made constructions and trends of NDVI. The observed changes are discussed in relation to the unique processes of urban sprawl characterizing Ouagadougou and relative to their spatiotemporal scale